Expanding the Applications of Adaptive Optics Scanning Light Ophthalmoscopy

by

Drew Scoles

Submitted in Partial Fulfilment of the

Requirements for the Degree

Doctor of Philosophy

Supervised by

Professors Alfredo Dubra and David R. Williams

Department of Biomedical Engineering

Arts, Sciences and Engineering

Edmund A. Hajim School of Engineering and Applied Sciences

University of Rochester

Rochester, New York

2015

ii

Biographical Sketch

Drew Scoles was born in Philadelphia, Pennsylvania in 1986. He attended the

University of Rochester (Rochester, NY) from 2004 to 2008, and graduated with a

Bachelor of Science degree in Biomedical Engineering. Mr. Scoles continued at the

University of Rochester in fall 2008 as a trainee in the Medical Scientist Training

Program, and began graduate studies in Biomedical Engineering in fall 2010. He

pursued research in adaptive optics retinal imaging under the co-mentorship of

Professors Alfredo Dubra and David Williams. From fall 2012 to summer 2014, Mr.

Scoles conducted his research at the Medical College of Wisconsin in Milwaukee, WI.

The following publications were the result of work conducted during doctoral study:

Publications Drew Scoles, Brian P. Higgins, Ryan Johnson, Joseph Carroll, Alfredo Dubra, Kimberley E. Stepien. Investigation of Photoreceptor Structure in Best Vitelliform Macular Dystrophy with split-detector adaptive optics scanning light ophthalmoscopy. In Review. Abozaid, M. A., Scoles, D., Goldberg, M., Carroll, J. & Han, D. P. (2015). En Face Optical Coherence Tomography of Outer Retinal Discontinuity and Fan-Shaped Serous Macular Detachment in Diabetic Macular Edema. JAMA Ophthalmology, 2015. [Online First]. Hansen, S., Batson, S., Weinlander, K. M., Cooper, R. F., Scoles, D., Karth, P. A., Weinberg, D.V., Dubra, A., Kim, J.E., Carroll, J, Wirostko, W. J. Assessing photoreceptor structure after macular hole closure. Retin Cases Brief Rep, 2015. 9(1):15-20. Drew Scoles, John A. Flatter, Robert F. Cooper, Christopher S. Langlo, Scott Robison, David V. Weinberg, Mark E. Pennesi, Dennis P. Han, Alfredo Dubra, Joseph Carroll. Assessing photoreceptor structure associated with ellipsoid lesions visualized with en face OCT. Retina, 2015. [Epub ahead of print]. Drew Scoles, Brian P. Higgins, Robert F. Cooper, Adam M. Dubis, Phyllis Summerfelt, David V. Weinberg, Judy E. Kim, Kimberley E. Stepien, Joseph Carroll, Alfredo Dubra. Microscopic inner retinal hyper-reflective phenotypes in retinal and neurologic disease. Investigative Ophthalmology & Vision Science, 2014. 55(7):4015-4029.

iii

Drew Scoles, Yusufu N. Sulai, Christopher Langlo, Gerald A. Fishman, Christine A. Curcio, Joseph Carroll, Alfredo Dubra. In vivo imaging of human cone photoreceptor inner segments. Investigative Ophthalmology & Vision Science, 2014. 55(7):4244-4251. Sulai, Y.N., Scoles, D., Harvey, Z., Dubra, A. Visualization of retinal vascular structure and perfusion with a nonconfocal adaptive optics scanning light ophthalmoscope. Journal of the Optical Society of America A, 2014. 31(3):569-579. Pinhas, A., Dubow, M., Shah, N., Chui, T.Y., Scoles, D., Sulai, Y.N., Weitz, R., Walsh, J.B., Carroll, J., Dubra, A., Rosen, R.B. In vivo imaging of human retinal microvasculature using adaptive optics scanning light ophthalmoscope fluorescein angiography. Biomedical Optics Express, 2013. 4(8):1305-1317. Drew Scoles, Yusufu N. Sulai, Alfredo Dubra. In vivo dark-field imaging of the retinal pigment epithelium cell mosaic. Biomedical Optics Express, 2013. 4(9):1710-1723. Presentations Drew Scoles, Mara R. Goldberg, Christopher S. Langlo, Yusufu N. Sulai, Kimberly E. Stepien, David V. Weinberg, Judy E. Kim, Alfredo Dubra, Joseph Carroll, Barbara A. Blodi. Non-invasive evaluation of microscopic retinal pathology in macular telangiectasia type 2. (5951 - C0182). Association for Research in Vision and Ophthalmology Annual Meeting, Orlando FL, May 8 2014. Drew Scoles, Yusufu N. Sulai, Alfredo Dubra. In vivo imaging of the retinal pigment epithelium using dark-field AOSLO. ISIE/Imaging Conference, Seattle WA, May 4 2013. Drew Scoles, Robert F. Cooper, Adam M. Dubis, Brian P. Higgins, Joseph Carroll, Alfredo Dubra. In vivo microscopic inner retinal phenotypes of retinal and neurologic disease. (1434 - B0161). Association for Research in Vision and Ophthalmology Annual Meeting, Seattle WA, May 4 2013. Drew Scoles, Yusufu N. Sulai, Alex D. Manguikian, Shakeel Shareef, Alfredo Dubra. Reflectance adaptive optics nerve fiber layer imaging in primary open angle glaucoma. (6957 – 408). Association for Research in Vision and Ophthalmology Annual Meeting, Ft. Lauderdale, FL, May 9 2012.

iv

Acknowledgements

I thank my advisors, Professors Alfredo Dubra and David Williams for their

guidance and support throughout my thesis work. I am indebted to David Williams for

welcoming me to his research group in 2006 as an undergraduate, where I became

completely enthralled with adaptive optics vision research. I must acknowledge the

excellent mentorship of Dr. Dubra, as his unlimited enthusiasm for research, attention to

detail, creativity, accessibility, patience and never ending energy created an amazing

training environment. His support inside and out of the lab was incredible. It was truly an

honor to train under him. I would be remiss not to acknowledge the mentorship of

Professor William Merigan during my time as an undergraduate and through my

graduate years. I also appreciate the guidance of my committee members, Professors

Jennifer Hunter, Andrew Berger, Regine Choe and Geunyoung Yoon.

Throughout my tenure as a graduate student I have had the extreme pleasure of

working alongside Yusufu Sulai, an immensely intelligent, skilled and kind colleague and

friend. Without the brainstorming, encouragement, laughter and help he shared I could

not have completed the work presented in this thesis. I also thank Zach Harvey for his

work on the software and hardware control without which the work presented here would

not have been possible.

Throughout the development of my thesis I also had the opportunity to work

closely with very talented students whose accompaniment I enjoyed in and out of the lab

including Bernard Gee, Robin Sharma, Adam Dubis, Christopher Langlo, Robert

Cooper, Melissa Wilk and Benjamin Sajdak. I would like to acknowledge the guidance

and support from past and present members of the Advanced Retinal Imaging Alliance

including Mina Chung, Ethan Rossi, Jesse Schallek, Hongxin Song, Jennifer Norris, Lisa

v

Latchney and Dollie Aiken. I thank the researchers and staff of the Medical College of

Wisconsin Advanced Ocular Imaging Program including Kim Stepien, Judy Kim, David

Weinberg, Dennis Han, Tom Connor, Brian Higgins, Vesper Williams, Mara Goldberg,

Jonathan Young, Jonathan Skarie, Clinton Warren, Phyllis Summerfelt and Chris Axtell

for welcoming me and helping immensely during my work. I want to especially thank

Professor Joseph Carroll for sharing an immense amount of time, knowledge and

passion for research.

I thank the Medical Scientist Training Program at the University of Rochester for

giving me the chance to join such an incredible academic program and for funding

support via NIH MSTP training grant T32GM007356. I also thank Donna Porcelli and

Cathy Senecal-Rice for their immense support, countless emails and behind-the-scenes

work on my behalf.

I must especially thank my parents and my sister for encouraging me throughout

my life and especially throughout my education. Finally I must thank my wife Liz a

thousand times over for the incredible support, love, patience and understanding she

has offered me throughout my time as a graduate student. I would not be where I am

without her.

vi

Abstract

In addition to visual impairment, vision loss often leads to significant emotional and

psychological hardship, triggered by feelings of isolation and dependence on others for

activities of daily life. Common blinding conditions such as glaucoma and macular

degeneration lead to retinal neuronal death, thus preventing detection of visual information

from the outside world and its transmission to the brain. Early detection of retinal pathology is

essential for effective treatment, since vision often does not recover substantially after insult

or injury and cell loss is permanent. Accordingly, there will always be a strong emphasis on

developing non-invasive imaging techniques for the early diagnosis and accurate monitoring

of retinal disease. The advent of adaptive optics retinal imaging provided the ability to

visualize individual photoreceptor cells in the living human retina, and has been applied to

the study of many retinal diseases. The work presented here focuses on expanding the

capabilities and scope of adaptive optics scanning light ophthalmoscopy (AOSLO).

The thesis begins with an extensive survey of previously understudied inner retinal

layers in normal and pathological conditions using AOSLO confocal imaging, which resulted

in a number of novel microscopic findings that span across multiple apparently unrelated

conditions. Following this, two non-confocal imaging modalities, namely dark-field and non-

confocal split-detection were demonstrated, allowing for visualization of the retinal pigment

epithelium and photoreceptor inner segments, respectively. These novel techniques are then

applied to the study of inherited, traumatic and idiopathic retinal disease in human patients.

Correlations between clinical imaging techniques including optical coherence tomography

and fundus photography with confocal AOSLO and the non-confocal AOSLO modalities are

assessed. Finally, these non-confocal techniques are shown to provide novel, unique and

critical information about cellular structure in the context of retinal disease and for

preparation of retinal gene therapies.

vii

Contributors and Funding Sources

This work was supervised by a dissertation committee consisting of Dr. David

Williams (co-advisor), Dr. Andrew Berger, Dr. Regine Choe and Dr. Guenyoung Yoon of

the Department of Biomedical Engineering, Dr. Jennifer Hunter of the Department of

Ophthalmology and Dr. Alfredo Dubra (co-advisor) of the Department of Ophthalmology

at the Medical College of Wisconsin.

The AOSLO systems used for experiments throughout the thesis were designed

by Yusufu Sulai and Alfredo Dubra. The author assisted in construction and

maintenance of the systems. The image acquisition and registration software was

designed by Zachary Harvey and Alfredo Dubra. The adaptive optics control software

was developed by Kamran Ahmed and Alfredo Dubra. The OCT segmentation software

was written by Robert F. Cooper.

In Chapter 3 Brian P. Higgins, Robert F. Cooper, Adam M. Dubis and Phyllis

Summerfelt assisted with data acquisition and processing. David V. Weinberg, Judy E.

Kim and Kimberly E. Stepien assisted with subject recruitment. Joseph Carroll and

Alfredo Dubra assisted with interpretation. These results were published in 2014 in an

article listed in the Biographical Sketch.

In Chapter 4 Yusufu N. Sulai and Alfredo Dubra assisted with image acquisition,

construction of optical components and interpretation. These results were published in

2013 in an article listed in the Biographical Sketch.

In Chapter 5 Yusufu N. Sulai assisted with construction of optical components.

Yusufu N. Sulai and Christopher S. Langlo assisted with data acquisition, processing

and analysis. Histological data collection and analysis were performed entirely by

Christine A. Curcio. Gerald A. Fishman and Joseph Carroll assisted with subject

viii

recruitment. Joseph Carroll and Alfredo Dubra assisted with interpretation. These results

were published in 2014 in an article listed in the Biographical Sketch.

In Chapter 6 Christopher S. Langlo and John Flatter assisted with data

acquisition, processing and analysis. Kimberly A. Stepien, David V. Weinberg, Judy E.

Kim, Kimberly E. Stepien, Mark E. Pennesi, Scott Robison and Dennis P. Han assisted

with subject recruitment. Joseph Carroll and Alfredo Dubra assisted with interpretation.

These results were published in 2015 in an article listed in the Biographical Sketch.

In Chapter 7 Yusufu N. Sulai, Brian P. Higgins, Christopher S. Langlo and Ryan

Johnson assisted with data acquisition, processing and analysis. Kimberly A. Stepien,

Joseph Carroll and Alfredo Dubra assisted with study design and interpretation. These

results were recently submitted for publication in an article listed in the Biographical

Sketch.

Funding support for this work was provided by: NIH Core Grants for MSTP

training (T32GM007356), for Vision Research to the University of Rochester (P30

EY001319) and the Medical College of Wisconsin (P30 EY001931), Research to

Prevent Blindness departmental awards to the Flaum Eye Institute and the MCW Eye

Institute, the Burroughs Wellcome Fund, the RD & Linda Peters Foundation and the

Glaucoma Research Foundation Catalyst for a Cure Initiative.

Unless mentioned above or in the thesis, the research presented here was all performed

by the author.

ix

Table of Contents

Biographical Sketch ............................................................................................................. ii

Acknowledgements ............................................................................................................ iv

Abstract .................................................................................................................................. vi

Contributors and Funding Sources ............................................................................... vii

List of Tables ....................................................................................................................... xii

List of Figures .................................................................................................................... xiii

Chapter 1 Introduction .................................................................................................. 1

1.1 Human Retina .............................................................................................................. 5

1.2 Retinal Disease .......................................................................................................... 11

1.3 Ocular Aberrations .................................................................................................... 12

1.3.1 Monochromatic Aberration .............................................................................. 13

1.3.2 Chromatic Aberration ....................................................................................... 14

1.4 Ophthalmic Adaptive Optics .................................................................................... 15

Chapter 2 Adaptive Optics Scanning Light Ophthalmoscopy ......................... 18

2.1 The Role of the Pinhole ............................................................................................ 18

2.2 AOSLO Components ................................................................................................ 21

2.3 Optical Setup .............................................................................................................. 27

2.4 Chromatic Aberration Compensation ..................................................................... 29

2.5 Image Registration and Averaging ......................................................................... 31

2.6 Eye Tracking .............................................................................................................. 33

2.7 Confocal Imaging ....................................................................................................... 35

2.8 Single-photon Fluorescence Imaging ..................................................................... 42

2.9 Non-confocal AOSLO ............................................................................................... 46

x

Chapter 3 Application of Adaptive Optics to the Study of the Inner Retina . 49

3.1 Introduction ................................................................................................................. 49

3.2 Methods ...................................................................................................................... 50

3.3 Classification of Inner Retinal Phenotypes ............................................................ 52

3.3.1 Punctate Reflectivity ......................................................................................... 56

3.3.2 Nummular Reflectivity ...................................................................................... 59

3.3.3 Granular Membrane ......................................................................................... 62

3.3.4 Waxy Membrane ............................................................................................... 64

3.3.5 Vessel Associated Membrane ........................................................................ 68

3.3.6 Microcysts .......................................................................................................... 69

3.3.7 Striate Reflectivity ............................................................................................. 72

3.4 Discussion .................................................................................................................. 73

Chapter 4 Dark-field Adaptive Optics Ophthalmoscopy ................................... 76

4.1 Introduction ................................................................................................................. 76

4.2 Methods ...................................................................................................................... 77

4.3 Results ........................................................................................................................ 81

4.4 Discussion .................................................................................................................. 93

Chapter 5 Non-confocal Split-detection Adaptive Optics Ophthalmoscopy 97

5.1 Introduction ................................................................................................................. 97

5.2 Methods .................................................................................................................... 100

5.3 Results ...................................................................................................................... 107

5.4 Discussion ................................................................................................................ 114

Chapter 6 Visualization of Photoreceptor Structure within Ellipsoid Zone

Lesions Imaged with Optical Coherence Tomography .......................................... 119

6.1 Introduction ............................................................................................................... 119

xi

6.2 Methods .................................................................................................................... 120

6.3 Results ...................................................................................................................... 124

6.4 Discussion ................................................................................................................ 134

Chapter 7 Photoreceptor Inner Segment Morphology in Best Vitelliform

Macular Dystrophy ........................................................................................................... 140

7.1 Introduction ............................................................................................................... 140

7.2 Methods .................................................................................................................... 142

7.3 Results ...................................................................................................................... 145

7.4 Discussion ................................................................................................................ 153

Chapter 8 Conclusions and Future Work ............................................................ 157

8.1 Survey of Hyper-reflective Inner Retina Structures in Confocal Imaging ....... 157

8.2 Demonstration and Application of Dark-field ....................................................... 159

8.3 Demonstration and Application of Non-Confocal Split-detection ..................... 160

8.4 Conclusions .............................................................................................................. 163

References ......................................................................................................................... 164

xii

List of Tables

Table 2.1 Confocal AOSLO studies of retinal disease ................................................... 38

Table 3.1 Diseases and inner retinal phenotypes observed........................................... 54

Table 5.1 Calculation of visual sampling based on residual cone photoreceptor spacing.

............................................................................................................................ 114

Table 5.2 Genotypic and demographic data of subjects with achromatopsia. .............. 117

Table 5.3 Ex vivo measurements of inner segment diameter between 0 and 12mm. .. 118

Table 5.4 Rod photoreceptor size estimate using nearest neighbor analysis in

achromatopsia vs. normal. ................................................................................... 118

Table 6.1 Clinical characteristics of patients included in this study. ............................. 121

Table 7.1 Best disease study subject demographics ................................................... 142

xiii

List of Figures

Figure 1.1 Cross sectional schematic of the human eye. ................................................. 2

Figure 1.2 Schematics of direct and indirect ophthalmoscopy. ........................................ 3

Figure 1.3 The retina, as viewed with ophthalmoscopy or fundus photography and cross

sectional histology. .................................................................................................. 8

Figure 1.4 Schematic representation of photoreceptor organization. ............................... 9

Figure 1.5 Photoreceptor density and morphology as a function of eccentricity. ............ 10

Figure 1.6 The magnitude of longitudinal chromatic aberration (LCA) in an average

human eye, shown in context of the retinal thickness. ........................................... 15

Figure 1.7 Closed loop ophthalmic adaptive optics imaging schematic. ........................ 17

Figure 2.1 The role of the detection pinhole in a confocal instrument. ........................... 20

Figure 2.2 Simplified schematic of an AOSLO with confocal detection illustrating the

raster scanning of a point source onto the retina to create an image. .................... 23

Figure 2.3 Schematic of AOSLO used for this work, flattened for display. ..................... 27

Figure 2.4 Example of AOSLO beam steering for increased acquisition speed. ............ 29

Figure 2.5 Illustration of transverse chromatic aberration in AOSLO imaging. ............... 31

Figure 2.6 Desinusoiding to remove distortion from the sinusoidal motion of the resonant

scanner. ................................................................................................................ 32

Figure 2.7 RPE autofluorescence imaging with AOSLO. ............................................... 33

Figure 2.8 Representative single image frames from a patient with significant eye

motion. .................................................................................................................. 35

Figure 2.9 Cone photoreceptor imaging and density measurement with AOSLO. ......... 36

Figure 2.10 Rod photoreceptor imaging with AOSLO at various eccentricities. ............. 37

Figure 2.11 Examples of “dark cones” (arrows) found in photoreceptor disease and

retinal disruption. ................................................................................................... 40

Figure 2.12 Capillary mapping with confocal AOSLO. ................................................... 41

Figure 2.13 Nerve fiber layer and lamina cribrosa imaging with AOSLO. ....................... 42

Figure 2.14 Adaptive optics fluorescein angiography. .................................................... 44

Figure 2.15 Retinal ganglion cells visualized in living mice with fluorescence AOSLO. .. 45

Figure 2.16 AOSLO offset pinhole imaging in an area of thick nerve fiber layer. ........... 47

Figure 2.17 AOSLO fluorescein imaging versus offset pinhole imaging of the avascular

zone. ..................................................................................................................... 48

xiv

Figure 3.1 Representative images of the first four features described in this study. ....... 55

Figure 3.2 Representative images of the final three features described in this study. .... 56

Figure 3.3 Multimodal imaging of punctate reflectivity example A1, rubella retinopathy

JC_0830. ............................................................................................................... 58

Figure 3.4 Follow up of punctate hyper-reflectivity, an enlarged portion of the lesion

shown in Figure 3.3. .............................................................................................. 59

Figure 3.5 Multimodal imaging of nummular reflectivity example B1, normal subject

JC_0007. ............................................................................................................... 61

Figure 3.6 Follow-up imaging of nummular reflectivity in B1, normal subject JC_0007. . 62

Figure 3.7 Multimodal imaging of granular membrane example C1, diabetic retinopathy

RS_1007. .............................................................................................................. 63

Figure 3.8 Illustration of how the specular reflectivity of waxy membranes can lead to

dramatic image intensity changes relative to the surrounding structures with eye

motion. .................................................................................................................. 65

Figure 3.9 Multimodal imaging of waxy membrane example D1, cone dystrophy

KS_1154. .............................................................................................................. 66

Figure 3.10 Example of waxy membrane with notable contraction in normal subject

JC_10146 OD. ....................................................................................................... 67

Figure 3.11 Two month follow up of a waxy membrane in a glaucoma patient,

DLAB_0029. .......................................................................................................... 68

Figure 3.12 Multimodal imaging of vessel associated membrane example E1, Leber’s

congenital amaurosis JC_0579. ............................................................................. 69

Figure 3.13 Multimodal imaging of microcysts example F1, macular telangiectasia

subject JC_10075. ................................................................................................. 71

Figure 3.14 Thirteen month follow up of the microcysts found in subject KS_1100 with

dominant optic atrophy. ......................................................................................... 72

Figure 3.15 Multimodal imaging of striate reflectivity example G1, Best’s disease

KS_0601. .............................................................................................................. 73

Figure 4.1 AOSLO image plane apertures in front of the detector. ................................ 79

Figure 4.2 AOSLO confocal (left) and dark-field (right) retinal images in four different

subjects, all collected at the foveal center. ............................................................. 83

xv

Figure 4.3 AOSLO confocal (left) and dark-field (right) retinal images in four different

subjects, all collected at 10° temporal to fixation. ................................................... 84

Figure 4.4 Comparison of SD-OCT data and an AOSLO dark-field image in a normal

subject. .................................................................................................................. 85

Figure 4.5 Reflectivity of photoreceptors in confocal images and locations of

photoreceptors in dark-field images. ...................................................................... 86

Figure 4.6 Confocal (photoreceptor) and dark-field (RPE) images collected

simultaneously. ...................................................................................................... 87

Figure. 4.7 Dark-field AOSLO images of the RPE mosaic at fixation collected using a 1

ADD thick filament and different pinhole diameters. ............................................... 89

Figure. 4.8 Dark-field AOSLO images of the RPE mosaic at fixation collected using a 16

ADD diameter pinhole and either 1 (A) or 3 ADD thick filament (B). ...................... 90

Figure 4.9 Time-averaged retinal point-spread function (PSF). ..................................... 90

Figure 4.10 Dark-field AOSLO images of the RPE mosaic at the center of fixation with

different imaging wavelengths. .............................................................................. 91

Figure 4.11 Effect of pupil apodization on image quality and contrast at 10° temporal to

fixation. .................................................................................................................. 91

Figure 4.12 Comparison of RPE autofluorescence to dark-field RPE imaging. .............. 92

Figure 4.13 RPE images collected in a patient DW_1188 with central serous retinopathy.

.............................................................................................................................. 93

Figure 4.14 Montage of foveal photoreceptors and RPE cells from normal subject

JC_0616. ............................................................................................................... 96

Figure 5.1 Schematic representations of split-detector implementation and images. ... 102

Figure 5.2 Side by side comparison of ex vivo [33] and in vivo imaging of the human

photoreceptor inner segment mosaic at 5° temporal from fixation in different eyes.

............................................................................................................................ 108

Figure 5.3 Confocal and split-detector imaging in a normal volunteer at 1, 5, 10, 20°

temporal to fixation. ............................................................................................. 109

Figure 5.4 Plot of average cone inner segment diameter from the foveal center along

temporal meridian. ............................................................................................... 110

Figure 5.5 Spectral domain optical coherence tomography (SD-OCT) appearance of the

subjects included in this study. ............................................................................ 111

xvi

Figure 5.6 Confocal and split-detector AOSLO images of the photoreceptor mosaic in a

patient with achromatopsia at 0.4 and 2° from fixation. ........................................ 112

Figure 5.7 Assessing the foveal photoreceptor mosaic in achromatopsia. ................... 113

Figure 6.1 Schematic of the generation of en face summed value projection OCT

images. ................................................................................................................ 123

Figure 6.2 Multimodal imaging in cone-rod dystrophy subject DH_1192...................... 125

Figure 6.3 Multimodal imaging in MacTel subject DW_10105. .................................... 127

Figure 6.4. Multimodal imaging in cg-BOT subject SR_10139. .................................... 129

Figure 6.5 Comparison of disruption size in confocal AOSLO, IZ and EZ en face OCT

segmentation. ...................................................................................................... 131

Figure 6.6 Multimodal imaging in cg-BOT subject WW_0923. ..................................... 132

Figure 6.7 Multimodal imaging in BCM subject MP_10097. ......................................... 135

Figure 6.8 AOSLO montages in linear and logarithmic contrast from the 5 subjects

reported in the study. ........................................................................................... 137

Figure 7.1 Imaging results from subject KS_0601. ...................................................... 147

Figure 7.2 Imaging results from subject KS_0325. ...................................................... 148

Figure 7.3 Parafoveal photoreceptor imaging in remaining subjects. ........................... 149

Figure 7.4 Longitudinal OCT follow-up of Best disease lesions for all patients. ........... 150

Figure 7.5 Cone photoreceptor density inside and outside of lesions. ......................... 151

Figure 7.6 Co-registered AOSLO and OCT imaging from subject KS_0325, with normal

AOSLO imaging for comparison spanning from 0.8 to 2.4 mm retinal eccentricity.

............................................................................................................................ 152

Figure 7.7 Short-term and long-term variability in photoreceptor layer imaging with split-

detector AOSLO in Best disease. ........................................................................ 155

Figure 8.1 Photoreceptor / RPE disambiguation with dark-field AOSLO. .................... 160

1

Chapter 1 Introduction

The eye provides a unique window to non-invasively study the retina, a crucial

component of the visual pathway (Figure 1.1). Physicians and researchers have

capitalized on the opportunity to observe the living human retina since the 19th century

[1], in order to diagnose and study eye disease respectively. The simplest instrument for

the task is a direct ophthalmoscope, which consists of a light source, a mirror to direct

light into the subject’s eye with a hole for observation, and a wheel of lenses to mitigate

the refractive error difference between the examiner and the subject (Figure 1.2). In this

configuration, an image of the subject’s retina is formed on the examiner’s retina, with a

small field of view (<10°) and ~15x magnification. The indirect ophthalmoscope allows a

wider field of view through the use of a condensing lens which is held close to the

subject’s eye and ~3x magnification. Most modern indirect ophthalmoscopes are

binocular, for better visualization of three-dimensional structures.

With the advent of photography, indirect ophthalmoscopy became fundus

photography [2] by replacing the observer’s eye with film and subsequently digital

cameras (Figure 1.3). In the 1980s, Webb et al. developed the scanning laser

ophthalmoscope (SLO) by replacing the full-field illumination found in fundus cameras

with point illumination from a laser [3, 4]. The retinal image was created by scanning the

spot across the retina and recording the signal over a square grid of points in a photon

detector. The SLO was further refined with the incorporation of a pinhole, placed in a

retinal conjugate plane before the detector, which rejects reflections from out of focus

retinal layers. The SLO was designed to illuminate and record the reflected, scattered [5]

or fluorescence light through the same 2-3 mm of the subject’s pupil, resulting in

increased contrast and less light exposure required compared to fundus photography.

2

Fundus photography and SLO are analogous to bright-field and confocal microscopy

respectively, with the optics of the eye replacing the microscope objective and the retina

being the sample. Thus any epi-microscopy technique, that is, any imaging modality in

which the illumination and imaging take place through the same objective lens, could

theoretically be implemented into an ophthalmoscope, within the restrictions of the

numerical aperture of the eye and light safety.

Figure 1.1 Cross sectional schematic of the human eye. Light enters and is sequentially refracted by the cornea, anterior chamber, lens and vitreous chamber to form an image on the surface of the retina. The fovea is the specialized area of the retina designed for high acuity vision.

Recent technological advances in computing power and light sources have

dramatically increased the impact of retinal imaging on early diagnosis and monitoring of

retinal and neurologic disease. Low coherence interferometry or optical coherence

tomography (OCT), first described in 1991, [6] provides near micrometer (currently ~3-7

µm in clinical instruments) axial resolution, allowing exquisite visualization of individual

3

retinal layers and pathology. This technology has improved rapidly, and it is currently the

first choice for clinical ophthalmic photography [7].

Figure 1.2 Schematics of direct and indirect ophthalmoscopy. Modified from figure courtesy of Yusufu Sulai, PhD.

Despite the axial resolution improvement offered by OCT, clinical retinal imaging

instruments remain limited to macroscopic assessment of retinal structures due to the

optical blur that limits transverse image resolution to 15-20 µm. This often means that

significant changes in the thicknesses of the photoreceptor nuclear layer in age related

macular degeneration or the ganglion cell layer in glaucoma likely correlate to the loss of

hundreds or thousands of cells. Retinal neurons are post-mitotic, meaning they cannot

regenerate to replace cells lost to disease or injury. For this reason, early diagnosis is

essential and evaluation of the retina on a cellular scale could detect the first signs of

4

retinal pathology. Therefore, the development of microscopic imaging methods holds the

potential for earlier diagnosis, improved study of the natural history of disease and more

sensitive monitoring of treatment outcomes.

The relatively poor optical quality of the human eye, limits the transverse and

axial resolution of current commercial ophthalmic instruments. Most ophthalmoscopes

image through a 2-3 mm pupil, as this is the range over which the ocular optics can be

considered diffraction limited. Since the impact of aberrations increases with pupil size, it

is not possible to take advantage of the theoretical resolution improvement provided by a

pharmacologically-dilated 5-8 mm pupil [8-10]. The incorporation of adaptive optics (AO)

technology for compensation of the ocular monochromatic aberrations led to a new

generation of ophthalmoscopes enhancing the fundus camera (AO flood), the optical

coherence tomograph (AO-OCT) and the scanning light ophthalmoscopes (AOSLO).

Among these, the AOSLO provides excellent lateral resolution (~2-5 µm) and modest

optical sectioning (~ 1 order of magnitude worse than AO-OCT). Optical sectioning is

accomplished through the use of a confocal pinhole in the light detection path. This

pinhole rejects light from out of focus retinal layers, providing high contrast images [11],

effectively making this instrument a confocal microscope with higher numerical aperture

than the SLO. Since its first demonstration [11], the excellent transverse resolution of

AOSLO has been applied largely to study the photoreceptor mosaic in healthy and

diseased eyes. Retinal capillaries near the fovea have also been imaged since AOSLO

provides the opportunity to visualize their flow without the introduction of any dye [12,

13]. Very recently, a non-confocal AOSLO imaging method demonstrated the ability to

visualize retinal vasculature with increased intrinsic contrast to the point that individual

red and white blood cells can be easily seen and mural cells can occasionally be

5

identified [14]. Single-photon fluorescence AOSLO has also been used to visualize the

intrinsic fluorescence of the retinal pigment epithelium (RPE) mosaic [15, 16], labeled

retinal cells [16-18] and perform microscopic fluorescein angiography [19-22]. By

comparison, there have been very few applications of AOSLO imaging to the nerve fiber

layer [23-27] and optic nerve head [28-31], structures relevant to the study of glaucoma,

the most common non-preventable blinding disease in the world.

The work described in this thesis intends to try to improve our understanding of

the in vivo pathologic changes of retinal and neurologic disease. This thesis describes

novel imaging modalities and clinically relevant applications of human AOSLO imaging.

First, the confocal AOSLO is applied to the study of the inner retina in health and a

broad number of retinal and neurological diseases. Then dark-field, a non-confocal

imaging technique, which allows the first non-invasive visualization of the retinal pigment

epithelium (RPE) mosaic without potentially toxic visible light is demonstrated. Finally

split-detection, the second novel non-confocal imaging technique, which reveals the

cone photoreceptor inner segment mosaic, is demonstrated. Lastly, these non-confocal

imaging techniques are used to study patients with various retinal disorders and

diseases.

1.1 Human Retina

The tear film, cornea and crystalline lens of the human eye focus incoming light to

create an image of the outside world onto the retina. The process of vision begins with

photon absorption at the photoreceptor outer segments and translation of the optical

signal into neural signal via neurotransmitter release. The visual information is then

transmitted from the photoreceptors through the bipolar cells to the ganglion cells, which

ultimately carry the visual information to the brain (see layers of the retina in Figure 1.3).

6

There are two classes of photoreceptors: rods and cones. Among the

photoreceptors, rods are responsible for low-light and low-acuity vision, with a broad

sensitivity curve that peaks at approximately 500 nm [32]. Due to their high sensitivity,

rods are saturated under most lighting conditions such that their signal does not

contribute to vision. Despite the narrower diameter of rod photoreceptors compared to

most cones, they do not transmit high acuity visual information, since their output is

locally pooled across multiple cells into relatively few bipolar cells. Cone photoreceptors

on the other hand, are responsible for high-acuity and color vision, in bright viewing

conditions. There are three types of cones present in the human and non-human primate

retina with peak sensitivities to long (560nm), medium (530nm) and short (420nm)

wavelengths [32]. Light enters their outer segments after passing through the inner-

segments, which are vertically aligned and form a monolayer at the external limiting

membrane (see Figure 1.4). In healthy retinas the inner segments are tightly packed with

no gaps, except at the optic nerve, where there is a blind spot. Each inner segment has

a posterior outer-segment, also forming a monolayer. The cell bodies of the

photoreceptors are packed randomly in the outer nuclear layer, sometimes with great

separation from their distal processes, and not vertically organized (see Figure 1.4).

To support high-acuity vision, cone photoreceptors are packed in an area of the

retina known as the fovea, at densities as high as 324,000 cones/mm2 [33]. At this

density, the image of a letter on the 20/20 line of an eye chart will span over

approximately 14 cones in height. Cone photoreceptor density rapidly decreases with

increasing distance from the fovea, while rod photoreceptors, absent in the central

fovea, begin to mix into the mosaic (see Figure 1.4 & Figure 1.5), peaking in density at

around 15 degrees of visual angle eccentricity (a measure of distance from the center of

7

the fovea expressed in degrees). Within the fovea, cone inner segment diameters are as

small as 2.2 µm, while in the periphery they reach a maximum diameter of approximately

8.5 µm [34]. Rod photoreceptors on the other hand, maintain a more constant 2.0 µm

inner segment diameter across the retina [33] (see Figure 1.5).

The photoreceptor axons synapse on the immediately anterior layer of the bipolar

cell dendrites known as the outer plexiform layer. The bipolar cells, which reside in the

inner nuclear layer, are responsible for conveying signals from one or more

photoreceptors to the retinal ganglion cells (RGC). Bipolar cells also initiate local

processing of visual information by transmitting the photoreceptor signal in an excitatory

(sign-conserved) or inhibitory (sign-reversed) fashion. At the level of the synapses of the

photoreceptors and bipolar cells, horizontal cells also participate in local processing and

modulation of photoreceptor signal through their connections to photoreceptor-bipolar

synapses as well as direct synapse to bipolar cells.

Depending on the classification scheme used, there are up to 18 different

subtypes of RGC [35-37] that participate in local processing of visual information and,

most importantly, transmit information. The long axons of the RGC, which form the nerve

fiber layer, exit the eye to form the optic nerve and to communicate with several visual

centers in the brain. Within the inner plexiform layer, and analogously to the horizontal

cells of the outer retina, the amacrine cells participate in the processing that occurs at

synapses of the bipolar cells with the RGC.

8

Figure 1.3 The retina, as viewed with ophthalmoscopy or fundus photography and cross sectional histology. A. Direct or indirect ophthalmoscopy views the retina en face, with major landmarks such as the optic disc, large vessels and fovea visible. B. When sliced in cross-section the layered organization of the retina and choroid is clearly visualized. Light impinges on the retina from the top of this image, and traverses through the nearly transparent layers of tissue to reach the rod and cone photoreceptors. From there the signal is processed and transmitted through the bipolar cells to the ganglion cells. The axons of the ganglion cells exit the eye at the optic disk and travel together to the brain as the optic nerve. B. is reproduced from Ross et al. 2011 [38] with permission.

9

Figure 1.4 Schematic representation of photoreceptor organization. Photoreceptor inner segments (IS) are tightly packed and in close proximity to the external limiting membrane (ELM), while their cell bodies in the outer nuclear layer (ONL) are less organized. When viewed en face, both cone-dominated (central) and rod-dominated (peripheral) regions, demonstrate strict monolayers of IS and OS. Blue photoreceptors represent cones, and green represent rods. The processes of the retinal pigment epithelium (RPE) cells (shown in orange) extend upwards and interdigitate with the outer segments (OS).

10

Posterior to the retina is the retinal pigment epithelium (RPE), a monolayer of

cells that provides the photoreceptors with nutrition, structural support and assists in the

recycling of visual pigment. This layer is discussed in depth in Chapter 3. Posterior to

the RPE lies Bruch’s membrane, a principal component of the blood-retina barrier.

Finally the choroid, a large vascular network, provides the oxygen supply and solutes

(e.g., glucose) to the majority of the outer retina including the RPE and photoreceptor

cells [39].

Figure 1.5 Photoreceptor density and morphology as a function of eccentricity. Top row: Histologic images of the photoreceptor inner segment mosaic at various retinal eccentricities. Within each image representative cone and rod photoreceptors are marked by “*” and arrows respectively. Plot: The cone photoreceptor density reaches a peak at the fovea, the area responsible for fixation and high acuity vision. Reproduced from Carroll et al. 2009 [40] with permission

In addition to the neural cells of the retina, there are several classes of non-

neuron support cells known as glial cells analogous to those found in the brain. This

class of cells includes the Müller cells, which extend vertically across the entire

11

neurosensory retina, and coalesce to create the internal and external limiting membrane.

Other glial cells include the astrocytes which assist in neuronal support and blood-retinal

barrier integrity, and the microglia which are thought to function as the immune cells of

the retina.

1.2 Retinal Disease

Vision loss can result from deficiencies in the creation of a retinal image by the

anterior structures (e.g., corneal opacification, cataract, etc.), in the detection and

transmission of the visual information by the retina to the brain or in transfer and

processing within the brain itself. The work here focuses on retinal causes of blinding

disease, of which photoreceptor, RPE and ganglion cell damage are by far the most

common causes. Of utmost importance in discussion of retinal disease, is the

understanding that no retinal neurons can regenerate, and every loss is permanent.

Photoreceptor and RPE disease are somewhat intertwined, in that degenerations of the

RPE almost always lead to loss of the previously supported photoreceptors. After

cataracts Age related macular degeneration (AMD) is the most common cause of

blindness in the United States, with prevalence of over 1.75 million [41]. AMD leads to a

destruction of the RPE with concurrent or resultant loss of the undernourished

photoreceptors [42, 43]. When significant photoreceptors are lost, visual acuity suffers.

There are many theorized contributors to the development of AMD including genetic

causes [44] and toxic exposures [45, 46], but significant work remains to understand a

disease that will afflict a significant portion of the rapidly increasing aging population [41].

Photoreceptor imaging technology promises a method to evaluate the microscopic

pathology in response to new treatment modalities, including potential cell therapies [47].

There are many other photoreceptor and RPE related causes of visual impairment

12

including inherited dysfunctions, inherited degenerations and trauma, several of which

are discussed further in Chapters 4, 5 and 6.

Glaucoma is a substantial cause of non-preventable blindness in the United

States, with an estimated 2.7 million adults over 40 affected by the disease [48]. The

most common form of glaucoma, open angle, is associated with an increased intraocular

pressure that may exert damaging stress on the optic disc [49-51]. Vision loss results

from the progressive loss of ganglion cells and their corresponding axons within the optic

nerve [52-54], starting in the periphery and closing in to include the fovea in late disease

stages. The inciting pathogenesis is not fully understood in glaucoma, but it is agreed

that early treatment promises the best chance for vision preservation [55]. The earliest

changes within the ganglion cell layer, nerve fiber layer or optic disc are not known due

to the lack of sufficient animal models of the disease [56], thus motivating microscopic

study of glaucoma in living human eyes. In addition to glaucoma, neurodegenerative

diseases including multiple sclerosis [57] and Parkinson’s disease [58], also lead to loss

of RGC and potentially bipolar cells within the inner retina [59].

1.3 Ocular Aberrations

While the optics of the eye provide a window to the retina, their imperfections

restrict the maximum theoretical resolution attainable by ophthalmoscopes. In order to

resolve the smallest photoreceptor cells described above, one must be able to achieve a

lateral resolution of approximately 2 µm. If the human eye was diffraction limited over a 7

mm pupil diameter, this resolution criterion would be easily met, even when imaging with

wavelengths as long as 800 nm (see resolution discussion in Chapter 2). The problem,

however, is the spectacle-corrected eye can only be considered diffraction limited over

pupil sizes of up to approximately 3mm, due to the presence of monochromatic

13

aberrations [8-10]. Therefore, monochromatic aberrations other than simple defocus and

astigmatism must be compensated for in order to take full advantage of the dilated pupil.

1.3.1 Monochromatic Aberration

Retinal imaging with a point-scanning instrument such, as the AOSLO or the

AOOCT, requires that incoming illumination is sharply focused onto the retina and that

the reflected light is captured onto a detector. The illumination and reflected beams can

be described geometrically as collections of rays, from which a wavefront can be

derived. Ignoring diffraction, as a wave comes to a perfect focus, it forms a spherical

shell centered on and shrinking toward the focus. The diversions of any real wavefront

from that ideal sphere are known as geometrical wavefront aberrations, and can be

described mathematically in terms of polynomials. Ocular aberrations are most efficiently

described in Zernike polynomials, according to experimental data [60].

Within the eye each of the ocular surfaces contributes to these aberrations, along

with factors such as tear film quality, pupil diameter, age and accommodation among

others. Ocular aberrations have been measured in human subjects with a variety of

techniques, with the Shack-Hartmann wavefront sensor (used in this work) being the

most widely adopted. Effectively, a Shack-Hartmann wavefront sensor is a camera

designed to sub-sample the aberration of a beam of light by measuring the local slope of

the wavefront, often using an array of small lenslets [8, 61, 62]. Each of these lenslets

form a spot on the camera and the displacement of these spots is considered

proportional to the wavefront slope at that lenslet. Unsurprisingly the results of

population wavefront sensing studies reveal that defocus and astigmatic errors

dominate, but the substantial magnitude and inter-individual differences of higher order

14

monochromatic aberrations impact the performance of retinal imaging devices when

used over large pupils [60, 63, 64].

1.3.2 Chromatic Aberration

All ocular imaging devices that illuminate the retina with multiple wavelengths

suffer from longitudinal chromatic aberration (LCA), which is defined as the differential

focus shift of light as a function of wavelength. The LCA of the human eye is substantial

when compared to the ocular thickness (Figure 1.6), and is due to the addition of the

LCA contributions from the ocular refractive surfaces (corneal and crystalline lens

surfaces), showing only small variations across the population [65]. Often AO

ophthalmoscopes use different wavelengths for imaging and wavefront sensing, based

on the assumption that other than for defocus (due to LCA), monochromatic aberration

changes with wavelengths are considered negligible [66, 67].

In addition to LCA, transverse chromatic aberration (TCA) presents another

challenge when imaging the retina with multiple wavelengths of light. The two

components of TCA include chromatic difference of magnification and chromatic

difference of position [68].

15

Figure 1.6 The magnitude of longitudinal chromatic aberration (LCA) in an average human eye, shown in context of the retinal thickness. As shown, incident blue and red wavelengths will focus nearly a full retinal thickness apart in depth. LCA curve is from Thibos et al. 1992 [69].

1.4 Ophthalmic Adaptive Optics

Liang et al. demonstrated in 1997 that AO technology, originally conceived by the

military for defense purposes, could be applied to retinal imaging, to measure and

correct optical aberrations in vivo before or while acquiring the images [70]. Of the AO

ophthalmoscopes, the AOSLO is most relevant to the work described in this thesis, and

is discussed in depth in Chapter 2. The AO sub-system of most current AO

ophthalmoscopes contain three key components: a light source used for wavefront

sensing (and potentially also used for imaging), a Shack-Hartmann wavefront sensor,

and a wavefront corrector that is used to introduce a wavefront equal and opposite to

that measured by the wavefront sensor (see Figure 1.7). When a closed-loop paradigm

16

is employed, each subsequent adjustment of the wavefront corrector is visible to the

wavefront sensor, and the correction process is iterated until a satisfactory correction is

achieved [70].

Different adaptations of each component of ophthalmic AO systems have been

proposed, and some have demonstrated indirect wavefront estimation without a

traditional wavefront sensor, in a so-called sensorless approach. Here, iterative

adjustments to the wavefront corrector are made after calculating image metrics such as

brightness [71, 72] or sharpness [73]. The most popular ophthalmic wavefront correctors

are continuous sheet deformable mirrors [70, 74-77], however segmented mirrors have

also been demonstrated [78, 79]. Liquid crystal elements have also been tested both for

ophthalmic imaging and vision testing [80, 81].

17

Figure 1.7 Closed loop ophthalmic adaptive optics imaging schematic. In order to correct for ocular aberrations, the retina is illuminated, and a wavefront sensor measures the wavefront error. The inverse error is applied to an adaptive mirror, resulting in a cancelling of aberrations and improved final image quality. From Carroll et al. 2005 [82] with permission.

18

Chapter 2 Adaptive Optics Scanning Light Ophthalmoscopy

The combination of adaptive optics and scanning light ophthalmoscopy (AOSLO)

created what can be conceptualized as a confocal microscope with the cornea and lens

functioning as the objective lens, and the retina as the sample. Therefore, the axial and

transverse image resolution of an AOSLO will always be tied to the optical properties of

the eye being imaged. As with confocal microscopes, AOSLO imaging provides high

contrast images through rejection of out-of-focus light when using confocal detection

(optical sectioning).

2.1 The Role of the Pinhole

The resolution improvement provided by the pinhole in confocal AOSLO with

coherent illumination and imaging can be summarized as [83]:

𝑃𝑆𝐹𝐴𝑂𝑆𝐿𝑂 = 𝑃𝑆𝐹𝑖𝑙𝑙𝑢𝑚𝑖𝑛𝑎𝑡𝑖𝑜𝑛 × (𝑃𝑆𝐹𝑖𝑚𝑎𝑔𝑖𝑛𝑔 ⨂ 𝐷(𝑥, 𝑦)), ( 1 )

where 𝑃𝑆𝐹𝑖𝑙𝑙𝑢𝑚𝑖𝑛𝑎𝑡𝑖𝑜𝑛 and 𝑃𝑆𝐹𝑖𝑚𝑎𝑔𝑖𝑛𝑔 are the amplitude point spread functions, in

illumination and imaging respectively, ⨂ represents convolution, and 𝐷(𝑥, 𝑦) represents

the transmission function of the confocal detection pinhole in the image plane.

If the illumination in the pupil was a plane wave (i.e., uniform amplitude and

phase), the three-dimensional axial and radial extent of the amplitude PSF of diffraction-

limited rotationally symmetric optics with unit radius can be described under the paraxial

approximation [84] as:

𝑃𝑆𝐹𝑖𝑙𝑙𝑢𝑚𝑖𝑛𝑎𝑡𝑖𝑜𝑛 ∝ ∫ 𝐽0(𝜈𝜌)𝑒−𝑖𝑢𝜌2

2

1

0

𝜌 𝑑𝜌, ( 2 )

where 𝐽0 is the zero order Bessel function, ρ, ν and u are dimensionless radial and axial

coordinates respectively, defined by:

19

𝜈 =

2𝜋

𝜆(

𝑎

𝑓) √𝑥2 + 𝑦2, 𝑢 =

2𝜋

𝜆(

𝑎

𝑓)

2𝑧.

( 3 )

Here 𝑎 is pupil diameter, λ is the wavelength, 𝑓 is the focusing optics focal length, with

𝑥, 𝑦 as coordinates in the transverse and 𝑧 in the axial direction. From these equations,

the distances from the focal point on the optical axis to the first minimum in the radial

and axial dimensions are:

𝜌𝑚𝑖𝑛 ≈

1.22 𝜆 𝑓

2 𝑎, 𝑧𝑚𝑖𝑛 ≈ 2 𝜆 (

𝑓

𝑎)

2. ( 4 )

These distances are usually accepted as the lateral and axial resolution for a

traditional diffraction limited bright-field microscope. However, the resolution in a

confocal microscope requires numerical evaluation of Equation ( 2 ), with some

modifications to account for the nature of the imaging signal (e.g., reflectance vs.

fluorescence). The plots in Figure 2.1 compare resolution and detected signal as

functions of pinhole size normalized to 2ρmin (i.e., Airy disk diameter; ADD) [85]. There

are several key concepts displayed in the figure below. First, it is apparent that the

maximum lateral resolution improvement offered by the pinhole is only realized at sizes

smaller than 1 ADD. With pinhole diameters approaching zero this resolution benefit is

approximately 25%, however, the accompanying reduction in the detected signal limits

the application of this approach. In our experience, even the approximately 10%

improvement offered by a 0.5 ADD pinhole is not achievable given the increased

requirements for exposure duration. As described below, longer recording intervals

compound error in the registration and averaging processes. With increasing pinhole

size beyond 1 ADD, however, lateral resolution remains constant.

20

Figure 2.1 The role of the detection pinhole in a confocal instrument. Assuming a numerical aperture of 0.24, which corresponds to a human eye with an 8 mm dilated pupil. Lateral (A.) and Axial (B.) resolution versus pinhole size, as fraction of the Airy disk. Panel C. reveals the detected signal after the confocal pinhole as a function of its size [85].

In the case of all the objects shown in Figure 2.1B, the maximal axial sectioning

benefit is also derived with pinhole sizes of approximately 1 ADD, after which there is

little improvement. An ideal point or line object is well sectioned at all pinhole sizes,

however, these are poor representation of the structures of interest in the retina.

Considering retinal layers as ideal reflective (e.g., photoreceptors) or fluorescent (e.g.,

21

RPE) planes without thickness, it is clear that adjacent features have a large impact on

the size of the axial blur. The worse performance of a fluorescent object is most likely

related to the isotropic (incoherent) emission of fluorescence.

As mentioned previously, the downside to incorporating a pinhole of decreasing

size is that the energy detected reduces dramatically at sizes less than 1 ADD. In the

end, most AOSLO system designers have adopted an approximately 1 ADD pinhole, as

a compromise between signal, transverse resolution and axial sectioning.

2.2 AOSLO Components

Current AOSLOs can be thought of as comprised by five main subunits:

illumination arm(s), optical scanning engine, wavefront sensor, wavefront corrector and

light detection or imaging arm(s) [17, 18, 24, 29, 75, 86-93]. These share relay optics

that create a sequence of alternating pupil and retinal conjugate planes within the

system, which accounts for the difference in size of each optical element placed in the

pupil conjugate planes (optical scanners, wavefront corrector(s) and wavefront sensor).

Illumination Arm(s)

In nearly every AOSLO to-date, optical fiber-coupled light sources are used to

provide point-source illumination. The light emerging from the fibre is collimated using a

relatively long focal length achromatic lens to achieve uniform pupil illumination, low

wavefront aberrations and low longitudinal chromatic aberration (when using broadband

light sources). Often overlooked, all three conditions are essential for achieving a

diffraction-limited illumination PSF and maximum image resolution (see previous

section). The light sources are typically a superluminescent diode (SLD) or a

supercontinuum source to minimize speckle through the low temporal coherence when

imaging in reflectance or a laser if imaging fluorescence. Multiple independent sources

22

can be used simultaneously or sequentially for wavefront sensing, imaging or

psychophysics after coaxial alignment along the same optical path using fold mirrors and

dichroic beamsplitters. Separate collimating lenses allow for independent focusing and

manipulation of the pupil intensity profile (e.g., apodization [94]).

Due to the raster scanning of the point illumination, temporal modulation can be

used to either illuminate or stimulate the retina with pre-determined patterns [95-98].

When it is not feasible to directly modulate the source at the desired frequency (e.g.,

current supercontinuum sources) or at all, additional optical devices can be used

instead, such as acousto-optic modulators [95, 99].

Raster Scanning

The optical scanning in current AOSLOs is accomplished through the use of a

fast, often resonant scanner (> 8 kHz) and a slow galvanometric scanner. Traditionally,

these are oriented horizontally and vertically respectively (see Figure 2.2) following the

convention of the TV raster. The frame rate is thus determined by the line rate

(horizontal scanner frequency) divided by the number of lines to be included in the

frame, and it is limited by the resonant scanner frequency (currently ≤16 kHz). The

AOSLO maximum field of view is determined by the maximum mechanical scan angle

and size of the resonant scanner, with most AOSLOs achieving maximum fields of view

between 2° and 3° of visual angle. These field sizes are comparable to the largest patch

of retina over which the ocular aberrations can be considered constant, also known as

the isoplanatic patch [100, 101]. Expanding the field of view larger than this will lead to

degradation of the image quality at the periphery of the frame. It is important to note that

increasing pupil size leads to decreasing isoplanatic patch size [100].

23

Figure 2.2 Simplified schematic of an AOSLO with confocal detection illustrating the raster scanning of a point source onto the retina to create an image. Two orthogonal optical scanners are used to create a 2-dimensional raster pattern on the retina, which is sampled at regular intervals by a light detector to create an image. In practice, only the central portion of the raster in one scanning direction (solid line) is recorded. The source fiber tip is optically conjugate to the retina, which is in turn optically conjugate to the plane of the confocal pinhole. Not shown are optical relay telescopes between pupil elements. The red rays represent light emerging from the eye, via reflectance or fluorescence emission.

24

Wavefront Measurement and Correction

The AOSLO wavefront sensing beam or beacon is typically delivered to the eye

through the optical scanners and the wavefront corrector in order to average the ocular

monochromatic aberrations across the field of view and provide a high signal-to-noise

ratio for wavefront measurements. Alternative configurations, for example bypassing the

wavefront corrector, have also been demonstrated [19]. Light returning from the eye is

divided using either amplitude or a dichroic beamsplitter between the light detection and

wavefront arms, as shown in Figure 2.3. Given the multi-wavelength illumination

described above, it is important to note that besides defocus (longitudinal chromatic

aberration described below) monochromatic aberrations can be assumed to be constant

with respect to wavelength [66, 67].

Wavefront corrector systems are designed based on the magnitude of correction

or “stroke” needed. As discussed in Chapter 1, the largest contribution to

monochromatic aberrations is defocus, which varies widely across the population [102].

The stroke 𝑆, needed to correct a wavefront with pure defocus can be approximated

from the sagitta equation:

𝑆 = 𝑅 − √𝑅2 − 𝑎2 ( 5 )

Here, 𝑅 represents the radius of curvature of the spherical wavefront emerging from the

eye and 𝑎 is the pupil radius. Roughly 25% of the US population has myopia of at least

one diopter (D), with 5% having at least 5 D [102]. For an 8 mm pupil, the corrector must

provide at least 40 μm of excursion, in order to correct 5 D of defocus assuming the

magnification between the eye and corrector is 1:1. If the corrector is a mirror only half of

this stroke is required, as the path difference will be doubled after reflection. This

relatively large stroke requirement has been addressed using a variety of methods

25

including: trial ophthalmic lenses [12, 18, 19, 103, 104], two wavefront correctors (one as

a “woofer” to correct large magnitude low-order aberration and a “tweeter” to correct the

higher-order aberrations) [105-107], or an adjustable Badal optometer [108]. More

recently, the development of large stroke deformable mirrors with high spatial resolution

of actuator effect, has allowed simultaneous correction of the low- and high-order

aberrations with a single device [75, 87]. All of these approaches require an iterative

wavefront correction approach, given the dynamic changes of the ocular aberrations

[109-111].

A simplified approach to adaptive optics control is provided below. In order to

accurately measure and correct wavefront error in a patient’s eye, it is necessary to first

calibrate the wavefront sensor. This is done by providing a flat (reference) wavefront that

bypasses all of the system optics and traverse only the lenses or mirrors that are unique

to the wavefront sensor path. The local beam slopes are sampled by the lenslet array,

and the centroids (centers of mass) of the resulting focused spots of light are saved. All

future wavefront sensing is performed in reference to these spot coordinates. Then the

wavefront corrector is calibrated through the use of a collimated source that illuminates

the wavefront corrector (in single pass) such that it traces backwards towards the

wavefront sensor, coaxially with the illumination path. After alignment each element of

the wavefront corrector is sequentially activated, inducing a deflection in the previously

flat wavefront measured in the Shack-Hartmann sensor. The magnitude of the x-y

displacement of each Shack-Hartmann spot versus the force applied to each wavefront

corrector’s element, that is the influence response matrix, is then saved for later use This

matrix is later inverted to use as part of the control algorithm, to pursue a least-square

fitting of the ocular monochromatic aberrations.

26

In order to control the wavefront corrector during patient imaging, a matrix of spot

displacements are measured in x and y from the reference spot locations. Using singular

value decomposition, the pseudoinverse of the influence response matrix is calculated

which provides a least square regression fit to the measured wavefront. Multiplication of

the pseudoinverse matrix by the displacement matrix results in a vector of control signals

(e.g., voltage for actuators). These control signals are generally applied after

multiplication by a fractional gain to prevent overshoot, as well as subtraction of a small

“bleed” chosen to minimize the effect wavefront corrections invisible to the wavefront

sensor. The sensing-correction loop is run continuously during imaging, generally at or

below the image acquisition frame rate. For optimal performance, the number of

wavefront sensing elements and correcting elements should be matched with a ratio > 1

[112].

Light Detection

Light returning from the eye diverted to the AOSLO imaging arms can be further

divided by wavelength [19, 75] or polarization state [113] to multiple detection channels.

In order to achieve optical sectioning, the light is focused onto a circular pinhole placed

in a retinal conjugate plane. As discussed above, the size of this pinhole determines the

lateral resolution and the axial sectioning. Additional masks can be placed in retina

conjugate planes to divert non-confocal (multiple scattered) light to additional detectors

as discussed later in this work. A substantial part of this thesis is centred on the

development of novel non-confocal imaging techniques and their application, shown in

Chapters 4-7. In order to minimize exposure to light, highly sensitive point detectors

such as avalanche photodiodes or photo-multiplying tubes are used for all imaging

27

modalities [114]. The detector(s) output is (are) converted to an analog voltage that it is

digitized at regular time intervals to create a retinal image.

Figure 2.3 Schematic of AOSLO used for this work, flattened for display. Multiple light sources are combined with dichroic mirrors and then enter the system via a 90/10 beamsplitter. Spherical mirrors relay the light between scanners, the wavefront corrector, and finally the subject’s eye. Light returning from the eye re-traces the incoming path, passing through the beamsplitter to reach the photomultiplier tube detectors and the Shack-Hartmann wavefront sensor. PMT stands for photomultiplier tube, P for conjugate pupil plane, PH for pinhole, F for filter, and D for dichroic mirror.

2.3 Optical Setup

The previously described components are integrated using a sequence of relay

optics, typically afocal telescopes that relay the entrance pupil of the system to each of

the optical scanners, the wavefront corrector, and finally, the pupil of the eye. The relay

optics themselves could be mirrors or lenses, however, with few exceptions [89, 115]

28

reflective elements have been preferred to minimize back-reflections, which could

overwhelm the signal collected from the eye. Mirror based designs are also not without

fault, since tilting each pair of mirrors, which form a relay, induces astigmatism [116].

Recently, however, orthogonal folding of these elements has been demonstrated to

mitigate this problem [75, 87]. Significant back-reflections are also possible in a reflective

system, if an internal image plane approaches the surface of any mirror.

As mentioned above, the field of view in AOSLOs is limited by hardware to a very

small field (2°-3°). Two approaches have been developed to overcome this limitation and

thus increase the retinal coverage. These approaches were not utilized in the work

described in this thesis. The first being the use of interchangeable relay paths in the

system to adjust the magnification of the exit pupil at the eye [117]. In this way, the field

of view is expanded by the same factor that the pupil is demagnified. This has two major

drawbacks in that the lateral resolution degrades proportionally to the increase in field of

view, and light collection efficiency is also reduced (quadratically, to a first

approximation). The second more powerful approach is to allow for “beam steering”

within the subject’s retina. By incorporating a set of large diameter relay optics close to

the eye, additional optical scanners can be utilized to rapidly steer the field of view [115,

118]. Beam steering allows the subject to fixate comfortably on-axis, while retinal images

are recorded at multiple retinal eccentricities. Figure 2.4 demonstrates how beam

steering can simplify AOSLO image acquisition. After a retinal magnification calibration,

the experimenter selects regions of interest and acquires images at those locations

without adjusting the subject’s fixation.

29

Figure 2.4 Example of AOSLO beam steering for increased acquisition speed. The user selects areas on a previously acquired clinical image (a), and then relocates the scan raster to that region. Panels (b)-(g) show the clicked area (solid circle) and the center of the image acquired at that location. The error between planned and actual imaging location is small. Reprinted from Huang et al. 2012 [24], with permission.

2.4 Chromatic Aberration Compensation

The LCA of the human eye results from contributions from the corneal and

crystalline lens surfaces, and can be coarsely calculated assuming that each of those

surfaces is a thin lens. Even though LCA has been reported as nearly constant across

the population [65], there are several key factors that may lead to underappreciated

inter-subject differences. First, differences in corneal topography [119-123], which

provides two thirds of the optical power of the eye, lead to appreciable changes in LCA

magnitude across the population. Second, LCA varies with imaging eccentricity, due to

changed angles of incidence on the ocular optics [124]. Finally, LCA may vary with age-

related changes in the crystalline lens and especially with cataract surgery, where the

native lens is replaced with an implant [125].

There is no adaptive LCA correction in existing AOSLO instruments. Rather, the

predicted average LCA [126] is minimized by adjusting the relative vergence of each

source at the subject’s pupil as was done in the work presented here. Achromatizing

30

lenses to nullify the average LCA [127, 128] have been incorporated into several

AOOCT systems [129, 130] and have been proposed for AOSLO. The shared goal of

most strategies is to bring short (e.g., 488 nm fluorescence excitation) and long (e.g., >

700 nm wavefront sensing) sources to focus at the same retinal depth, to allow for

simultaneous multi-wavelength imaging.

In addition to LCA, transverse chromatic aberration (TCA) presents another

potential source of error when illuminating the retina with multiple wavelengths. The two

components of TCA include chromatic difference of magnification and chromatic

difference of position [68]. Chromatic difference of magnification, or a difference in image

size as a function of wavelength, is caused by the eye’s LCA and the physical separation

of the eye’s entrance pupil and front nodal point. Chromatic difference of position is the

difference in image location as a function of wavelength, caused by off-axis illumination,

and is proportional to the TCA. Since both chromatic difference of magnification and

chromatic difference of position are proportional to LCA, the best method to compensate

for TCA in an imaging system is to remove the LCA to the greatest extent possible.

Although not applied in the work presented here, residual TCA may be removed by

objective measurements [131]. In addition to these contributions to ocular TCA,

misalignments of each independent illumination source within the AOSLO contribute

non-negligible amounts to the final amount of TCA. Harmening et al. [131] demonstrated

that even after careful LCA correction, the residual TCA (see Figure 2.5) could be

corrected for after careful calibration using the information in the images collected with

different wavelengths.

31

Figure 2.5 Illustration of transverse chromatic aberration in AOSLO imaging. The same patch of retina was imaged simultaneously with NIR, red, and green light. The resulting images are shifted relative to each other due to TCA. From Harmening et al. 2012 [131], with permission.

Scale bar 10 μm.

2.5 Image Registration and Averaging

The fast optical scanners of AOSLO often oscillate with a sinusoidal velocity

pattern, and since the pixel acquisition rate is almost always constant, this creates

uneven sampling across the recorded frame (see Figure 2.6). To remove this sinusoidal

image warping, images of calibrated grids (e.g., Ronchi rulings) are collected. The

images of the grid, oriented vertically and horizontally, are fitted to a sinusoid and a

linear curve respectively, to calculate the image transformation that would ensure 1:1

pixel aspect ratio across the image, as illustrated by the right panel in Figure 2.6.

32

Figure 2.6 Desinusoiding to remove distortion from the sinusoidal motion of the resonant scanner. Images of a Ronchi ruling are acquired with the AOSLO (above and left of images; fast scanning is along the horizontal), from which the sinusoidal distortion can be measured. The AOSLO image sequences are resampled along the horizontal dimension to create “square” pixels.

In order to improve the signal-to-noise ratio of AOSLO images, multiple de-

sinusoided and registered frames are often averaged together. Prior to this, image

distortions caused by the point scanning nature of an AOSLO, combined with constant

involuntary eye motion, must be removed. Image registration techniques for AOSLO

images are nearly universally equivalent between research and commercial groups [88,

132, 133]. The approach, first described by Stevenson [132], requires manual selection

of a reference frame from the AOSLO video with minimal distortion. The remaining

frames of the video are each divided into thin strips, with the long dimension aligned to

the direction of the fast scanner. Each strip of each frame is then compared to the

reference frame by cross-correlation, to calculate the offset (in pixels) of the strip with

respect to the reference frame. After the offsets are applied to each strip of a frame,

image metrics can be used to decide which of all the frames should be used in the

averaging. If multiple image sequences are recorded simultaneously (e.g., reflectance

33

and fluorescence), the registration can be applied identically to both sequences to

improve the signal-to-noise ratio of both final images (see Figure 2.7).

Figure 2.7 RPE autofluorescence imaging with AOSLO. Individual fluorescence frames have low signal compared to reflectance frames. The inter- and intra-frame motion is measured in the reflectance sequence and then the motion correction is applied identically to both the reflectance and fluorescence sequences. Registration and averaging of 300 frames improves the signal-to-noise of the fluorescence sequence substantially and reveals the hexagonal mosaic of RPE cells. Scale bar 100 μm.

2.6 Eye Tracking

While off-line image registration (described above, and used exclusively for the

work presented here) can correct eye-motion to sub-pixel accuracy, it is would be highly

desirable to stabilize the eye motion during AOSLO image acquisition. This is both to

34

deliver precise psychophysical stimuli [95-98] and to facilitate image registration. The

latter is critical for patients with extreme involuntary eye motion (e.g., nystagmus; see

Figure 2.9).

In order to stabilize AOSLO acquisition in real-time, the registration technique

described above can be incorporated on-line [99] as so-called software eye-tracking, by

calculating eye motion in real time and predicting the position of the eye to successfully

deliver a psychophysical stimulus with high accuracy [99, 134]. This approach does not

stabilize the beam on the retina itself, but rather stabilizes the position of each cone or

feature of interest with respect to the recorded frame. The critical assumption is that an

undistorted reference frame can be captured to initiate the stabilization, which is often

difficult or impossible in patients with poor fixation or poor cooperation.

If, however, the same calculated shifts are provided to a tip-tilt scanner within the

AOSLO system, then the scanner can adjust the imaging raster to maintain a completely

stable illumination patch on the retina. These scanners offer the ability to maintain a

vertical scan pattern while displacing the raster globally in the horizontal or vertical

direction [135]. Alternative eye motion stabilization approaches, using a lock-in detection

scheme tracking on a vessel junction, have been demonstrated [86, 118].

35

Figure 2.8 Representative single image frames from a patient with significant eye motion. The image on the right is significantly more distorted from intra-frame motion than that on the left, however, both frames demonstrate similar contrast and sharpness. Arrows indicate identical photoreceptors. Scale bar 50 μm.

2.7 Confocal Imaging

Until recently, the majority of AOSLO imaging studies were focused on the

photoreceptor outer segment layer. In fact, out of over 120 original articles utilizing

AOSLO between 2002 and 2014, more than 80 imaged only the photoreceptor layer.

This was partially motivated by the fact that the photoreceptor waveguiding [136]

provides a high-contrast bright reflection/back-scattered signal. Advances in AOSLO

system design have made it possible to image rod and cone photoreceptors within the

central 30 degrees of fixation [75, 87, 137] in normal subjects and many eye diseases

(see Table 2.1). In agreement with histology [33], confocal AOSLO images show that

cone photoreceptors follow the increase in spacing with retinal eccentricity (see Figure

2.9) [87, 88, 137], and rod photoreceptors begin to appear outside of the anatomical rod-

free area (see Figure 2.10) [137].

36

Figure 2.9 Cone photoreceptor imaging and density measurement with AOSLO. In agreement with histology [33], photoreceptor number measured with AOSLO is highest at the fovea and decreases significantly in the periphery [138]. Rod photoreceptors become visible as the smaller bright dots in peripheral images (C & D). As the cone photoreceptors increase in diameter, their reflection often takes on an inhomogeneous or multimodal appearance (C & D). Scale bar 25 μm.

Cone photoreceptor identification in AO images was initially a manual process,

but now fairly robust semi- or fully- automatic algorithms have been developed [139-

141]. The performance of these algorithms is variable across operators, diseases, and

instruments, but repeatability and reliability is constantly improving. For example, the

algorithm by Li and Roorda achieves ~94% agreement with manual counts in high-

quality images, with a coefficient of repeatability of ~3% when the results are manually

inspected and corrected [142]. The coordinates of the identified cells are evaluated using

a variety of geometrical descriptors including: cell density [86, 108, 143], nearest

neighbour distance [144], cell spacing [145, 146] and Voronoi tessellation [137, 147].

37

While the quantification of these images is by no means settled, it is clear that some

metrics are highly susceptible to cell misidentification (e.g., density), which represents a

challenge in the presence of pathology or poor image quality. Other metrics, such as cell

spacing, are robust to missed identification of cone photoreceptors [145], but this very

reason makes them less sensitive to detecting random (as opposed to focal) cell loss

due to disease. This strongly suggests that photoreceptor mosaic metrics for diagnosing

or monitoring of eye disease will have to be tailored to each condition.

Figure 2.10 Rod photoreceptor imaging with AOSLO at various eccentricities. Rod photoreceptors show temporal fluctuation in reflectivity similar to cone photoreceptors. By averaging rod images, acquired once an hour over the course of twelve hours, the reflections are normalized and the image quality improves (right panel). Scale bars 25 μm. Images are reprinted from Dubra et al. 2011 [137] & Cooper et al. 2011 [148], with permission.

Even in subjects without eye disease, the photoreceptor reflectance is highly

variable between cells and within the same cell over time. The same cells imaged over

38

multiple hours to days can be followed through cycles of dim to bright reflectivity [96,

148-150]. The complexity of these variations is compounded by light-evoked responses,

different from bleaching [151]. The time-kinetics of this phenomenon are believed to be

related in some way to the cell’s processing of visual pigment, and thus could offer a

biomarker to evaluate the cell’s functional status [152].

Table 2.1 Confocal AOSLO studies of retinal disease

Disease Studies (see bibliography)

Achromatopsia [153, 154] Acute macular neuroretinopathy [155] Acute zonal occult outer retinopathy

[156]

Age-related macular degeneration

[157, 158]

Best vitelliform macular dystrophy

[159]

Branch retinal vein occlusion [160] Central serous retinopathy [86] Chloroquine retinopathy [161] Choroideremia [146, 162, 163] Colorblindness [143, 164, 165] Cone-rod dystrophy [104, 145, 166, 167] Cotton-wool spot [24] Diabetes [168, 169] Epiretinal membrane [170] Fundus albipunctatus [171] Glaucoma [25, 28, 30] Macular hole [172, 173] Macular Telangiectasia [174, 175] NARP syndrome [147, 162] Night blindness [152] Other inherited degenerations [167, 176-178] Pigment epitheliopathy [179] Retinitis pigmentosa [90, 145, 162, 180] Stargardts [181] Unexplained scotoma [182, 183] Ushers syndrome [162, 180, 184] X-linked retinoschisis [185]

39

While anatomical studies of the normal photoreceptor mosaic are important to

the in vivo understanding of retinal anatomy, the application of confocal AOSLO to the

study of photoreceptors in retinal disease has provided new insights into the cellular

changes associated with many retinal diseases. The most common pathologic finding in

AOSLO photoreceptor imaging has been the reduction of cone photoreceptors density

(increased spacing), along with the “dark-cone” phenotype (shown in Figure 2.11),

where diseased, injured or otherwise abnormal cones reflect very little to no light [186].

40

Figure 2.11 Examples of “dark cones” (arrows) found in photoreceptor disease and retinal disruption. Examples are shown from red/green colorblindness (R/G CVD), acute macular neuroretinopathy (AMN), achromatopsia (ACHM) and closed globe blunt ocular trauma (cgBOT). Scale bar 20 μm. Figure reprinted from Carroll et al. 2013 [186], with permission.

41

In areas with thin nerve fiber layer, confocal AOSLO imaging also reveals some

retinal capillaries with microscopic detail [12, 13]. In order to create an angiographic

image of perfused vessels and capillaries without injecting a fluorescent dye in the blood

stream, temporal image intensity variations due to blood cell motion are translated into

contrast. In this way, motion contrast maps, such as that shown in Figure 2.12, can be

created, which show the vessels as bright lines on a dark background. This type of

imaging has been performed with different illumination wavelengths, and though green

light may give the best signal-to-noise result [187], NIR light has been adopted to

improve subject comfort and safety [13]. Finally, confocal AOSLO has also been used to

image the optic nerve head [28-31] and the nerve fiber layer [23-27] in normal and

diseased eyes. The improved lateral resolution of the instrument allows for visualization

of individual lamina cribrosa pores and individual nerve fiber bundles (see Figure 2.13).

Figure 2.12 Capillary mapping with confocal AOSLO. Image sequences of the photoreceptor layer were acquired (A), then the temporal changes in brightness of each pixel were calculated to create a variance map (B). Pixels that fall within blood vessels fluctuate in intensity due to blood cells traversing the frame, resulting in higher motion signal. The full border of the foveal avascular zone is revealed with this technique (B). Scale bar 300 μm. Figure reprinted from Tam et al. 2010 [13], with permission.

42

Figure 2.13 Nerve fiber layer and lamina cribrosa imaging with AOSLO. Left panel shows the lamina cribrosa imaged in a non-human primate. Dark areas represent pores, through which the nerve fibers travel on their exit from the eye. Right panel shows the nerve fiber layer adjacent to the optic disk in a 62-year-old human subject. Bright striations represent nerve fiber bundles. Scale bar 200 μm. Figure reprinted from Carroll et al. 2013 [186], with permission.

2.8 Single-photon Fluorescence Imaging

Since the combination of AOSLO and the eye is in effect a confocal microscope,

it is only natural to explore fluorescence imaging in the living eye. In fact, this is already

done routinely in the clinic to study the retinal vasculature (fluorescein angiography) and

the retinal pigment epithelium (through the visualization of the intrinsic fluorescence of

43

lipofuscin). In practice, the only modifications required to switch from reflectance to

fluorescence imaging are illumination with an appropriate excitation wavelength and the

addition of a barrier filter in the imaging channel. A major practical challenge, however, is

the registration of the recorded image sequences, which due to light safety limitations

have less than an order of magnitude signal when compared to the reflectance channel.

This is usually addressed by co-registering the dim sequences to a simultaneously

recorded reflectance image with a dramatically higher signal-to-noise ratio, as

demonstrated in Figure 2.7. In this way, sufficient frames can be recorded and averaged

to provide a high-contrast fluorescence image. To date, the fluorescence AOSLO has

been utilized to image the autofluorescence of the RPE [15, 19, 188, 189], fluorescein

within the retinal vasculature [19-22], and artificially labelled neurons in experimental

animals [17-19].

The placement of the RPE posterior to the photoreceptors makes visualization of

the layer difficult with confocal AOSLO. One solution to visualize the cellular structure of

this layer is to exploit the strong autofluorescent signal described for the RPE cells [190].

Morgan et al. imaged the full foveal RPE mosaic with autoflourescence AOSLO in non-

human primates, and quantified peripheral RPE density in human subjects [15], with

good agreement to previous histologic reports. To date, clinical applications of AOSLO

autofluorescence imaging have been avoided due to light safety concerns [188, 189],

however, this method has promise for the cellular study of RPE pathology if its light

detection efficiency could be improved [191].

The use of AOSLO for fluorescein angiography was motivated by the extensive

literature of histologic microvasculature preparations, and the lack of available AOSLO

vascular imaging techniques to visualize capillaries in areas of thick nerve fiber layer at

44

the time. With AOSLO, fluorescein angiography resolves individual capillaries, and

optically separates the various capillary stratifications in vivo [20, 21]. Though first

applied to non-human primates, AOSLO fluorescein angiography has since been

demonstrated in normal and diseased human subjects. A first survey of vascular

pathologies revealed six distinct microaneurysm morphologies [22] not visible with

standard clinical imaging (see Figure 2.14).

Figure 2.14 Adaptive optics fluorescein angiography. Retinal microaneurysms visualized in a human subject with hypertension. Over a small retinal region, 6 different types of microaneurysms can be visualized. Red and blue dots represent arterioles and venules respectively. Scale bar 300 μm. Figure reprinted from Dubow et al. 2014 [22], with permission.

Despite the lateral and axial sectioning improvements offered by confocal

AOSLO, the vast majority of the neurons in the retina have remained invisible to

reflectance imaging techniques. Thus, in order to visualize inner retinal cells, such as

ganglion cells, investigators have artificially induced fluorescence in these cells. The

interventions described below have only been applied to research animals and are too

invasive to apply to human subjects. Methods to induce fluorescence have included

45

targeted brain injections that carry dye into the retina via the optic nerve, transfection

with viral vectors encoding a fluorescent protein, and the use of transgenic animals that

express fluorescence proteins. To date, these techniques have allowed for individual cell

and neurite imaging in mice [17], rats [18], and non-human primates [16, 19]. The

research applications of this AOSLO technique include in vivo discrimination of ganglion

cell classes based on their morphology [17], which could provide important insight in the

in vivo pathology of common diseases such as glaucoma (see Figure 2.15).

Figure 2.15 Retinal ganglion cells visualized in living mice with fluorescence AOSLO. Fluorescence was induced with viral vector or genetically (5&6). Nearly all of the neurites were visible, and the cells were classified as “ON” or “OFF” ganglion cells based on previous ex vivo literature studies. Scale bar 20 μm. Figure reprinted from Geng et al. 2012 [17], with permission.

A unique application of AOSLO fluorescence imaging of labelled ganglion cells is

the interrogation of function in vivo. The transfection of calcium indicator probes into

retinal neurons allows for the optical measurement of function, as increased or

decreased fluorescence gives a direct readout of neural activity. So far, the indicator G-

CaMP has been successfully transfected in mouse [192] and non-human primate [97].

46

Both of these studies delivered a specific visual stimulus via AOSLO, and measured a

robust functional signal encoded in the resultant fluorescence change.

2.9 Non-confocal AOSLO

Thus far, the AOSLO methods described here take advantage of the resolution

and depth sectioning benefits offered through the use of a confocal pinhole. However, it

has been known since the first description of the SLO, that a substantial multiply

scattered signal can be recorded from the retina, if it is not spatially filtered out by the

confocal detection pinhole [4]. In SLO, multiple scattered images provided improved

non-invasive contrast of three-dimensional alterations to the retina, such as sub-retinal

fluid accumulation and drusen [5, 193]. More recently, Chui et al. were first to

demonstrate this technique in an AOSLO, revealing dramatic images of the retinal

capillaries and vessels, even in areas of thick nerve fiber layer [194]. This seminal work

used a large detection pinhole (~10 Airy disks in diameter) purposely offset in the image

plane, enough to block the confocal spot (see Figure 2.16) [14, 194, 195].

47

Figure 2.16 AOSLO offset pinhole imaging in an area of thick nerve fiber layer. Increasing pinhole offset from a centered position leads to reduction of the specular reflections from the nerve fiber layer and vessels. The vessel appearance from A (arrow) to G changes substantially with pinhole offset. Increasing offset also improves the contrast of motion contrast maps of the vasculature (compare B to H). Figure reprinted from Chui et al. 2012, with permission.

This technique was then applied to the non-invasive visualization of cellular

structure within retinal vessel walls, and identified candidates for vascular endothelial

cells as well as pericytes in vivo in human subjects [14]. Application to the study of

diabetic retinopathy identified subclinical capillary abnormalities including

microaneurysms, wall thickening, local stasis and remodelling [195]. These studies

highlight the promise of non-confocal AOSLO imaging for the study of the retinal

microvasculature. More recent studies have shown that offset pinhole imaging provides

comparable vasculature contrast to that found in fluorescein AOSLO [196], as illustrated

in Figure 2.17.

Despite the progress since the first demonstration of the AOSLO, the summary

above indicates the need for further study in several areas. First, the majority of work

has focused on the photoreceptor layer, with little emphasis on the inner retina.

Furthermore, photoreceptor imaging has not resolved the significance of so called “dark

48

cones,” shown in Figure 2.11. Lastly, non-confocal imaging techniques have been

applied only to the study of the retinal vasculature, and not to imaging of any other

retinal layers, where they may also provide increased structural information. In the

following chapters, several projects that expand the applications of AOSLO are

described.

Figure 2.17 AOSLO fluorescein imaging versus offset pinhole imaging of the avascular zone. The fluorescein image (left) and the offset pinhole image (center) reveal the same capillary network, as shown by the pseudocolor merge image (right). Scale bar 100 μm. Figure reprinted from Chui et al. 2014 [196], with permission.

49

Chapter 3 Application of Adaptive Optics to the Study of the Inner Retina

3.1 Introduction

Since the first demonstration of adaptive optics (AO) for retinal imaging [70], its

main application has been the study of the cone photoreceptors by taking advantage of

their strongly directional reflectivity [136]. Defects in a structural protein [197], membrane

channel [87, 153, 198], physical disruption of the photoreceptor [199] or otherwise [155,

159], show as abnormal reflectivity patterns and often result in decreased or complete

absence of reflectivity (dark cone). Although each independent study has improved the

understanding of a particular disease, when the collective findings are considered

together it becomes clear that impaired cone photoreceptor waveguiding is an important,

yet non-specific biomarker for photoreceptor integrity [186].

Adaptive optics imaging is also being used to study retinal microvasculature

structure and perfusion in normal and diseased eyes [13, 22, 168, 169, 194, 195, 200-

206], with a recent focus on microaneurysms [22, 195]. Studies on diabetic retinopathy

and other conditions [168, 169, 195] have found that microscopic vascular changes and

in particular, microaneurysm morphology, are also not specific to any one disease [22].

As with the dark cone phenotype, microscopic vascular pathology may therefore be

more suggestive of common response mechanisms, rather than disease-specific insults.

With the exception of recent work [23-27], inner retinal pathology remains largely

unexplored with AO ophthalmoscopy. However, in previous work by myself (Scoles D et

al. IOVS 2012;53:ARVO E-Abstract 6957; Scoles D et al. IOVS 2013;54:ARVO E-

Abstract 1434) and others (Gast TJ et al. IOVS 2013;54:ARVO E-Abstract 1507)

performed in a small number of subjects seem to suggest that abnormal epiretinal

highly-reflective structures are also not disease-specific.

50

To expand upon previous work I examined the inner (anterior to the outer

plexiform layer) and epiretinal (anterior to the nerve fiber layer) findings in AOSLO

imagery across a broad cohort of retinal and neurologic diseases. The superior

transverse image resolution and axial sectioning of the AOSLO relative to non-AO

ophthalmoscopes revealed previously unreported hyper-reflective structures (relative to

their surroundings). The results of this study again indicate that similar to the biomarkers

of photoreceptor and vascular diseases, microscopic and macroscopic hyper-reflective

inner retinal features are not unique to a single condition. Although the findings

described here may not be disease specific, their potential for aiding diagnosis,

monitoring disease progression and adjusting management remains to be determined. In

what follows, the appearance of similar inner retinal features across varied conditions is

shown, followed by specific examples highlighting each feature and placing them in

context with fundus photography, scanning laser ophthalmoscopy and en face OCT

sections generated from volumetric imaging.

3.2 Methods

Human Subjects

Research procedures followed the tenets of the Declaration of Helsinki and

informed written consent was obtained from all subjects after explanation of the nature

and possible consequences of the study. The study protocol was approved by the

institutional review board at the Medical College of Wisconsin and the University of

Rochester. Subjects were either referred by their physicians, or self-referred in response

to advertised studies.

Axial length measurements were obtained on all subjects (Zeiss IOL Master; Carl

Zeiss Meditec, Dublin, CA, USA). Using the Gullstrand 2 schematic eye, the predicted

51

291 µm per degree of visual angle [207] was scaled linearly by the subject’s axial length

to determine the scale of AOSLO images. Prior to all retinal imaging, the eyes to be

imaged were dilated and cycloplegia was induced through topical application of

phenylephrine hydrochloride (2.5%) and tropicamide (1%).

Optical Coherence Tomography

In all subjects, spectral domain optical coherence tomography (SD-OCT) line

scans and cube scans were performed in the area of AOSLO imaging (Carl Zeiss

Meditec, Dublin, CA, USA; Bioptigen, Research Triangle Park, NC, USA; Heidelberg

Engineering, Heidelberg, Germany). Dense SD-OCT volumes were also obtained

(Bioptigen, Research Triangle Park, NC, USA), nominally covering 3 3 (400 A-

scans/400 B-scans) or 7 7 mm (1000 A-scans/250 B-scans), and used to create en

face OCT sections approximately 50 µm thick at the retinal layer(s) of interest with

custom software (Java; Oracle, Redwood City, CA). OCT B-scans through features of

interest were generated by registering and averaging 3 adjacent B-scans to increase

signal to noise ratio [208]. The fundus image provided by the line scan ophthalmoscope

(LSO, Carl Zeiss Meditec, Dublin, CA, USA) was recorded for comparison and

registration of AOSLO images. All OCT images are displayed in logarithmic scale.

Confocal Reflectance Adaptive Optics Retinal Imaging

Inner retinal AOSLO images from 101 subjects representing 38 different retinal

conditions (see Table 1 below for full list included in this study), acquired between

August of 2010 and February of 2014 were identified. All images were collected using

either 680 or 790 nm light sources (Superlum, Cork, Ireland), on one of three similar

custom built AOSLO instruments at the University of Rochester and the Medical College

of Wisconsin [75]. Incident powers measured at the cornea were 15-30 µW for the 850

52

nm wavefront sensing light source, 120 µW for the 680-nm source and 80-150 µW for

the 790-nm source. All exposures were kept 5 times below the maximum permissible

exposure as per ANSI Z136.1-2007 [209, 210]. Inner retinal images from 11 subjects

with no history of vision limiting eye disease were also analyzed for comparison. Where

possible, images from subjects imaged on multiple occasions were compared to display

longitudinal changes of inner retinal features. The AOSLOs were focused on the retinal

features of interest by changing the curvature of the deformable mirror (Hi-Speed DM97-

15; Alpao, Giéres, France). A 1-2 Airy disk confocal pinhole was used to provide axial

sectioning and increase the contrast of structures of interest [211]. After the features

were located and in focus, image sequences of 150 frames were captured at 17 frames

per second. The images within each sequence were registered, and the 20-50 images

with highest normalized cross-correlation relative to a user-selected reference frame

were averaged to create a high signal-to-noise ratio result with minimal distortion due to

eye motion [133]. Where possible, multiple registered images were manually tiled using

Adobe Photoshop (Adobe Photoshop; Adobe Systems Inc., Mountain View, CA) to

increase retinal coverage and/or to locate the feature with the context of fundus or OCT

images. All AOSLO images are displayed in linear scale.

3.3 Classification of Inner Retinal Phenotypes

The review of the hyper-reflective structures with consideration of previous work,

as well as feature size, location and subjective perceived texture in the AOSLO images

suggested seven groups. We propose the following names for these findings: punctate

reflectivity, nummular (disc-shaped) reflectivity, granular membrane, waxy membrane,

vessel-associated membrane, microcysts and striate reflectivity (Figure 3.1 & Figure

3.2). For each disease included in this study, all images were examined for the presence

53

of the seven feature groups, with the results summarized in Table 3.1. Given the limited

retinal coverage in the AOSLO imaging protocols used, it is important to note that the

absence of observation on this study does not mean the absence of a structure. In fact,

it is very likely that future high-resolution retinal imaging studies on the conditions

reported here will find features of more types than those encountered in this study. For

this reason, it would be unreasonable to report prevalence of features within each

condition without a larger population and more systematic study.

54

Table 3.1 Diseases and inner retinal phenotypes observed. The columns represent each of the seven findings described here; an “x” indicates that it was observed in our cohort: A punctate reflectivity, B nummular (disc-shaped) reflectivity, C granular membrane, D waxy membrane, E vessel associated membrane, F microcysts and G striate reflectivity. The absence of a feature (no “x”) should not be interpreted as an absolute absence, rather not yet observed. Next to each condition is the number, in parentheses, of patients with that condition.

Pathology A B C D E F G Achromatopsia (5) X X X

Acquired optic disc pit (1) X X X

Acute macular neuroretinopathy (1) X

Age-related macular degeneration (AMD, 2) X X

Astrocytic hamartoma (1) X X X

Autoimmune retinopathy (1) X

Best's disease (2) X X X

Birdshot choroidoretinopathy (4) X X X

Bornholm eye disease (1) X

Branch retinal occlusion (BRAO & BRVO, 3) X X X

Central retinal artery occlusion (1) X X

Central serous retinopathy (3) X X X X X X

Choroideremia (8) X X X X

Color blindness (1) X X

Commotio retinae (5) X X X X

Cone dystrophy (4) X X X X

Cotton-wool spot (1) X

Diabetic retinopathy (2) X X X

Epiretinal membrane (2) X X

Free of disease (11) X X X

Glaucoma (11) X X X X

Leber's congenital amaurosis (2) X X X

Leber's hereditary optic neuropathy (1) X X

Macular hole (4) X X X X X X

Macular telangiectasia (7) X X X X

Microscotoma (3) X X X

Multiple sclerosis (1) X X

Optic atrophy (5) X X X X X X

Optic nerve drusen (2) X

Optic neuritis (3) X

Parkinson's disease (3) X X X X X X

Pathologic myopia (1) X

Premature birth (1) X

Retinitis pigmentosa (4) X X X X X

Retinoschisis (1) X X X

Rubella retinopathy (1) X X X

Stargardt's (1) X

Traumatic Brain Injury (1) X

Unknown retinopathy (1) X X

55

Figure 3.1 Representative images of the first four features described in this study. Punctate reflectivity (A1-4), nummular reflectivity (B1-4), granular membrane (C1-4), waxy reflectivity (D1-4). Diseases in each group include, rubella retinopathy (A1), achromatopsia (A2), optic disc pit (A3), normal (A4), normal (B1), glaucoma (B2), normal (B3), multiple sclerosis (B4), diabetic retinopathy (C1), Parkinson’s (C2), branch retinal vein occlusion (C3), optic atrophy (C4), cone dystrophy (D1), central serous retinopathy (D2), birdshot choroidoretinopathy (D3), age related macular degeneration (D4). B4 is a contiguous vertical montage split into two halves, top is on the left and bottom is on the right. The first column of each row is highlighted below. All scale bars 100 µm.

56

Figure 3.2 Representative images of the final three features described in this study. Vessel associated membrane (E1-4), microcysts (F1-4), striate reflectivity (G1-4). Diseases in each group include, Leber’s congenital amaurosis (E1), Stargardt’s (E2), macular hole (E3), diabetic retinopathy (E4), macular telangiectasia (F1), glaucoma (F2), optic neuritis (F3), optic atrophy (focused on photoreceptors) (F4), Best’s disease (G1), choroideremia (G2), commotio retinae (G3), unknown retinopathy (G4). The first column of each row is highlighed below. All scale bars 100 µm.

3.3.1 Punctate Reflectivity

In many diseases, as well as subjects free-of-disease that were imaged, sparse

punctate reflective structures measuring approximately 3 to 5 µm in diameter (size can

vary with image saturation) are sometimes visible on the inner surface of the retina. In

normal volunteers, especially those aged 30 years old or more, these punctate reflective

structures appear to line the inner surface of the foveal pit (Figure 3.1 A3, A4). Nearly

identical features are observed in and around clinically visible lesions on the inner

surface of the retina or just deep in the ganglion cell layer (Figure 3.1 A1, A2) in subjects

with rubella retinopathy and achromatopsia. It is unlikely that these features are cell

57

somas (bodies), given their small size. It is possible, however, that they represent debris,

or portions of cells. Due to the small size of these features, it is not possible to make any

distinctions between those found in normal versus those found in pathology with current

reflectance confocal AOSLO. There are no apparent differences in the size between

normal and diseased subjects; however, the distributions are vastly different with

diseased areas showing a dramatically increased density of punctate features.

In the case of rubella retinopathy highlighted in Figure 3.3, the punctate features

are thought to be collections of intra or extracellular melanin granules [212]. This lesion

appears dark brown or black in color fundus photography, suggesting that it may have

been formed by pigment migration. Melanin granules are normally 1 – 1.2 µm in

diameter [213], which is below the theoretical Rayleigh resolution limit of 2.2 µm for 790-

nm light and a 7.75 mm AOSLO pupil. This means that any structure 2.2 µm or smaller,

such as melanin granules, will appear in the AOSLO images as 2.2 µm or larger,

depending on object size, focus and image saturation. The average diameter of each

punctate feature was 3.5 ± 0.3 µm (mean ± SD, n = 51) (Figure 3.3), which is consistent

with the expected theoretical size of a melanin granule as seen in our AOSLO

(calculated as the convolution of the granule image with the object point spread

function). The aggregation of punctate hyper-reflective features can span areas greater

than 0.1 mm2, in close association with a retinal vessel, with sparse individual or small

clusters of hyper-reflective puncta observed in the surrounding region. Figure 3.3

highlights the discrepancy between imaging modalities potentially due to the detection

methods, imaging wavelength and magnification, while the pigmented lesion appears

dark in fundus photography and LSO, in en face OCT and AOSLO it appears hyper-

reflective. Although the punctate structures in the foveal pit do not appear to change

58

noticeably over the duration of this study, the lesion in Figure 3.3 changed substantially

over the 18 months between imaging sessions, with apparent reorganization of the

punctate features inside and outside of the lesion (Figure 3.4).

Figure 3.3 Multimodal imaging of punctate reflectivity example A1, rubella retinopathy JC_0830. (A) Fundus photo with outline of AOSLO imaging. (B) AOSLO image showing many small punctate reflective structures approximately 5 µm across. These structures appear to coalesce at the vessel creating what appears to be a membrane in (C) and (D), scale bar 100 µm. Of interest here are the small structures themselves, as they are found in a variety of other diseases and normal (Figure 1 A2-4). (C) Fundus image from scanning laser ophthalmoscope (SLO) of the Cirrus OCT, scale bar 200 µm. (D) En face OCT created from 3 mm OCT volume segmented at the level of the vasculature (horizontal arrows). Square shows AOSLO imaging region of interest, horizontal line indicates location of OCT B-scan in (E). (E) Average of 3 adjacent B-scans in OCT cube, showing hyper-reflectivity surrounding a retinal vessel, scale bar 100 µm.

59

Figure 3.4 Follow up of punctate hyper-reflectivity, an enlarged portion of the lesion shown in Figure 3.3. In the 18 months between the first imaging session (A), and the second imaging session the there are few if any punctate structures that have not changed position. The lesion appears to be expanding along the vessels, and appears to be contracting such that the vessel no longer follows its original course, as illustrated by the dashed lines in (B). Scale bar 50 µm.

3.3.2 Nummular Reflectivity

In addition to the small punctate hyper-reflective features, there are also

substantially larger (approximately 10 to 30 µm in diameter), round hyper-reflective

features sometimes visible on the internal limiting membrane (ILM). Each spot has a

granular texture nearly identical to that of the nerve fiber layer (NFL) below, and some

have a circular clearing near their center, shown in Figure 3.5.

These features are believed by our group and others [214] to be the Gunn’s dots,

described in direct ophthalmoscopy and sometimes visible in fundus photography [215,

216]. Unfortunately, the true identity of Gunn’s dots remains to be elucidated. It has been

suggested that Gunn’s dots are most easily found in younger volunteers, above and

below the optic nerve head (ONH) in areas of the thickest NFL. Our limited data set

supports this statement (Figure 3.1); and also shows these larger round reflective

60

structures in areas of NFL disease or loss. Figure 3.1 shows these structures in

glaucoma and multiple sclerosis, both more than 12º temporal to the ONH. It is uncertain

whether the Gunn’s dots relocated, or were revealed by the pathologic NFL thinning. No

detectable changes were observed in the appearance, location and number of Gunn’s

dots in normal subjects over 2.5 months between imaging sessions, as illustrated by the

images in Figure 3.6.

61

Figure 3.5 Multimodal imaging of nummular reflectivity example B1, normal subject JC_0007. (A) Fundus photo with outline of AOSLO imaging. (B) AOSLO image showing many reflective structures 10-30 µm across glistening on the surface of the nerve fiber layer, scale bar 100 µm. In this image the largest dot is approximately 24 µm in diameter (arrow), and the smallest is only 13 µm (arrowhead). (C) LSO fundus image does not resolve the dots, scale bar 200 µm. (D) En face OCT created from 3 mm OCT volume segmented at the level of the ILM (horizontal arrows) also cannot resolve dots. (E) OCT B-scan does not clearly resolve the small dots, scale bar 100 µm. The size and distribution of Gunn’s dots are inconsistent with glial cell endfeet as

has been hypothesized [214], which should be found across the entire retina in a

contiguous mosaic. It is unlikely that the reflection is due to a retinal cell, since to our

knowledge no glial cell or neuron has been described anterior to the ILM in the absence

62

of disease. The size of the structures and scattered distribution around the ONH anterior

to the ILM is most consistent with hyalocytes [217]. These resident macrophages of the

vitreous are normally dormant, but have been implicated in the formation of epiretinal

membranes [218, 219]. It is hypothesized that they collect at the ONH, as well as the

ciliary body, due to firm attachment of the vitreous fibers at these locations [217].

Figure 3.6 Follow-up imaging of nummular reflectivity in B1, normal subject JC_0007. (A) Initial AOSLO imaging, (B) 2.5 month follow-up imaging reveals that nearly all of the dots have not moved or changed in appearance. Arrows depict a dot that was identified initially but not in follow-up. Scale bar 100 µm.

3.3.3 Granular Membrane

Previous AOSLO imaging of inner retinal membranes, (Gast TJ et al. IOVS

2013;54:ARVO E-Abstract 1507) found that these membranes are common with

advancing age and partial or full posterior vitreous detachment. In agreement with our

findings, Gast et al. noted that these membranes are not always visualized with fundus

photography and OCT. Here, we divide these hyper-reflective membranes into granular

and waxy, based on appearance.

63

Figure 3.7 Multimodal imaging of granular membrane example C1, diabetic retinopathy RS_1007. (A) Fundus photo with outline of AOSLO imaging does not reveal any membrane. (B) AOSLO image shows a meshwork of small reflective granules, although highly reflective, the retinal vessel is clearly visible underneath, scale bar 100 µm. (C) SLO fundus image shows a glistening membrane (arrow), scale bar 200 µm. (D) En face OCT created from 3 mm OCT volume segmented at the level of the ILM (horizontal arrows) also resolves the thin membrane (arrow). (E) OCT B-scan shows a small glistening reflection on the surface of the NFL (arrow), scale bar 100 µm.

The granular membranes (Figure 3.1 C1-4) appear to be composed of many

small bright granules that are connected by a thin dimmer mesh-like structure, which

does not obscure the underlying nerve fiber layer, or vasculature from view. The

appearance is somewhat similar to the punctate features described above, but in these

membranes no isolated puncta can be observed. These structures are often formed by

two or more disconnected areas with smooth or irregular (jagged) boundaries. These

64

membranes often contain hypo-reflective areas through which the NFL and vessels are

visualized. They show no obvious spatial correlation with NFL bundles, but might be

somewhat related to vasculature.

We have not yet observed this type of membrane in the presence of retinal

traction, in contrast to the membranes described in 3.3.4. In the case highlighted in

Figure 3.7, the membrane is visualized in every modality albeit faintly. Although the

membrane appears co-localized with a vessel, the OCT B-scan confirms that the hyper-

reflectivity is epiretinal as opposed to adherent to the vessel itself.

3.3.4 Waxy Membrane

In contrast to the granular hyper-reflectivity, the second category of large

epiretinal finding (>50 µm across) is substantially more reflective than the other

structures reported in this work, and has a smoother texture. We have termed this

category waxy reflectivity, to evoke the impression of candle wax dripped on a flat

surface, not to imply that the composition of the structure itself is substantially different

than the granular membrane. These structures obscure the underlying tissue, yet often

exhibit several abrupt circular clearings within their area (Figure 3.1 D2, D3). In contrast

to all other structures described here, waxy membranes appear to generate a strong

specular reflection in addition to scattering incident light. An example of a normal

specular reflection in the retina is the foveal reflex, visible with ophthalmoscopy, fundus

photography, AOSLO and OCT. This scintillating reflex is caused by the near

perpendicular orientation of the foveal pit to the incident beam, which acts like a mirror.

Waxy membranes behave similarly, with brightness changes with eye movements as

small as 52 µm (Figure 3.8), which is consistent with a specular reflection.

65

Figure 3.8 Illustration of how the specular reflectivity of waxy membranes can lead to dramatic image intensity changes relative to the surrounding structures with eye motion. Panels (A) and (B) are single frames taken from the same image sequence in subject KS_0625 with cone rod dystrophy (Figure 3.1 D4). The relative brightness of the membrane (lower right) changes substantially due to an 80 µm eye movement. An example from subject DLAB_0029 with glaucoma shows a similar phenomenon caused by a 52 µm eye movement (C, D). Each panel is 100 µm across.

Some very large membranes on the surface of the NFL are clearly visualized

with all imaging modalities used in this study (Figure 3.9). We have only found these

membranes anterior to the NFL and often in subjects with what would likely be clinically

diagnosed as epiretinal membrane. Membrane contraction has been noted in some eyes

with these lesions, but not all. An example of a contractile waxy membrane in an

asymptomatic subject is shown in Figure 3.10. The AO image shows the transition from

a brightly reflective membrane which obscures the underlying tissue at the top to clear

NFL bundles at the bottom that appear to undulate in reflectivity due to the topography of

the contracted retina.

66

Figure 3.9 Multimodal imaging of waxy membrane example D1, cone dystrophy KS_1154. (A) Fundus photo shows a yellowish membrane in the region within the region of AOSLO imaging. (B) AOSLO image shows a clumpy, highly reflective membrane on the surface of the retina, obscuring the NFL underneath, scale bar 100 µm. (C) SLO fundus image shows a highly reflective membrane covering a large portion of the superior portion of the image field, scale bar 200 µm. (D) En face OCT created from 3 mm OCT volume segmented at the level of the ILM (horizontal arrows) also resolves the extensive hyper-reflective membrane. (E) OCT B-scan shows a thick hyper-reflective membrane on the surface of the NFL, scale bar 100 µm.

The dramatic reflectivity of these structures suggests a potential connective

tissue component [220]. In agreement with previous work (Gast TJ et al. IOVS

2013;54:ARVO E-Abstract 1507) we found that these membranes can remodel

significantly over only 2 months (Figure 3.11). Despite our categorization of these

membrane-like lesions into two groups based on texture, we do not believe that we have

enough evidence to perform a comparison of the granular and waxy membranes against

67

the classical clinically described cellophane macular reflex (CMR) and pre-retinal

macular fibrosis (PMF) [221].

Figure 3.10 Example of waxy membrane with notable contraction in normal subject JC_10146 OD. (A) En face OCT created from 7 mm OCT volume segmented at the level of the ILM shows a hyper-reflective membrane and contraction lines supero-nasal to the foveal pit. (B) OCT B-scan through the membrane shows a hyper-reflective layer at the ILM spanning over the nerve fiber layer. Arrowheads indicate extent of AOSLO imaging shown in D. (C) OCT B-scan outside of the membrane shows an abnormal peaked appearance to the NFL. (D) AOSLO imaging on and below the membrane shows the clumped waxy appearance of the membrane, as well as the linear radial shadowing in the NFL caused by the contraction of the membrane. All scale bars 200 µm.

68

Figure 3.11 Two month follow up of a waxy membrane in a glaucoma patient, DLAB_0029. Despite the absence of contraction, the membrane has changed in appearance significantly over a short period. Scale bar 100 µm.

3.3.5 Vessel Associated Membrane

In some retinal diseases, striking hyper-reflective membranes are present in

close association to retinal blood vessels. The width of these membranes in the AOSLO

image varies substantially even within a small retinal area and can be as large as four

times the vessel diameter. The composition of the membranes appears to be granular

structures close to or below the AOSLO diffraction limit at the wavelengths used for

imaging. In contrast to the membranes described earlier, these vessel coatings reside

just posterior to the NFL as shown by OCT B-scans. In Leber’s congenital amaurosis, it

has been shown that some genotypes have inflammatory and glial proliferations

surrounding the vessels [222], consistent with the findings in Figure 3.12.

69

Figure 3.12 Multimodal imaging of vessel associated membrane example E1, Leber’s congenital amaurosis JC_0579. (A) Fundus photo appears normal. (B) AOSLO image shows capillary loops entirely coated with a hyper-reflective membrane, scale bar 100 µm. (C) SLO fundus image shows no obvious pathologic changes, scale bar 200 µm. (D) En face OCT created from 4 mm OCT volume segmented at the level of the GCL (horizontal arrows) shows hyper-reflective and disorganized vasculature. (E) OCT B-scan shows many hyper-reflective spots (arrows), corresponding to hyper-reflective vessels, scale bar 100 µm.

3.3.6 Microcysts

Retinal diseases with profound inner retina cell loss, such as glaucoma and optic

atrophy, can sometimes lead to microcystic spaces in the inner nuclear layer (INL) [59,

223, 224]. AOSLO imaging reveals these INL microcysts with sharp distinct edges,[23]

allowing for quantification and monitoring of numbers and size (Roorda A et al. IOVS

2013;54:ARVO E-Abstract 3606). The shape of the microcysts ranges from round to

70

ovoid and from 10 to 140 µm across the longest dimension. Interestingly, these cysts

can act as lenses or obstructions that affect the imaging of the photoreceptors in their

geometrical shadow [225] (Figure 3.2 F4), by altering the image magnification,

brightness, or focus. The appearance of the microcysts changes considerably with

imaging focal plane. Cysts appear as dark circles with bright reflective rims when

imaging across the equator of the cyst, while when imaging at the anterior or posterior

apex the edge appears dark, and there is sometimes a specular reflection (Figure 3.2).

71

Figure 3.13 Multimodal imaging of microcysts example F1, macular telangiectasia subject JC_10075. (A) Fundus photo does not resolve microcysts. (B) AOSLO image shows a scattered distribution of very small (arrowheads) to very large microcysts (arrows), scale bar 100 µm. The borders of the cysts appear darker than the surrounding structure, and nearly all have a bright reflex on their apex. (C) SLO fundus image shows disordered reflectivity, but no microcysts, scale bar 200 µm. (D) En face OCT created from 3 mm OCT volume segmented at the level of the INL (horizontal arrows) resolves only the largest microcysts (arrows). (E) OCT B-scan shows the same two very large microcysts (arrows) seen in AOSLO imaging, scale bar 100 µm.

Numerous microcysts were seen in a subject with macular telangiectasia (Figure

3.13). The large cysts are easily visualized with en face OCT and AOSLO but could be

unresolved by LSO and fundus photography due to poor spatial sampling and the optical

blur introduced by the monochromatic aberrations of the eye. Microcysts in a subject

with dominant optic atrophy were found to remain relatively unchanged over 13 months

(Figure 3.14), in contrast to the dynamic structures highlighted above.

72

Figure 3.14 Thirteen month follow up of the microcysts found in subject KS_1100 with dominant optic atrophy. There are no obvious changes in number or appearance of the microcysts over this time period. Scale bar 50 µm.

3.3.7 Striate Reflectivity

The final feature shown here in inner-retinal AOSLO imaging presents as fine

hyper-reflective striae that run near perpendicular to the course of NFL axon bundles

(Figure 3.2 G1-4). The striations in our limited sample population extend longer than 400

µm, with average separation of 13, 11, 6 and 14 µm respectively for the examples

shown in Figure 3.2 G1-4. This feature has only been noted within the central 10°

around fixation, in very limited regions. Diseases which alter the normal laminar

arrangement of the retinal layers can enhance the back-scattering of the Henle fiber

layer [226], a possible source of the striate hyper-reflectivity reported here. In fact, there

is spatial registration between the Henle fiber reflectance in OCT and AOSLO (Figure

3.15). Although not an inner retinal layer, it is possible that the Henle fiber layer is

visualized with AOSLO at the NFL focus in the parafovea due to the decreased

separation of the layers. In addition to the striations consistent with Henle fibers (Figure

3.2 G1, 2, 4), we have observed striate hyper-reflectivity that does not seem related to

Henle fibers (Figure 3.2 G3). Although these two phenomena are unlikely to be caused

73

by the same cellular processes, their phenotypic appearance is similar and could be

confused in subjects with poor image signal to noise.

Figure 3.15 Multimodal imaging of striate reflectivity example G1, Best’s disease KS_0601. (A) Fundus photo shows the large vitelliform lesion just temporal to the macula. (B) AOSLO image shows the NFL coursing horizontally, while a striped reflective structure runs from vertically, scale bar 100 µm. (C) SLO fundus image also resolves the vitelliform lesion, but not the vertically oriented fibers, scale bar 200 µm. (D) En face OCT created from 3 mm OCT volume segmented along the contour of the Henle fiber layer (horizontal arrows) shows a rim of bright Henle fiber reflectivity surrounding the vitelliform lesion. (E) OCT B-scan shows regions of increased Henle fiber reflection on either side of the vitelliform lesion (arrows), scale bar 100 µm.

3.4 Discussion

A diverse set of inner and epiretinal structures that appear hyper-reflective in

reflectance confocal AOSLO in various ocular and neurological conditions were

74

observed and we propose here a classification based on qualitative appearance. We

envisage that this tentative and non-exhaustive classification scheme will be both

expanded and refined as more eyes with disease and normal volunteers are imaged.

Longitudinal confocal AOSLO studies and additional imaging modalities that provide

complementary structural or functional information might provide information on the

etiology of these hyper-reflective structures.

Arguably, the most important finding of this study is the non-specificity of all the

reported structures when looked at only with reflectance confocal AOSLO. This strongly

indicates that thorough characterization is needed before drawing conclusions and

assigning causation to any given pathology. Similar to AOSLO studies reporting dark

cones [186] and microaneurysms [22], the same microscopic changes in the retina can

arise due to quite different insults. The similarity of the hyper-reflective features on a

cellular scale across such extremely varied conditions as found in this study suggests

that they are more likely caused by common downstream response pathways, rather

than representing distinct disease entities. While the low specificity of any given hyper-

reflective structure to a disease potentially limits the clinical applicability of imaging as a

diagnostic tool, the potentially high sensitivity may provide insight on disease

progression and aid management.

Although wide-reaching across conditions, this study is limited to only a few

subjects per conditions. For this reason, it is not possible to report on a complete

correlation of inner retinal pathology and disease. It is quite possible that each condition

might have a specific retinal locus where these features first appear, or that the feature

size may correlate with severity, but these questions cannot be addressed with the

limited data here. Another limitation of this study is the difficulty of pan-retinal imaging

75

with current AOSLO technology. Current hardware limitations prevent covering large

retinal areas at multiple retinal depths in time scales comparable to current clinical tests,

such as visual fields, although recent work has begun to address this limitation [24, 117].

Finally, due to the retrospective nature of the analysis, we could not target any specific

lesion for extensive hyper-reflective structure coverage.

As new AOSLO imaging modalities are developed, it is important to re-evaluate

inner and outer retinal findings, to refine this classification. For example, non-confocal

techniques implemented in commercial SLO have been shown to enhance the

appearance of structures that disrupt the laminar arrangement of the retina [227, 228].

Our group and others have applied these and other non-confocal imaging to the study of

the retinal pigment epithelium [229], and retinal vasculature [14, 194, 195, 206], but only

in a small number of subjects. Another possibility would be to exploit optical differences

of each feature by adopting techniques such as polarization sensitive imaging [230,

231], or multiple illumination wavelengths [137, 187]. Regardless of imaging method

advancement, analysis of microscopic changes should include a variety of patients, in

order to gain a greater appreciation for the specificity of findings. The data presented

here serves as a useful starting point for future studies examining the inner retina with

AOSLO.

76

Chapter 4 Dark-field Adaptive Optics Ophthalmoscopy

4.1 Introduction

Besides the inner retina, another level of the retina less explored with AOSLO is

the retinal pigment epithelium (RPE). The RPE lies directly posterior to the

photoreceptor layer, with apical processes enveloping the outer segments of rods and

cones [232]. This proximity allows the RPE to phagocytize the photoreceptor outer

segments, and assist in the turnover of visual pigments [233]. This homeostatic role of

the RPE is essential to normal health [234], and diseased RPE has been implicated in

the pathogenesis of age-related macular degeneration [43], diabetic retinopathy [235],

Stargardt’s disease [236], Best’s disease [237], Leber’s congenital amaurosis [238] and

retinitis pigmentosa [239].

Currently, in order to evaluate the health of the RPE in vivo, the autofluorescence

of the fundus is imaged using visible or near-infrared light, with hyper, or hypo-

fluorescence revealing areas of disease and/or cell loss [240-242]. In addition, spectral

domain optical coherence tomographs (SD-OCTs) can resolve two highly reflective

layers believed to contain contributions from RPE cells [243]. Breaks in these reflective

layers are typically interpreted as areas of RPE loss [244]. Recent work suggests,

however, that individual RPE cell loss and dysmorphology are early biomarkers of

disease [43]. Neither conventional wide-field fundus autofluorescence nor SD-OCT can

evaluate structural changes at the cellular scale, and hence the need for higher spatial

resolution imaging techniques. The use of adaptive optics scanning light

ophthalmoscopes (AOSLOs), has allowed in vivo imaging of individual RPE cells in non-

human primates and human volunteers using the intrinsic fluorescence of the lipofuscin

(a byproduct of the visual cycle normally found within the RPE), allowing for analyses

77

previously possible only with histology [15, 93]. Despite this advance, the use of AOSLO

for imaging RPE has remained limited both due to light safety concerns [209, 210, 245,

246] and subject discomfort. Confocal reflectance AOSLO imaging has also revealed the

RPE mosaic using near infrared light, in conditions where the photoreceptors are

diseased or displaced from the RPE due to fluid [167].

The motivation for this work was to develop a method for visualizing the RPE cell

mosaic non-invasively with better light safety than that of autofluorescence imaging.

Experimental evidence below reveals that this can be achieved in subjects with normal

retinal architecture with moderate success, using near-infrared light in a modified

AOSLO. The AOSLO was converted for RPE imaging by replacing the confocal aperture

with a spatial filter inspired by the work of Webb et al. [4] and others [5, 194, 227, 247].

The proposed spatial filter blocks the confocal signal, preferentially attenuating light

back-scattered by the photoreceptors, while passing the light multiply-scattered by the

RPE cells, thus revealing their structure. Various imaging parameters including: pinhole

diameter, filter thickness, illumination and imaging pupil apodization, unmatched imaging

and illumination focus, wavelength and polarization, were varied in order to improve the

visualization of the RPE cell boundaries. This dark-field imaging technique [248] is

validated against AOSLO autofluorescence in a subject free from eye disease, as well

as confocal AOSLO reflectance in a subject with central serous retinopathy.

4.2 Methods

Human Subjects

Research procedures followed the tenets of the Declaration of Helsinki and

informed written consent was obtained from all subjects. The study protocol was

approved by the institutional review board of the Medical College of Wisconsin. Seven

78

normal volunteers were recruited for the study, aged 19-40 years. One 65 year old

subject (DW_1188) diagnosed with central serous retinopathy was recruited for

validation of the dark-field visualization of the RPE mosaic. Axial length measurements

were obtained on all subjects (Zeiss IOL Master; Carl Zeiss Meditec, Dublin, CA, USA)

in order to determine the scale (in µm per pixel) of each retinal image. Prior to retinal

imaging, the eye was dilated and cycloplegia was induced through topical application of

phenylephrine hydrochloride (2.5%) and tropicamide (1%).

Spectral-Domain Optical Coherence Tomography

SD-OCT volumetric and line scan images were acquired (Bioptigen, Research

Triangle Park, NC, USA) at regions of planned AO imaging and averaged as previously

described [208]. A 33 mm macular volume scan was manually registered and used to

create an en face view of the choroid of one subject normal subject age 26 (AD_1025).

AOSLO reflectance imaging

A custom AOSLO [75] was used for this study. The detection path was modified

by replacing the confocal aperture (Figure 4.1 A) in the image plane in front of the

detector, with a larger aperture and a centered filament. Pinholes with 4, 8, 12 and 16

Airy disk diameters (ADD) and filaments 1 and 3 ADD thick were evaluated to enhance

RPE signal relative to that of the photoreceptors. The filament was always aligned to

maximally block the directly back-scattered light (confocal signal) from a model eye. This

is a dark-field configuration [248], in which we believe the source of image contrast is

multiple scattering. To further explore the attenuation of photoreceptor signal, we tested

apodizing masks with annular binary transmission in the pupil of the illumination and/or

imaging paths [94] in one subject (JC_0616) to take advantage of the Stiles-Crawford

effect [249]. Three additional experiments were performed in an attempt to improve the

79

resolution and contrast of the RPE: 1) changing the focus of the illumination while

keeping the imaging channel focused on the RPE; 2) filter the detected light with a linear

polarizer placed in the detector’s pupil plane; and 3) use different wavelengths for

illumination (680 and 790 nm).

Figure 4.1 AOSLO image plane apertures in front of the detector. A) traditional confocal pinhole, approximately one Airy disk diameter (ADD), and B) large pinhole with centered filament.

The imaging light sources were either a 790 nm super-luminescent diode (Superlum,

Carrigtwohill, Co. Cork, Ireland) or a super-continuum light source (NKT photonics,

Denmark) with a tunable band-pass filter (NKT Photonics, Birkerød, Denmark) centered

either at 565 (only for autofluorescence imaging) or 680 nm, with a 10 nm bandwidth.

The wavefront sensing source was an 850 nm super-luminescent diode (Superlum).

Incident powers for these light sources were 70, 60 and 17 μW respectively, measured

at the cornea. The 565 and 680nm sources were never used simultaneously. The

combined light exposure of all three sources was kept a minimum of 5 times below the

maximum permissible exposure set forth by the ANSI Z136.1-2007 [209, 210]. Image

sequences of 150 frames were collected and processed to remove the warp due to the

sinusoidal motion of the horizontal scanner. Those images were then registered, and the

50 images with highest normalized cross-correlation relative to a user-selected reference

frame were averaged to improve signal-to-noise ratio [133].

80

Image sequences were collected at the center of the macula and 10° visual angle

lateral (temporal) to fixation using either a 1.00 or 1.75° square field of view. These

locations were chosen to represent cone and rod dominated areas, respectively. First,

confocal images of the photoreceptor layer (PRL) were collected, using a 1 ADD pinhole

and no filament. This was followed by collection of dark-field image sequences at the

same retinal location at multiple foci using various pinholes and/or filaments. The time

required to exchange and center apertures and filaments was approximately five

minutes. Filament centration was performed by adjusting the x, y and z positioning of the

filament to minimize the light returning from a model eye.

One of the most abundant features in the dark-field RPE images are bright spots

which could be confused with photoreceptors. In order to study the relative position of

these spots in the dark-field images relative to those of photoreceptors in confocal

images, a 45:55 % splitting ratio pellicle beam splitter (Thorlabs Inc., Newton, NJ, USA)

was used during one imaging session to simultaneously record confocal and dark-field

images in perfect registration. Both signals were collected using Hamamatsu H7422-50

photomultiplier modules (Hamamatsu Corporation, Bridgewater, NJ, USA).

AOSLO autofluorescence imaging

The visible channel of the AOSLO was used in subject AD_1025 to record visible

RPE (lipofuscin) autofluorescence images in order to validate the dark-field RPE images.

The lipofuscin autofluorescence was excited with the supercontinuum light source with

the tunable filter reconfigured to provide 60 μW of corneal incident power at 565±5 nm

for excitation. The resulting emission was collected using an interferometric optical filter

with central wavelength 625 nm and 90 nm bandwidth. As in Morgan et al.’s work [15]

high signal-to-noise ratio 790 nm confocal reflectance images were simultaneously

81

recorded to create registered averages of the fluorescence images. The images were

collected with exposures < 20 seconds, using a square 2.0° field of view, resulting in a

light exposure at a level approximately 350 times below the ANSI Z136.1-2007

maximum permissible exposure [209, 210].

Image analysis

The photoreceptor and RPE mosaic images were analyzed in two ways. First,

the radial average of the power spectrum was calculated for all images, with local

maxima indicating the spatial frequency that corresponds to average cell spacing [140].

Next a subset of the images were chosen for semi-automatic cell identification using

custom Matlab software (The Mathworks Inc, Natick, MA, USA) based on the algorithm

of Li and Roorda [139]. The cell coordinates were also used to create Voronoi cell maps

[250] and to estimate nearest neighbor distances.

Point spread function imaging

In order to measure the spatial extent of the dark-field and confocal signals, the

point spread function (PSF) of subject JC_0616 was imaged near fixation using a Qicam

camera (Qimaging, Surrey, BC, Canada) focused on the confocal aperture plane. The in

vivo PSFs were recorded at the best focus for dark-field RPE with an exposure

equivalent to one AOSLO frame (62.5 ms). After acquisition, 10 PSFs were averaged

without registration.

4.3 Results

Dark-field RPE images

The dark-field aperture blocks the confocal signal from the retinal layer that is in

focus. When focused onto the photoreceptors layer (inner/outer segment), this results in

a dramatic attenuation of their signal, thus revealing the RPE mosaic behind, as

82

illustrated in Figure 4.2 and Figure 4.3 using a 1 ADD filament. The panels in these

figures show the photoreceptor mosaic as seen with confocal detection and the RPE

seen with dark-field detection at the center of the macula (fixation) and 10° temporal to

fixation. Similar to what is found in AOSLO autofluorescence images the RPE cells

appear bright at the border and dark at their center. The best contrast in the RPE images

was consistently obtained at the best confocal imaging focus for the photoreceptor

mosaic for all volunteers, to within 0.025 D.

All dark-field images show a mottled background consisting of dark and light patches

many cell widths across. An en face view of the choroid at the same retinal location

using a volume projection from a manually segmented SD-OCT data cube (shown in

Figure 4.4) shows reasonable correspondence with the pattern observed in the dark-field

image. This supports the hypothesis that a significant fraction of the light captured by the

detector in this technique has been reflected by the choroid, as proposed by Webb et al.

[4].

83

Figure 4.2 AOSLO confocal (left) and dark-field (right) retinal images in four different subjects, all collected at the foveal center. The confocal images show the cone photoreceptor mosaic, while the dark-field images show the characteristic hexagonal RPE cell mosaic. The scale bar is 100 μm.

84

Figure 4.3 AOSLO confocal (left) and dark-field (right) retinal images in four different subjects, all collected at 10° temporal to fixation. The confocal images show the cone and rod photoreceptor mosaic, while the dark-field images show the hexagonal RPE cell mosaic with significant cross-talk from the photoreceptor mosaic. The scale bar is 100 μm across.

85

Figure 4.4 Comparison of SD-OCT data and an AOSLO dark-field image in a normal subject. The en face view shown in B was created by coarsely segmenting the SD-OCT signal from the choroid over the area highlighted in panel A over the depth range indicated by the blue band in C (yellow dots indicate manually placed inflection points). Panel D, shows the same retinal area as B as seen using AOSLO dark-field imaging. Scale bars are: A) 500 μm; C) 500 μm horizontal and 100 μm vertical; B) & D) 100 μm.

One of the most noticeable features in the AOSLO dark-field image presented in this

work are the bright dots with sizes comparable to that of the rod and cone

photoreceptors [137]. Confocal and dark-field images were collected simultaneously and

in perfect registration, in order to investigate the correspondence between the bright

spots in both images (see Figure 4.6). The resulting images indicate that not all bright

dots in the RPE images correspond to a cone photoreceptor, instead the locations

corresponding to cones could appear as both well-defined bright or dark spots.

Automated identification of 5616 cones and comparison against the image intensity at

the same locations in the RPE images show a poor cross-correlation coefficient (0.40)

(see Figure 4.5).

86

Figure 4.5 Reflectivity of photoreceptors in confocal images and locations of photoreceptors in dark-field images. There is poor correlation of bright photoreceptors to bright spots in RPE images.

Most RPE cells could be manually identified at center of fixation in all volunteers

(Figure 4.2), while at 10° temporal to fixation this is a much harder task, and often not

possible (Figure 4.3) due to significantly decreased contrast of the RPE mosaic.

Although a full cell count could not be achieved with confidence in any of the images,

automatic estimation of cell spacing using the radial average of the image power

spectrum showed good correspondence to manual measurements. The average nearest

neighbor distances across all 7 subjects of 10.7±0.9 μm (± standard deviation) at the

center of fixation, and 13.4±0.6 μm at 10° temporal to fixation, are in good agreement

with histologic [251] and in vivo studies [15]. A similar measurement derived from direct

cone photoreceptor counting yielded 3.4±04 μm and 9.4±1.1 μm, respectively.

0 50 100 150 200 2500

10

20

30

40

50

60

70

80

90

100

Photoreceptor intensity (a.u.)

RP

E inte

nsity (

a.u

.)

Correlation coefficient: 0.401

87

Figure 4.6 Confocal (photoreceptor) and dark-field (RPE) images collected simultaneously. Images are recorded in normal subject JC_0616 at approximately 0.8° from fixation. Panels A-B and C-F show cones recorded with 1 ADD pinhole and dark-field recorded with a 16 ADD pinhole and 1 ADD filament, respectively. Panels B and E show the cone and RPE cell centers marked with crosses and circles superimposed to the images in A and D, respectively. Panel C shows the dark-field image with cone centers and RPE centers superimposed, while panel F adds the cell borders, determined as Voronoi cells derived from the estimated cell centers. The scale bar is 10 μm across.

Dark-field filter and pinhole dimensions

In an attempt to attenuate the low spatial frequency pattern affecting the RPE dark-

field images, 4, 8, 12 and 16 ADD diameter pinholes were tested by collecting images in

the same subject at the same retinal location and focus. The resulting images (Figure.

4.7) show similar contrast and structures when using 8, 12 and 16 ADD pinholes.

Images collected using a 4 ADD diameter pinhole, however, were consistently worse in

that high spatial frequency structures make it more difficult to visualize the RPE mosaic.

The other key parameter of the dark-field filter is the diameter of the filament used to

block the confocal signal. Comparison of dark-field AOSLO images using a 1 and 3 ADD

88

filament consistently showed that both filament widths provide comparable visualization

of the RPE mosaic (see Figure. 4.8).

An additional experiment was performed to try to gain some understanding of the

spatial extent of the RPE dark-field signal, by recording long exposure PSF images at

the AOSLO detector’s image plane (subject JC_0616). Figure 4.9 shows one of those

images, the radial average and the radial sum (integrated along the azimuthal

coordinate). These curves are compared against a diffraction-limited PSF assuming a

single, infinitesimally thin retinal reflecting layer. This is not the case in practice, even at

the foveal center where the reflection from the nerve fiber layer is negligible relative to

contributions of the photoreceptor and RPE layers. Therefore, due to the at least two

distinct reflective layers (photoreceptors and RPE) a wider-than-theoretical PSF should

be expected, as the PSFs originating at each layer are slightly defocused relative to

each other. The experimental PSFs, collected at the foveal center (fixation) show, as

expected, a significant portion of the energy outside the extent of the central lobe of the

theoretical PSF (Airy disk). Interestingly, there are no obvious boundaries indicating the

extent of the confocal or the dark-field RPE signals, neither in the radial average, nor in

the radial sum of the experimental PSF.

89

Figure. 4.7 Dark-field AOSLO images of the RPE mosaic at fixation collected using a 1 ADD thick filament and different pinhole diameters. Normal subject JC_0616 A) 16, B) 12, C) 8 and D) 4 ADDs. The scale bar is 100 μm across.

90

Figure. 4.8 Dark-field AOSLO images of the RPE mosaic at fixation collected using a 16 ADD diameter pinhole and either 1 (A) or 3 ADD thick filament (B). The scale bar is 100 μm across.

Figure 4.9 Time-averaged retinal point-spread function (PSF). Recorded from research subject JC_0616, focused on the photoreceptor layer (Left; logarithmic color scale). The central and right panels show the radial average and integral, respectively, compared to that of a single retinal layer theoretical PSF (red solid lines).

Dark-field imaging with visible and near infrared light

RPE images were collected at multiple retinal locations using 680 and 790 nm

light to investigate the effect of wavelength on AOSLO dark-field contrast. The 680 nm

images appear slightly less sharp, with the low spatial frequency choroidal features

enhanced, as seen in Figure 4.10. Both these factors make the identification of individual

RPE more difficult when imaging with the shorter wavelength. This is likely related to the

91

increased absorption of shorter wavelength light within the retina as well as within the

hemoglobin rich choroid [5].

Figure 4.10 Dark-field AOSLO images of the RPE mosaic at the center of fixation with different imaging wavelengths. Using a 16 ADD diameter pinhole, 1 ADD thick filament and 790 nm (A) and 680nm (B) light. The scale bar is 100 μm across.

Apodization of entrance and/or exit pupils

In an attempt to further attenuate the cone photoreceptor reflectance signal in the

dark-field images, a centered 3 mm diameter circular block was used in either the

illumination or detection pupil planes [94]. The resulting images, shown in Figure 4.11,

show that apodization of the imaging pupil produced a comparable image, which was

slightly grainier due to the lower signal. Apodization of the illumination pupil, on the other

hand, completely degraded the view of the RPE mosaic.

Figure 4.11 Effect of pupil apodization on image quality and contrast at 10° temporal to fixation. Using 790 nm illumination, 16 ADD pinhole, 1 ADD filament (A) and with a centered 3

92

mm diameter circular block in the imaging (B) or the illumination paths (C). The scale bar is 100 μm across.

Validation of AOSLO dark-field RPE images

The AOSLO dark-field RPE imaging was validated using RPE autofluorescence

AOSLO imaging. Images were recorded at the same retinal locations with both

techniques in a subject with no eye disease. Ignoring the low spatial frequency

structures from the choroid and the shadows of the retinal vasculature, both techniques

show the edges of the RPE cells as brighter than the center (see Figure 4.12). This

might suggest that the scattering source is colocalized with the lipofuscin granules.

Figure 4.12 Comparison of RPE autofluorescence to dark-field RPE imaging. Normal subject AD_1025 at 3° superior and 9° temporal from fixation. A) dark-field image, B) autofluorescence images collected using 565 nm excitation and 625±45 nm emission. The scale bar is 100 μm across.

We sought further validation of the RPE dark-field images by collecting AOSLO

reflectance and dark-field images in one patient diagnosed with central serous

retinopathy. In this condition, the retina detaches from the RPE at some locations, thus

providing a direct view of the RPE mosaic without interference from the photoreceptor

layer, when imaging with a confocal AOSLO [167]. Images of the same retinal location

93

show 1:1 correspondence between AOSLO dark-field and confocal imaging where the

photoreceptor layer is clearly displaced from the RPE (Figure 4.13).

Figure 4.13 RPE images collected in a patient DW_1188 with central serous retinopathy. The SD-OCT image in panel A shows the 187 µm thick fluid collection that separates the retina from the RPE (below). En face AOSLO images of the area between the white arrows in A show RPE morphology in confocal mode (B), as well as in dark-field mode (C) at this location approximately 6° superior to fixation. The scale bar is 100 μm across.

4.4 Discussion

The modification of an AOSLO detection path by blocking the confocal signal and

collecting what has been referred to as the indirect or dark-field signal, shows a dramatic

attenuation of the light back-scattered by the photoreceptor inner and outer segments,

thus revealing the RPE mosaic. It appears that a large proportion of the light that

reaches the detector in this configuration has been multiply scattered and reached as

deep as the choroid. Furthermore, the good correspondence between SD-OCT and

dark-field AOSLO low spatial frequency intensity profiles suggests that the choroid might

be critical for the visualization of the RPE using dark-field and that this technique can be

also thought of as retro-illumination. The poorer visualization of the RPE mosaic when

using 680 nm light when compared with 790 nm, is consistent with the lower retinal and

choroidal penetration of shorter wavelengths [5].

Dark-field imaging with different pinhole diameter and filament thickness, as well as

PSF imaging, showed that there are no clear optimal dimensions for the dark-field mask

in terms of RPE visualization among those chosen for this experiment. Loosely, it

94

appears that the central block should be equal or larger than one Airy disk in diameter

and the pinhole larger than 4 Airy disks in diameter. This is consistent with the idea that

dark-field imaging requires blocking the reflected or single-scattered light that would be

found in the central Airy disk.

Although we have shown that visualizing the RPE mosaic is possible using the

proposed technique, identification of individual RPE cells is not always possible. In fact,

additional point-like structures, potentially due to residual photoreceptor signal make cell

identification very difficult in the rod-dominated areas outside the macula. Two additional

experiments (data not shown) were performed in an attempt to further attenuate any

potential non-confocal light back-scattered by the photoreceptors. First, we defocused

the illumination source, hoping to reduce the coupling of light onto the photoreceptor

inner/outer segments, as we intended when using the annular pupil masks mentioned

above. Second, a linear polarizer was placed in the pupil plane of the detector plane,

and images where collected at 10° temporal to fixation at the orientations that produce

dark-field RPE images with maximum intensity, then at 45 and 90° relative to it. Neither

approach improved the contrast of the RPE mosaic. In theory, the angle of the polarizer

that creates the brightest image should correspond to the acceptance of light directly

back-reflected from the photoreceptors (preserved polarization). By rotating the polarizer

away from this orientation, there should be a relative decrease in photoreceptor

contribution and a relative increase in contribution from multiple-scattered light from the

RPE(non-preserved polarization). In practice the polarizer did not improve RPE contrast.

This result suggests that the contrast of the RPE is not limited by the photoreceptors, as

diminishing their relative contribution does not improve RPE visualization.

95

The RPE images recorded with dark-field AOSLO have generally lower contrast than

those acquired using autofluorescence collected both in this (Figure 4.12) and previous

work [15, 93]. Dark-field imaging seems to reveal RPE structure, although further

investigation on the nature of the bright dots that form these images is required, before

this modality can be adopted for screening and/or diagnosing eye disease. Despite

showing poorer contrast than autofluorescence, dark-field RPE imaging is an appealing

avenue for studying eye disease at the microscopic scale non-invasively. When

compared to AOSLO autofluorescence imaging, dark-field AOSLO imaging requires one

less light source and imaging channel [15, 93], less complex data processing and

provides increased patient comfort and light safety with no concerns about potential

photochemical damage. It still remains to be studied in healthy and diseased eyes

whether these two techniques provide identical or complementary information. Current

RPE autoflorescence imaging is not tolerable at the fovea, where the comparison would

be best performed.

96

Figure 4.14 Montage of foveal photoreceptors and RPE cells from normal subject JC_0616. Images obtained with confocal and dark-field AOSLO respectively, as indicated in icons in top right corners. The scale bar is 100 μm across.

For reasons not yet fully understood, the images from the foveal center, an area

dominated by cone photoreceptors, provide a clearer view of the RPE cell boundaries,

with nearly all cells visible in some volunteers, as shown in Figure 4.14. Multiple

parameters were explored to try to improve the visualization of the RPE cell boundaries

including: pinhole diameter, obscuration filament thickness, illumination and imaging

pupil apodization, unmatched imaging and illumination focus, wavelength and

polarization. None of these offered a clear benefit and some even lead to poorer RPE

visualization.

Dark-field AOSLO could be a useful tool in the study of retinal disease

mechanisms. In particular, in macular degeneration, RPE cells often change morphology

before widespread degeneration and atrophy [43]. This technique could also be

translated into a clinical tool for screening, monitoring progression of disease, and

evaluation of therapeutic interventions.

97

Chapter 5 Non-confocal Split-detection Adaptive Optics Ophthalmoscopy

5.1 Introduction

Given the success of the offset-pinhole and dark-field detection techniques, our

group, led by fellow graduate student Yusufu Sulai sought to further enhance the

contrast of non-confocal imaging by capitalizing on the asymmetrical scatter generated

by retinal vessels [206]. As discussed in Chapter 2, Chui et al. previously demonstrated

that capturing the scatter generated in one perpendicular direction of a vessel improved

the visualization of wall structure [194]. Sulai et al. adopted this approach, and went one

step further, by incorporating two detectors to simultaneously capture both directions of

multiple-scatter simultaneously [206]. This detection method is analogous to the

microscopy technique known as split-detector [252], which derives contrast based on

phase differences between the two detectors. Sulai et al. implemented split-detector in

the AOSLO by placing a fold mirror at the image plane to equally divide the focal spot

between two detectors. The split-detector signal is calculated as the difference between

the detectors divided by their sum. We found that split-detector vascular imaging

provided improved contrast for flow and fine structure without the need to readjust for

varying vessel trajectories.

By focusing our illumination on layers above and below the vessels, we realized

that multiple-scattering is not a phenomenon specific or limited to the retinal vasculature,

in agreement with work described with SLO [5, 193]. After preliminary experiments using

the same detection method as [206], we decided it was important to rigorously

investigate AOSLO split-detector imaging. With assistance from Yusufu, I rebuilt the

detection arm of the AOSLO as described below, to simultaneously collect the spit-

detector and confocal signals. In only a short time we realized the potential of split-

98

detection for photoreceptor imaging, and we began imaging many subjects with

photoreceptor degeneration or disease. The first major project with split-detector

photoreceptor imaging was a proof of concept study, including normal subjects and

patients with an inherited form of colorblindness known as achromatopsia (ACHM). The

work performed below was a unique opportunity to work with Dr. Christine Curcio, a

world expert in retinal anatomy and histology. As a part of this project, we include

histologic measurements of inner segment size not previously published by Dr. Curcio.

Recently, there have been multiple successful applications of genetic [253-256]

and cellular replacement [257, 258] therapies to animal models of inherited blindness.

Early human trials have also shown positive results [259], demonstrating the promise of

gene therapy for a wide range of human photoreceptor degenerations. These

interventions aim to rescue existing dysfunctional photoreceptors using gene therapy, or

restore vision by transplanting functional photoreceptors or precursor cells. A critical

knowledge gap in retinal gene therapy efforts surrounds the degree of retained

photoreceptor structure given a genotype and penetrance. Therefore, the lack of an

objective method to directly assess the residual photoreceptor population in patients with

retinal degenerations presents a roadblock for predicting the success of such therapies

in vivo, especially in humans [260].

Adaptive optics (AO) retinal imaging enables direct visualization of rod and cone

structure [8, 137]. Ophthalmoscopes enhanced with AO can provide images with

resolution near the limit imposed by the eye's pupil diameter and axial length, by

correcting for the monochromatic aberrations induced by the cornea and lens [8]. The

contrast in images of the photoreceptor mosaic depends on the imaging modality and

the optical properties of the photoreceptors and their surroundings, though whether

99

imaged with an AO fundus camera, AO optical coherence tomography (AOOCT) or a

confocal AO scanning light ophthalmoscope (AOSLO), individual healthy photoreceptors

appear as bright spots. This is explained by the strong directional coupling

(waveguiding) of light by the photoreceptor inner segment into the outer segment [136],

the higher refractive index relative to its surrounding and the backscattering that takes

place at both ends of the photoreceptor outer segment [261]. Visualization of

photoreceptors with AO ophthalmoscopy is dependent on intact outer segment

morphology, thus, despite the high-resolution of AO retinal imaging devices, the

disambiguation of residual cone structure in patients with retinal degenerations remains

elusive.

Here, we propose and demonstrate a non-confocal variation of a scanning

microscopy technique, known as split-detection [252, 262, 263], to visualize the

photoreceptor inner segment mosaic using an AOSLO [75]. In this method, a reflective

mask with a transparent annulus is placed in the image plane where typically a circular

pinhole is placed for confocal detection [11]. This mask reflects the confocal signal to a

first light detector and transmits the multiple-scattered light, which is then captured by

two incoherent optical detectors which collect the light in the left and right semi-annuli

(Figure 5.1 A). The split-detector (as we will refer to it from here on) signal is then

calculated as the difference between the signals from non-confocal detectors, divided by

their sum. In this arrangement, the waveguided light from the photoreceptor outer

segment (confocal) and the multiple-scattered light from the inner segment (split-

detector) can be visualized simultaneously and in perfect spatial registration (Figure 5.1

B,C). We used this imaging approach to directly examine residual cone structure in

patients with ACHM, revealing a robust but variable remnant cone population. Despite

100

substantial disruption of outer retinal structure in ACHM clinical images, cone inner

segment structure was observed at the foveal center in the split-detector images. The

ability to directly ascertain cone structure in these patients represents an important first

step towards being able to predict the therapeutic potential for gene therapy efforts on

an individualized basis.

5.2 Methods

Human Subjects

Research procedures followed the tenets of the Declaration of Helsinki and informed

written consent was obtained from all subjects after explanation of the nature and

possible consequences of the study. The study protocol was approved by the

institutional review board of the Medical College of Wisconsin. Patients were referred by

their physicians, or self-referred for advertised studies.

Axial length measurements were obtained on all subjects (Zeiss IOL Master; Carl

Zeiss Meditec, Dublin, CA, USA) in order to determine the scale (in micrometers per

pixel) of each retinal image. Axial length was assumed to be constant across all

eccentricities imaged in this study (0-6mm, ~20º), as it typically varies less than 2.0% in

this range.[264, 265] All subjects were imaged without spectacles or trial lenses in order

to avoid additional scaling errors. Prior to all retinal imaging, each eye was dilated and

cycloplegia was induced through topical application of phenylephrine hydrochloride

(2.5%) and tropicamide (1%).

Two visually normal volunteers and four individuals with genetically-confirmed ACHM

were recruited for imaging.

101

102

Figure 5.1 Schematic representations of split-detector implementation and images. A, AOSLO schematic with an annular reflective mirror (inset) to separate the confocal from the multiple-scattered light, which is then equally divided (split) between two light detectors. The confocal signal is directly recorded in Detector 1, while the split-detector signal is the result of the subtraction of the intensities recorded in Detector 2 from Detector 3 divided by their sum at every pixel. B, Representative split-detector image of the photoreceptor inner segment mosaic acquired at 10° of visual angle from fixation in a normal volunteer, showing cones and an inability to resolve individual rods. C, Simultaneously recorded confocal image showing cones with varying reflectivity surrounded by rods. Scale bar, 25 µm. D, Photoreceptor schematic shows the likely origin of the light back reflections.

Genetic Testing

All four achromatopsia subjects had previously documented mutations in either

CNGA3 or CNGB3 (see Table 5.2 for a list of mutations). Testing was performed at

either The John and Marcia Carver Nonprofit Genetic Testing Laboratory (University of

Iowa, IA, USA) or Casey Eye Institute Molecular Diagnostics Laboratory (Oregon Health

and Sciences University, OR, USA).

Optical Coherence Tomography

In all achromatopsia subjects, line scans were acquired with a Bioptigen

(Bioptigen SD-OCT; Bioptigen, Research Triangle Park, NC, USA) or Spectralis SD-

OCT (Heidelberg Engineering, Heidelberg, Germany). To improve signal to noise ratio,

multiple line scans (11 to 22) were registered and averaged. Foveal structure was

evaluated for ellipsoid zone integrity as previously described.[154] The lateral scale of

each image was estimated using the patient’s axial length data.

Adaptive Optics Retinal Imaging

A custom AOSLO was modified for this study [75] to capture non-confocal light as

demonstrated by Webb et al. [4] in a split-detection configuration [252, 262, 263]. The

detection path was modified by replacing the confocal aperture in the image plane in

front of the detector with a reflective annular mask. The central disk of the mask was

103

sized to reflect the central 2 Airy disk diameters (ADDs) of the focal spot towards

detector 1 (confocal channel), and to transmit the remaining light up to 20 ADDs (Figure

5.1). An afocal telescope relayed the plane of the mask onto a second conjugate image

plane where a flat mirror with a vertical straight edge and minimal bevel divided (split)

the light annulus between two additional light detectors (Figure 5.1). The non-confocal

split-detection image intensity was then calculated as the difference of the detector

signals divided by their sum. A multiplicative gain factor and an additive offset are used

to stretch the contrast of each image for optimal display in computer monitors with 256

gray levels, while avoiding saturation. Because the light reaching the split detectors is

not confocal, the detected signal cannot be interpreted through geometrical or physical

optics without considering multiple scattering. Although a quantitative description of the

source of contrast for this imaging method is still lacking, the resulting images resemble

those that are seen in phase-gradient microscopy techniques such as differential

interference contrast (DIC) (Figure 5.2).

The epi-illumination and the use of two detectors with on-axis point illumination

as presented here is somewhat reciprocal of the oblique back-illumination method

recently proposed by Ford et al. [266], with the advantage that both symmetrically

opposed detectors are recorded simultaneously, thus enabling the visualization of

dynamic events such as blood flow [206].

The imaging light source was a 790 nm super-luminescent diode (SLD) (Superlum,

Carrigtwohill, Co. Cork, Ireland) and the wavefront sensing light source was an 850 nm

SLD (Superlum). Incident powers for these light sources were 70 and 17 μW

respectively, measured at the cornea. The combined light exposure was kept below 5

times below the maximum permissible exposure set forth by the ANSI Z136.1-2007 [209,

104

210]. The output of each Hamamatsu H7422-50 photomultiplier module (Hamamatsu

Corporation, Bridgewater, NJ, USA) light detector was amplified by a Femto HCA-10M-

100K high speed current amplifier, inverted using custom electronics and digitized using

a eA Helios framegrabber (Matrox Electronic Systems Ltd., Dorval, Quebec, Canada).

Image sequences were collected at the center of the fovea and from 1 to 20° visual

angle lateral (temporal) to fixation using a 1.0 and 1.50° square field of view. Image

sequences of 150 frames (confocal and split-detector) were collected and processed to

remove the warp due to the sinusoidal motion of the horizontal scanner. Those images

were then registered, and the 40 images with highest normalized cross-correlation

relative to a user-selected reference frame were averaged to improve signal-to-noise

ratio [133]. Because the image sequences were collected in synchrony and processed in

exactly the same manner, the resulting averaged images are in perfect registration [133].

AOSLO Image Analysis

Using the Gullstrand 2 schematic eye, the predicted 291 µm per degree of visual

angle [207] was scaled linearly by the subject’s axial length to determine the scale of

AOSLO images. One examiner manually marked contiguous mosaics of foveal cones in

split-detector AOSLO images from normal subject AD_1225 to estimate the minimum

cell size resolved with this technique. Rods were similarly marked in confocal images

from AD_1225 (10° temporal) and achromat JC_10069 (parafoveal and 5° temporal) to

compare rod size estimates with the resolved foveal cone size. Coordinates of marked

photoreceptors were analyzed with Delaunay triangulation using custom MATLAB

software (Mathworks, Natick, MA, USA) to determine the average nearest neighbor

distance, which can be interpreted as an estimate of the cell size when considering a

contiguous mosaic. For calculation of inner segment diameter three observers fit circles

105

of varying diameter to best match the size of inner segments in split-detector images at

multiple eccentricities in two normal volunteers AD_1225 and AD_1207. Each observer

fit 10-17 separate cones per image, resulting in 30-51 measured diameters per image.

For coarse theoretical calculation of minimum angle of resolution (MAR) in

achromatopsia subjects, cone photoreceptors from the split-detector images within the

central 1° of the anatomical fovea were manually marked. The average intercone

distance (ICD) over a 36.5 × 36.5 µm sliding window was calculated with custom

MATLAB software (Mathworks), then converted to the Nyquist cone sampling in arc

minutes as described by Rossi et al.[98] The Nyquist cone sampling was assumed to be

the best possible MAR, as found in the previous work in normal subjects [98].

Tissue collection and preparation

Eyes obtained from donors within 3 hr of death were preserved by immersion in

4% paraformaldehyde and 0.5% glutaraldehyde in 0.1M phosphate buffer after the

cornea and lens had been removed. Retinas were prepared as unstained whole mounts

[267]. In brief, the retina was dissected free from the pigment epithelium, flattened on a

slide, rinsed in water, and cleared under a coverslip overnight in dimethyl sulfoxide

(DMSO). Excess DMSO was blotted, 100% glycerol was applied to the tissue, and a

coverslip was mounted and sealed with nail polish. A series of similarly prepared retinas

underwent a slight expansion in tissue area, and inner segment diameters were not

corrected for these small changes. Tissue was viewed with Nikon Optiphot (Nikon,

Tokyo, Japan) with a combination of differential interference contrast microscopy and

video (NDIC-video).

Data were obtained from five donors. Peripheral retina was analyzed in four

donors age 27-35 year (H2-H5 [33]). The fovea was analyzed in two donors (35 year old

106

male, H5[33]; 68 year old male, eye #18 [268]). The foveal centers of these eyes had an

intact external limiting membrane, optically clear tissue at all levels of focus through

cones, and similar peak cone density (181,800 cones/mm2 in 35 year old male and

170,100 cones/mm2 in 68 year old male).

Ex vivo analysis

Cone inner segment diameters in the periphery (> 1 mm) of the 4 young retinas

were determined by circle-fitting at a focusing depth where cones were optically

separate. At eccentricities exceeding 1 mm, individual cone inner segments are

surrounded by a ring of rods and are circular in profile. The observer centered a

computer-generated circle on a NDIC-video image of a cone inner segment and

adjusted its size to match the cone. Thirty cones were measured for each location, and

means and standard deviations were computed. The mean diameter for the 30 cones

obtained by circle fitting was within 3% of the mean of the same cones as measured by

outline tracing and was obtained in 40% shorter time.

Cone inner segment diameters in the foveas (<1mm) of two eyes were calculated

from component area densities (AA) of inner segments measured with point-counting

stereology [269] divided by the local density of cells, to produce an average cross-

sectional area and equivalent diameter for an individual photoreceptor. The relative area

of structures in a containing reference area can be estimated by counting points in a grid

overlying the component and the reference area. Thus, AA = Pi / Pref , where Pi is the

number of points overlying a specific tissue component and Pref is the total number of

points in the reference area, containing all components. A custom program

superimposed a square grid on the NDIC-video image of the tissue, presented one grid

intersection at a time for scoring, and enabled the observer to press a key indicating

107

whether the point was over a cone, rod, or extra-receptoral space between the inner

segments. The grid used was a square lattice whose spacing between lines was

determined empirically to produce relative standard errors of 5% or less for AA of cone

inner segments, the smallest of the 3 components over this eccentricity range, and

errors of 2-3% for rod inner segments and extra-receptoral space. A grid spacing of

0.0037 µm provided 100 points in a square window. A single window was scored for

each location in each of two foveas, including the foveal center and at 50 µm intervals to

an eccentricity of 400 µm on 4 cardinal meridians.

5.3 Results

Split-detector imaging reveals cone photoreceptor inner segment mosaic.

The photoreceptor mosaic was imaged at multiple retinal eccentricities in two

subjects without known eye disease. In normal subjects the confocal images (Figure 5.3

A-D), show bright spots that correspond to light waveguided by intact photoreceptors

[136]. A comparison between the confocal and split-detector images reveals a 1:1

correspondence between the bright spot in the confocal image and the mound-like

structures in the split-detector image (Figure 5.3). The split-detector inner segment

images (Figure 5.1 C and Figure 5.3 E-H) strongly resemble differential interference

contrast imaging of ex vivo retinal preparations (Figure 5.2).

108

Figure 5.2 Side by side comparison of ex vivo [33] and in vivo imaging of the human photoreceptor inner segment mosaic at 5° temporal from fixation in different eyes. Cone inner segments are clearly resolved in (A) ex vivo and (B) in vivo, however, the resolution of the histologic images is superior due to the larger numerical aperture of the oil immersion microscope objective compared to that of the human eye (1.4 vs. 0.2). For this reason, only a few rods can be resolved in the AOSLO image (arrows). Scale bar, 10 µm.

109

Figure 5.3 Confocal and split-detector imaging in a normal volunteer at 1, 5, 10, 20° temporal to fixation. A-D, Confocal Images. E-H, Split-detector images. The figure illustrates how cone photoreceptors increase in diameter with increasing eccentricity from the fovea. The increasing distance between cone inner segments is due to increasing density of rod photoreceptors, which are not resolved with split-detector imaging in most normal volunteers. Scale bar, 50 µm.

110

Figure 5.4 Plot of average cone inner segment diameter from the foveal center along temporal meridian. Ex vivo measurements are averages of 2 retinas (< 1.4°) or 4 retinas (> 3.4°). Squares indicate ex vivo measurements, and gray shading reflects the standard deviation across retinas. Within 1.4° of the foveal center, measurements were averaged across all four meridians. Also shown are data from two normal subjects measured in vivo with non-confocal split-detection AOSLO in the temporal direction. The in vivo data is shown as triangles with error bars of one standard deviation.

Measurements of cone structure from in vivo split-detector images in two normal

subjects showed diameters ranging from 3.0 ± 0.4 to 8.2 ± 0.6 µm (mean ± standard

deviation) from 1 to 20° temporal to fixation. These measurements are consistent with ex

vivo measurements at comparable retinal eccentricities, ranging from 4.2 to 8.3 µm

(Figure 5.4), as well as previous histologic reports in non-human primates [270]. The full

range of ex vivo inner segment diameters measured between 0 and 41° are shown in

Table 5.3. Taken together, these findings support the interpretation that it is the cone

inner segment, and not the outer segment, visualized by split-detector AOSLO. It is

important to note that most rod and some foveal cone photoreceptors seen in the

confocal images cannot be resolved in the corresponding split-detector images,

suggesting a resolution limit determined either by the contrast mechanism itself or the

photoreceptor refractive index profile, rather than the quality of the AO correction.

111

Figure 5.5 Spectral domain optical coherence tomography (SD-OCT) appearance of the subjects included in this study. The top three scans show ellipsoid zone disruption (JC_10069, KS_10088 and JC_10028), while the bottom shows a hypo-reflective zone (JC_10089). Arrows indicate where AO images in corresponding figure panels were recorded. All scans show foveal hypoplasia. Scale bar is 200 µm.

112

Determining the degree of retained cone photoreceptor structure in

achromatopsia.

Four patients with achromatopsia caused by mutations in the A3 or B3 subunits

of cone photoreceptor cyclic nucleotide-gated (CNG) channels (Table 5.2) were

recruited to quantify their retained cone structure. OCT cross sectional images shown in

Figure 5.5 reveal variable central ellipsoid zone (EZ) disruption in all four subjects, as

has been reported in many patients with achromatopsia [154].

Figure 5.6 Confocal and split-detector AOSLO images of the photoreceptor mosaic in a patient with achromatopsia at 0.4 and 2° from fixation. A, D, confocal images; B, E, split-detector images; and C, F, color merged images, where the confocal image is displayed in orange, and split-detector image is shown in blue. Note the 1:1 correspondence between the dark cones in the confocal images and the inner segments in the split-detector images, highlighted by the pseudocolor images (C,F). Scale bar, 50 µm.

Confocal AOSLO images in one of these subjects (JC_10069) near fixation and in the

parafovea show retained waveguiding rods, with little to no reflectivity from cones

113

(Figure 5.6 A,D) precluding identification of cone photoreceptors at these locations. The

simultaneously recorded split-detector images (Figure 5.6 B,E) resolve both rod and

cone inner segments. As shown best in the pseudo-color merged images (Figure 5.6

C,F), there is 1:1 correspondence between the dark circular structures in the confocal

image and the mound-like structures in the split-detector image. This indicates that there

can be substantial retained cone inner segment structure in patients with achromatopsia,

though the altered reflectivity of the residual cones indicates morphological disruption of

the outer segments and/or disturbance of the refractive indices of the cells. In this

patient, the rods are visible in the split-detector channel, unlike in normal subjects, due

to the fact that they are enlarged (see Table 5.1).

Figure 5.7 Assessing the foveal photoreceptor mosaic in achromatopsia. Confocal (top) and split-detector (bottom) AOSLO images in patient A,E, JC_10069 B,F, KS_10088 C,G, JC_10028 and D,H, JC_10089 illustrate the substantial variability of retained cone structure at the fovea between individuals and genotype. The confocal images (A-D) at these locations show ambiguous photoreceptor reflectivity, while the split-detector images reveal the foveal cone inner segments. Scale bar, 25 µm.

114

Predicted visual acuity

In order to estimate the best possible visual acuity recovery with gene therapy,

assuming the limiting factor is photoreceptor spatial sampling, we measured the

maximum cone density in four subjects with achromatopsia. Images from within 1° of the

center of the anatomic fovea in all four subjects with achromatopsia are shown in Figure

5.7, demonstrating substantial variability in retained cone numbers across individuals.

Retained cone photoreceptors were counted in these images and spatial sampling

estimated based on cone spacing as previously described [98]. The spacing of retained

cone photoreceptors at these locations is approximately two times that of normal [98],

though it varied between the four subjects. Assuming the normal connectivity between

foveal cones and midget ganglion cells is preserved,[271] this predicts an increase in

MAR by a factor of two to five compared to normal (Table 5.1). These results offer a

promising perspective on the maximum therapeutic benefit in emerging achromatopsia

gene therapy trials.

Table 5.1 Calculation of visual sampling based on residual cone photoreceptor spacing. Assuming a best-case scenario where the entire retinal and cortical circuitry is either intact in achromatopsia or at least sufficient plasticity remains, the foveal acuity should be limited by the cone spacing. Using the calculation proposed by Rossi et al. [98], and the measured center-to-center intercone distance (ICD) over a 36.5 × 36.5 µm window from split-detector images, the achromatopsia subjects in this study show potential visual sampling that is between 1.6 and 5.3 times worse than the normal subjects in Rossi’s study.

Subject ID # ICD* ± SD (µm) MAR (arcmin)

JC_10069 4.73 ± 0.60 0.88 KS_10088 7.59 ± 1.64 1.29 JC_10028 7.74 ± 0.95 1.52 JC_10089 14.20 ± 2.47 2.79

*Cone photoreceptor center to center intercone distance (ICD) Minimum angle of resolution

5.4 Discussion

Split-detector imaging provides a robust method to visualize cone inner segment

structure in a manner that appears to be independent of the integrity of the outer

115

segment. The appearance and size of the inner segments are well matched to those

acquired with histology. Although the inter-observer repeatability of inner segment

diameter measurement is relatively poor with the software currently available to us, the

inner segment size could be used as a future biomarker, assuming adequate image

contrast. Conventional AO (confocal, flood illuminated and OCT) imaging relies on a

waveguided reflection from an intact, correctly oriented outer segment to visualize cones

[136]. However, outer segment structure degenerates in a variety of retinal diseases,

including retinitis pigmentosa [272-274], age-related macular degeneration [275, 276]

and choroideremia [277]. Quantification of cone structure in AO retinal images had until

now been based on detecting visible waveguiding cones, with dark areas in the mosaic

often interpreted as devoid of photoreceptors. Using the split-detector technique in

patients with achromatopsia, we showed that cone inner segments occupied the majority

of the dark gaps in the confocal AOSLO photoreceptor images. This provides the first

direct in vivo evidence of substantial remnant cone structure in patients with

achromatopsia, and demonstrates that analyses based only on confocal/bright field

signals will underestimate the degree of residual cone structure. A similar “dark cone”

appearance has been described in a number of other conditions [90, 173, 182, 186],

suggesting that split-detector AOSLO imaging would provide a more direct quantification

of cone structure in these patients as well.

Previous studies of achromatopsia have measured the reflectivity of the EZ and

the thickness of the outer nuclear layer with OCT [154, 278, 279] to assess the

remaining cone photoreceptor population. Neither of these analyses can distinguish

between contributions of rods and cones, due to insufficient transverse image resolution.

More recently, parafoveal cone structure has been estimated with AOSLO in

116

achromatopsia [153] and blue-cone monochromacy [280] by using rings of rods to

facilitate counting of presumed non-waveguiding cones. However, this is not possible at

the foveola where there is a contiguous dark patch without rods, and in other conditions

in which rods also degenerate (such as retinitis pigmentosa), the ability to use intact rods

to infer the presence of a perifoveal cone is limited. Moreover, in other retinal

degenerations, the RPE can sometimes be resolved [167] and often contains structures

with reflectance profiles similar to small photoreceptors. Disambiguating RPE from

photoreceptor structure in these cases is difficult, if not impossible, using only confocal

AOSLO imaging. Split-detector imaging should be invaluable in elucidating cone

structure in these more complex retinal diseases.

The direct visualization of cone structure in achromatopsia afforded through the

use of split-detector AOSLO stands to benefit emerging gene therapy efforts. Prior to

intervention these images could be used to predict the anatomic upper limit of visual

recovery that may change with genotype and age [279]. In addition, knowledge of the

degree of residual foveal cone structure could inform the estimation of the relative risk to

benefit ratio on an individualized basis, and one could actually select patients for

inclusion based on the amount of remnant cone structure. Beyond achromatopsia, the

new split-detector AOSLO technique could positively impact the design and recruitment

for clinical trials for other retinal degenerations involving damage to the photoreceptor

outer segment.

117

Table 5.2 Genotypic and demographic data of subjects with achromatopsia. The far right column indicates the best corrected visual acuity (BCVA) measured on an Early Treatment Diabetic Retinopathy Study (ETDRS) chart at 4 meters in the eye imaged with AOSLO (OD refers to the right eye). *A variation of unknown significance was also identified in PDE6C (p.Lys585 Asn:c.1755G>T).

Subject ID

Age

Gene Allele 1 Allele 2 BCVA Ocular axial length (mm)

JC_10069

18 CNGA3, heterozygous

c.847C>T-p.Arg283Trp

c.542A>G-p.Tyr181Cys

OD 20/125

23.11

JC_10028

13 CNGB3, homozygous

c.1148delC-p.Thr383fs

c.1148delC-p.Thr383fs

OD 20/100

21.78

KS_10088

64 CNGA3, heterozygous

c.450-1G>A c.1557G>A-p.Met519Ile

OD 20/120

25.20

JC_10089

40 CNGB3, homozygous

c.1148delC-p.Thr383fs*

c.1148delC-p.Thr383fs

OS 20/125

21.67

118

Table 5.3 Ex vivo measurements of inner segment diameter between 0 and 12mm. Inner segment diameters measured between 0 and 0.39mm (2 retinas) were pooled across the superior, inferior, nasal and temporal meridians due to symmetry. Measurements from 1.0 to 11.94mm are averaged across 4 retinas.

Inner segment diameter (mean ± SD, µm)

Eccentricity (mm)

Eccentricity (degrees)

Nasal Temporal

0.00 0.0 2.23 ± 0.11 2.23 ± 0.11

0.05 0.2 2.48 ± 0.04 2.48 ± 0.04

0.10 0.3 2.99 ± 0.03 2.99 ± 0.03

0.15 0.5 3.38 ± 0.14 3.38 ± 0.14

0.20 0.7 3.75 ± 0.25 3.75 ± 0.25

0.25 0.8 4.00 ± 0.23 4.00 ± 0.23

0.29 1.0 4.16 ± 0.22 4.16 ± 0.22

0.39 1.3 4.49 ± 0.19 4.49 ± 0.19

1.00 3.4 6.27 ± 0.71 6.84 ± 0.51

1.99 6.8 6.84 ± 0.85 7.33 ± 0.49

2.99 10.3 7.61 ± 0.45 7.54 ± 0.6

3.98 13.7 * 7.88 ± 0.69

4.98 17.1 7.84 ± 0.65 8.10 ± 0.73

5.97 20.5 7.81 ± 0.72 8.26 ± 0.61

6.97 23.9 7.78 ± 0.56 8.38 ± 0.55

7.96 27.4 7.86 ± 0.44 8.30 ± 0.58

8.96 30.8 7.98 ± 0.33 8.42 ± 0.57

9.95 34.2 8.09 ± 0.24 8.51 ± 0.59

10.95 37.6 8.21 ± 0.15 8.57 ± 0.63

11.94 41.0 8.32 ± 0.11 8.55 ± 0.57

*The optic nerve head precluded measurement along the nasal meridian at this eccentricity. Table 5.4 Rod photoreceptor size estimate using nearest neighbor analysis in achromatopsia vs. normal. In achromat JC_10069, rod photoreceptors across the retina are significantly enlarged compared to both normal rods, as well as normal foveal cones (Kruskal-Wallis test, Dunn’s multiple comparisons test of each group to normal cones at the fovea, see p-values on right column). This substantial difference in size (~1 µm or 40%) makes rods resolvable in the achromatopsia subjects, and not in normal subjects in split-detection.

Retinal location Subject NN*± SD (µm) # cells p

Cones – fovea Normal 2.63 ± 0.40 123 - Rods – 10° eccentricity Normal 2.31 ± 0.32 70 p < 0.001 Rods – fovea Achromat 3.34 ± 0.45 145 p < 0.001 Rods – 5° eccentricity Achromat 3.19 ± 0.51 253 p < 0.001

*Photoreceptor nearest neighbour distance.

119

Chapter 6 Visualization of Photoreceptor Structure within Ellipsoid Zone Lesions Imaged with Optical Coherence Tomography

6.1 Introduction

Optical coherence tomography (OCT) is rapidly replacing color fundus

photography as the most common retinal imaging modality [7]. En face OCT has

emerged as an important tool for the evaluation of layer specific retinal pathology,

enabling visualization of lamina cribrosa [281, 282], microvasculature [283-285], RPE

detachment [286-288], subretinal fluid [286, 289, 290], cystoid macular edema [291],

outer retinal tubulations [292, 293] and other retinal structures. One of the most

promising applications of en face OCT is the measurement of ellipsoid zone (EZ)

disruption, to provide insight into photoreceptor structure. To date, en face OCT has

been utilized to evaluate photoreceptor structure in macular telangiectasia (MacTel) type

2 [294], diabetic retinopathy [291], macular hole [291, 295] macular degeneration [291,

296-298] and ocular trauma [199]. Quantification of EZ disruption with en face OCT was

recently approved as the primary outcome measure for a therapeutic clinical trial in

MacTel [299], yet little is known about the in vivo cellular anatomy within and at the

margins of these disruptions.

One of the challenges in using en face OCT to evaluate photoreceptor structure

is the limited lateral resolution and poor transverse spatial sampling of commercially

available OCT systems. By correcting the monochromatic aberrations of the eye, AO

ophthalmoscopy provides the opportunity to visualize individual rod and cone

photoreceptors in normal and diseased retinas [70, 137]. The spatial correlation of

disruptions as per confocal AOSLO imaging and en face OCT in closed globe blunt

ocular trauma (cg-BOT) revealed photoreceptor cell disruption in the vicinity of EZ

120

disruptions in OCT images [199]. Despite increased resolution, confocal AOSLO is also

limited, as the technique does not visualize photoreceptors with abnormal outer segment

morphology or alignment [34]. Since many retinal diseases are known to cause loss of

the photoreceptor outer segment before loss of the remainder of the cell [272-277], this

limitation could prevent the accurate interpretation of the photoreceptor mosaic in the

vicinity of such disruptions.

AOSLO split-detector imaging [34, 206] has recently demonstrated the non-

invasive visualization of the cone photoreceptor inner segment mosaic, regardless of the

status of the corresponding outer segment [34]. Here, we apply split-detector AOSLO in

five subjects with wide ranging retinal pathology to evaluate photoreceptor anatomy

within and around EZ disruptions identified with OCT, to determine their anatomical

correlates. The data demonstrate that there can be significant residual photoreceptor

structure within EZ disruptions identified with en face OCT, and that the borders of the

disruption can be more clearly demarcated with the split-detector AOSLO technique.

6.2 Methods

Participants

Research procedures followed the tenets of the Declaration of Helsinki and

informed written consent was obtained from all subjects after explanation of the nature

and possible consequences of the study. The study protocol was approved by the

institutional review board at the Medical College of Wisconsin.

Axial length measurements were obtained on all subjects (Zeiss IOL Master; Carl

Zeiss Meditec, Dublin, CA, USA) in order to determine the scale of AOSLO and OCT

images [34]. Prior to all retinal imaging, the eyes to be imaged were dilated and

121

cycloplegia was induced through topical application of phenylephrine hydrochloride

(2.5%) and tropicamide (1%).

Five subjects with clinical diagnoses of closed-globe blunt ocular trauma (cg-

BOT; n = 2), macular telangiectasia type 2 (MacTel; n = 1), blue cone monochromacy

(BCM; n = 1) or cone-rod dystrophy (CRD; n = 1) were identified for inclusion in the

study. Clinical details are summarized in Table 6.1.

Table 6.1 Clinical characteristics of patients included in this study.

Subject Eye BCVA* Age Diagnosis Details

DH_1192 OS 20/200 43 Cone-rod dystrophy

Previously reported causative mutation (p.R838H:c.2513G>A) [300] identified in GUCY2D gene.

DW_10105 OD 20/25 54 Macular telangiectasia type 2

Stage 2 [301].

MP_10097 OD 20/50 43 Blue-cone monochromacy

Previously reported causative mutation (p.C203R; c.607T>C)[143] identified in OPN1MW and OPN1LW genes.

SR_10139 OS 20/20 55 Closed-globe blunt ocular trauma

Motor vehicle accident approximately 35 years prior to imaging.

WW_0923 OS 20/20 22 Closed-globe blunt ocular trauma

Motor vehicle accident approximately 22 months prior to imaging. Patient previously described at 7 months post trauma [199].

*Best corrected visual acuity

En face optical coherence tomography

Spectral domain optical coherence tomography (Bioptigen, Research Triangle

Park, NC, USA) line scans and dense cube scans nominally covering 3 3 mm (400 A-

scans/B-scan; 400 B-scans/volume) were performed in the area of planned AOSLO

imaging. Using custom designed Java (Oracle, Redwood City, CA) software, we derived

en face summed volume projection images from the SD-OCT volumes of all

subjects. These images were created to emphasize structures within the EZ (and in one

case, also the interdigitation zone, IZ). The macular volume was exported from the

122

machine as an “.avi” and was uncompressed using the Xuggler (Xuggle Inc., San

Francisco, CA) library. Each of the 400 B-scans was read from the volume and marked

with a flat contour, which was initially a spline fit to three points along the B-scan. The

contour for each B-scan was manually modified by the user by both adding points to the

contour and adjusting the axial position of each of the points. The number of points and

the axial position of the points were different for each B-scan, based on the varying

morphology of the EZ (See Figure 6.1). At each axial position along the B-scan, the

pixels within the contour were averaged, resulting in a 1D average array of gray values

along each B-scan. The final en face image was created by combining the 1D average

array from each B-scan in the volume (See Figure 6.1). All OCT images are displayed

on logarithmic scale. All final figures were upsampled to 600 ppi for optimal display using

bicubic interpolation in Adobe Photoshop (Adobe Photoshop; Adobe Systems Inc.,

Mountain View, CA). Naming of the outer retinal bands is based on the recent study by

Staurenghi et al.[302], as there is considerable controversy regarding the naming of

these reflective bands. Here, the band referred to as the EZ is the hyperreflective band

that lies just posterior to the ELM, and this has been referred to previously as the inner

segment / outer segment junction (IS/OS). The band referred to as the IZ is the

hyperreflective band that lies just posterior to the EZ, and this has been referred to

previously as the cone outer segment tip band (COST).

123

Figure 6.1 Schematic of the generation of en face summed value projection OCT images. The layer of interest is manually segmented in every B-scan of the volume with a contour, illustrated by the blue bands. At each axial position along the B-scan, the pixels within the contour are averaged. The final en face image was created by combining the average array from each contour in the volume. Scale bars = 200 µm.

Adaptive optics imaging

AOSLO confocal and non-confocal imaging was performed as described in

Chapter 5. Multiple registered images were manually aligned using Adobe Photoshop to

ensure AOSLO imaging encompassed the entire EZ disruption, and then were manually

aligned to en face OCT using blood vessel shadows or other anatomical landmarks.

Confocal and split-detector images are recorded simultaneously at the same

wavelength, and thus are in perfect spatial register. While all AOSLO images are

displayed here on linear scale, we provide corresponding log display versions (see

Figure 6.8). All final figures were upsampled to 600 ppi for optimal display using bicubic

interpolation in Adobe Photoshop.

124

6.3 Results

The EZ disruptions visualized with B-scan and en face OCT varied from mottling

in cg-BOT and BCM (SR_10139 and MP_10097) to focal discontinuities in CRD, cg-BOT

and MacTel (DH_1192, WW_0923, DW_10105). Confocal AOSLO images from all

subjects showed cone and/or rod photoreceptor mosaic disruption. Split-detector

AOSLO revealed enlarged photoreceptor inner segments within and on the margins of

all EZ disruptions. Of note, the retinal sampling of the en face projections is

approximately 7.5 μm per pixel, compared to approximately 0.5 μm per pixel for AOSLO

images.

Case 1:

Subject DH_1192 (43 year-old female) was referred by an outside physician for

decreasing visual acuity and color vision abnormalities. Best-corrected visual acuity at

the time of research imaging for this study was 20/200 OS, and she had pronounced

color vision impairment on the AOHRR pseudoisochromatic plates (D value > 0.088).

She had a family history of progressive visual impairment beginning with her great-

grandmother, including her mother, two out of four siblings and her son. Genetic testing

revealed a substitution p.R838H; c.2513G>A in the GUCY2D gene, a mutation reported

to cause autosomal dominant cone-rod dystrophy [300].

125

Figure 6.2 Multimodal imaging in cone-rod dystrophy subject DH_1192. The location of the en face OCT shown in (D) is outlined on the fundus photograph (A). In this subject a large elliptical EZ disruption with a central core of low reflectivity is visualized with en face OCT (D). Dashed lines on D indicate locations of the horizontal and vertical B-scans (B,C), while the square represents the area shown in (E-G). The confocal AOSLO image revealed ambiguous reflectivity from the present photoreceptors (F). The split-detector image revealed that the central core of the disruption contains dramatically enlarged (likely cone) photoreceptor inner segments, surrounded by smaller diameter (likely rod) photoreceptors indicated by “*” (G). The darkest area of the en face image (E) contains few scattered enlarged inner segments, visible in the split-detector image (G, arrows). The corresponding locations in the confocal image (F) are marked with open arrows. B-D scale bars = 200 µm. E-G scale bars = 100 µm.

126

Figure 6.2 summarizes the imaging for this subject’s left eye. En face OCT

revealed a large elliptical EZ disruption with a central island of decreased reflectivity.

The B-scan OCTs through the disruption show that the parafovea has apparently normal

retinal lamination, while the elliptical en face disruption has almost no reflectivity, and the

central island shows dim and inconsistent reflectivity spread across the EZ and IZ. Co-

localized confocal AOSLO imaging revealed that the elliptical island contains very few

bright cone photoreceptors and a majority of low-reflectivity structures. The margins of

the en face OCT disruption, on the other hand are composed of mostly normal

appearing photoreceptors. Split-detector AOSLO revealed that the island within the en

face OCT disruption is composed of dramatically enlarged photoreceptor (likely cone)

inner segments, surrounded by smaller diameter photoreceptors (likely rods). The

darkest area of the en face OCT image contains a sparse population of enlarged

photoreceptors.

Case 2:

Subject DW_10105 (54 year-old male) presented to the clinic without visual

disturbance, but had mildly reduced visual acuity 20/30 OD, 20/50 OS. On exam there

was a slight sheen in the temporal foveae, fluorescein angiography showed dilation and

leakage of the temporal parafoveal capillaries and subsequent OCT imaging revealed

inner and outer retinal cavitations OU. A diagnosis of MacTel was made, and classified

as stage 2 OU according to Yannuzzi et al. [301].

127

Figure 6.3 Multimodal imaging in MacTel subject DW_10105. The location of the en face OCT shown in (D) is outlined on the fundus photograph (A). The en face OCT revealed a clover leaf shaped EZ disruption centered at the macula (D). Dashed lines on D indicate locations of the horizontal and vertical B-scans (B,C), while the square represents the area shown in (E-G). Within the EZ disruption there are two distinct levels of reduced reflectivity, the center of the disruption shows nearly no reflectivity, while there is a bordering region that reflects at a level between the center and the normal EZ outside (E). The confocal AOSLO imaging revealed waveguiding photoreceptors only in the regions of normal EZ reflectivity (F). The split-detector image on the other hand revealed photoreceptors in the border zone of abnormal EZ reflectivity, as well as photoreceptors within the EZ disruption (G). B-D scale bars = 200 µm. E-G scale bars = 100 µm.

128

Figure 6.3 summarizes the imaging for this subject’s right eye. En face OCT

reveals a clover-leaf shaped EZ disruption centered approximately 1° temporal to the

macula. The EZ disruption itself contains two distinct gray levels of reduced reflectivity,

the center shows nearly no reflectivity, while the margin is reduced at a level between

that of the center and the bright EZ reflectivity in the parafovea. Confocal AOSLO

imaging revealed normal (waveguiding) photoreceptors confined to the regions of normal

EZ reflectivity. The split-detector AOSLO imaging on the other hand, revealed

photoreceptors in the margin of abnormal EZ reflectivity, as well as a few photoreceptors

well within the EZ disruption itself, which are not visible with any other modality.

Case 3:

Subject SR_10139 (55 year-old male) presented with a persistent central

scotoma in the right eye from a motorcycle accident approximately 35 years prior. No

cause for visual deficit was identified clinically prior to the research visit. Both eyes were

imaged, and visual acuity at the time of research imaging was 20/20 OU. A foveal

photoreceptor defect was detected in the right eye (not shown), despite no subjective

vision loss in the left eye, a parafoveal abnormality was visualized, and is described

below.

129

Figure 6.4. Multimodal imaging in cg-BOT subject SR_10139. The location of the en face OCT shown in (D) is outlined on the fundus photograph (A). OCT imaging in this subject with ocular trauma 35 years previous revealed a small EZ disruption in the parafovea, approximately 0.75mm supero-temporal from the foveal center (D). Dashed lines on D indicate locations of the horizontal and vertical B-scans (B,C), while the square represents the area shown in (E-G). In addition to the EZ disruption, the B-scan OCT revealed interdigitation zone (IZ) disruption that spans a larger area (B & C). Confocal AOSLO revealed a small square shaped region of reduced photoreceptor waveguiding larger than the region of EZ disruption (F). The split-detector image revealed a mosaic of enlarged photoreceptors (arrows) in the area of EZ disruption, surrounded by apparently normal cone and rod photoreceptors (G). The corresponding locations in the confocal image (F) are marked with open arrows. The dark vertical stripes in the confocal image (F) are likely vessel shadows, which appear as a blur in the split-detector image (G). B-D scale bars = 200 µm. E-G scale bars = 100 µm.

130

Figure 6.4 summarizes the imaging for this subject’s left eye. En face OCT in this

subject revealed a small EZ disruption in the parafovea, approximately 0.75mm supero-

temporal from the foveal center. B-scan OCT revealed that the EZ disruption is

accompanied by an IZ disruption that spans a larger lateral extent. Confocal AOSLO at

this location revealed that the region of EZ and IZ disruption on OCT corresponded to a

small square shaped region of reduced photoreceptor waveguiding. The split-detector

AOSLO images revealed a mosaic of enlarged photoreceptors in the area of EZ and IZ

disruption, surrounded by apparently normal cone and rod photoreceptors. Notably, the

disruption size on confocal AOSLO more closely mirrors the size of the IZ disruption as

previously reported [199] (Figure 6.5); whereas split-detector AOSLO demonstrates

there is not complete loss of the photoreceptor mosaic as the EZ disruption might

suggest. The dark vertical stripes in the confocal image are likely vessel shadows, which

appear as a blur in the split-detector image.

Case 4:

This subject was previously described shortly after trauma [199]. Briefly, subject

WW_0923 (22-year-old male) was an unrestrained driver in a motor vehicle collision in

which airbags were deployed. Clinical evaluation on post-trauma day 2 revealed

commotio retinae and RPE changes within the macula, as well as wide-spread

hemorrhaging in the posterior pole OU. Research imaging presented here was

performed 22 months after trauma; he reported a persistent central scotoma OS, yet

visual acuity was 20/20 OS at this time.

131

Figure 6.5 Comparison of disruption size in confocal AOSLO, IZ and EZ en face OCT segmentation. The two disruptions above show better spatial correspondence of confocal disruptions to that seen with IZ segmentation in en face OCT. Since the IZ has been reported to represent the intersection of the photoreceptor outer segments with the RPE [302], this suggests that the hypo-reflectivity observed in the confocal images is likely related to abnormalities in the outer segments. Scale bars = 100 µm

Figure 6.6 summarizes the imaging for this subject’s left eye. En face imaging

revealed a tri-petaloid EZ disruption centered at the fovea. Confocal AOSLO imaging

revealed an anchor shaped area of abnormal non-waveguiding photoreceptors that

corresponded roughly to the region of EZ disruption. Split-detector imaging revealed a

pattern nearly identical to that seen in the en face OCT with a contiguous mosaic of

photoreceptors except for the infero-temporal prong. The photoreceptors themselves

within areas close to the EZ disruption vary widely in size over very small regions from

dramatically enlarged to nearly normal diameters. As with subject SR_10139, the IZ

132

disruption (as opposed to the EZ disruption) on en face OCT better correlates to

confocal AOSLO findings of reduced photoreceptor waveguiding (Figure 6.5).

Figure 6.6 Multimodal imaging in cg-BOT subject WW_0923. The location of the en face OCT shown in (D) is outlined on the fundus photograph (A). En face imaging in this subject revealed a tri-petaloid EZ disruption centered at the fovea (D). Dashed lines on D indicate locations of the horizontal and vertical B-scans (B,C), while the square represents the area shown in (E-G). Confocal AOSLO imaging revealed a similarly shaped, though enlarged, region of non-waveguiding photoreceptors (F). Split-detector imaging revealed a nearly contiguous mosaic of photoreceptors which change dramatically in size within small area (G). There is only one small region at the bottom right corner of the disruption where there appears to be a complete absence of photoreceptor structure (G). B-D scale bars = 200 µm. E-G scale bars = 100 µm.

133

Case 5:

Subject MP_10097 (40-year-old male) was previously diagnosed with BCM.

Genetic testing revealed a previously reported causative mutation (p.C203R; c.607T>C)

[143] in OPN1MW and OPN1LW genes. He complained of photophobia, problems with

dark adaptation, and difficultly with color discrimination. His visual acuity measured

20/60+2 and 20/70-1 in the right and left eyes, respectively with a refraction of -4.5 D OD

and -5.5 D OS. The anterior segment exam was unremarkable, while fundus exam

revealed healthy appearing optic nerves and normal vasculature. There was a blunted

foveal light reflex in the right eye. The peripheral retinal exam was unremarkable with

exception of a nevus nasal to the optic nerve OD. Octopus kinetic visual fields revealed

intact responses to all isopters OU. Multifocal ERGs were nearly unrecordable OU.

Full-field ERGs demonstrated normal Dark-adapted 0.01 responses, normal amplitude of

the Dark-adapted 3.0 response but loss of the oscillatory potentials. Dark-adapted cone

responses were recorded by subtracting the response to a red flash (KW26 filter) from

the matched response to a blue flash (KW47, 47A, 47B, 0.6 ND filters). These

were severely attenuated. Light-adapted 3.0 single flash and flicker responses were

severely attenuated and prolonged. ERG recordings met or exceeded the ISCEV

standards [303].

Figure 6.7 summarizes the imaging for this subject’s right eye. En face OCT

revealed an elliptical area of EZ hyper-reflectivity spanning from the bottom left to the top

right corner of the area shown in Figure 6.7E. Interestingly, the EZ abnormality in this

patient is most clearly visualized with B-scan OCT, as the en face OCT shows only a

subtle hyper-reflectivity. In contrast to the subjects discussed above (Figure 6.2, Figure

6.3, Figure 6.4, Figure 6.6), en face OCT disruption is demarcated hyper not hypo-

134

reflectivity. Within the disruption the confocal AOSLO image contains severely disrupted

photoreceptor mosaic where the vast majority of photoreceptors are non-waveguiding.

Despite a near total loss of cone function on ERG, the split-detector image revealed a

contiguous photoreceptor mosaic composed of cones at the center, and then rods and

cones in the immediate surrounding periphery throughout the areas of little to no

reflectivity in the confocal image. The area of greatest EZ reflectivity corresponds

spatially to the area of dense cone packing. Although the EZ reflectivity appears normal

in the parafovea, the confocal image revealed that the majority of cones are abnormal

and non-waveguiding in this region.

6.4 Discussion

In this study, we utilized two AOSLO modalities in order to evaluate the

photoreceptor structure within EZ disruptions identified with OCT. We do not

characterize the visual sensitivity within each disruption, or claim that every

photoreceptor visualized with split-detector is a functional photoreceptor. It has been

shown that the EZ integrity can be a good surrogate marker for visual function [295, 304-

307], however, disruption of the EZ band does not always correlate with reduced acuity

[308]. The results here indicate that the integrity of the EZ band is also not a good

predictor for presence of photoreceptors. A study of patients recovering from macular

hole repair found that the integrity of the external limiting membrane (ELM) rather than

EZ in B-scan OCT was a better functional indicator [309]. All five cases demonstrated

here have fully intact ELM over their photoreceptor lesions, which may argue that

integrity of the ELM also better predicts photoreceptor anatomy.

135

Figure 6.7 Multimodal imaging in BCM subject MP_10097. The location of the en face OCT shown in (D) is outlined on the fundus photograph (A). The EZ disruption in this patient is most clearly visualized with B-scan OCT, as the en face image revealed a poorly discriminated hyper-reflectivity at the center of the macula (D). Dashed lines on D indicate locations of the horizontal and vertical B-scans (B,C), while the square represents the area shown in (E-G). The confocal AOSLO image revealed a severely disrupted mosaic, where the vast majority of photoreceptors are non-waveguiding (F). The split-detector image revealed a contiguous photoreceptor mosaic composed of cones at the center, and then rods and cones in the immediate surrounding parafovea (G). The area of EZ hyper-reflectivity in (E) corresponds spatially to the area of densest cone packing (G). Although the EZ reflectivity appears normal in the parafovea (E), the confocal image revealed that the majority of cones are abnormal and non-waveguiding (F). B-D scale bars = 200 µm. E-G scale bars = 100 µm.

136

137

Figure 6.8 AOSLO montages in linear and logarithmic contrast from the 5 subjects reported in the study. The left column shows the confocal AOSLO montage presented on a linear display, while the center column shows the same confocal AOSLO montage on a log display. This increases the apparent brightness of some of structures within the dimly reflecting regions, not all of which are photoreceptor in origin. The right column provides the split-detector AOSLO montage on a linear display. Scale bars = 100 µm.

Although the physiologic mechanisms for the disruption of photoreceptors are

undoubtedly different between these varied conditions, the appearance with AOSLO is

nonetheless quite similar. The confocal images fail to identify a large proportion of

photoreceptor cells visualized with split-detector AOSLO. Potentially the most

unexpected similarity is found between the images in cg-BOT, CRD and MacTel. In

these disparate pathologies the en face image shows bright normal appearing EZ

surrounding a disruption with two distinct gray-levels. In both of these cases the area of

dim EZ signal corresponds to areas of residual photoreceptor structure with enlarged

inner segments in the split-detector images, while the area with no reflectivity in OCT

shows scant enlarged photoreceptors.

Regarding the naming controversy of the retinal reflective bands in OCT images,

the comparisons of AOSLO and OCT here offer a unique perspective. As shown above,

inner segment structure is visualized with split-detector in areas without EZ reflectivity.

Thus, inner segments do not appear to generate a strong reflection in OCT. This may

signify that an outer segment is required to generate the reflection, and thus that the

reflective band is more likely from the junction of the inner and outer segments, rather

than within the EZ of the inner segment. Unfortunately split-detector cannot currenly

distinguish the myoid versus the ellipsoid zones of the inner segment, so an intact

appearing inner segment may in fact be lacking a normal EZ portion, and this may be

the cause of the loss of the OCT reflection.

138

The reflectivity of the more posterior band, the IZ, appears to be closely related

to the confocal AOSLO signal. This result implies that not-only is an outer segment

required to generate the IZ reflection, but the outer segment should be well interfaced

with the RPE, and waveguiding light normally.

The significance of the split-detector imaging results demonstrated here varies

depending on the pathology. As genetic causes of photoreceptor disruption, CRD and

BCM are both potentially amenable to future gene therapy approaches to halt the

disease and hopefully rescue diseased photoreceptors [280]. The examples shown here

in Figure 6.2 & Figure 6.7, offer great promise for this approach, since they demonstrate

robust populations of photoreceptors at the center of the fovea that could not be

visualized with confidence in either confocal AOSLO or OCT. There is no gene therapy

currently in development for MacTel or cg-BOT, but the finding of photoreceptors with

split-detector within areas of EZ disruption is promising nonetheless. The fact that

AOSLO imaging is able to follow the anatomic changes of injured photoreceptors as they

enlarge and lose their outer segment [276, 277] offers a useful biomarker with which to

evaluate a potential therapeutic intervention.

A potential limitation of this study is the lack of hardware eye tracking in the SD-

OCT machine used to record the macular volumes. Eye-motion artifacts are visible in

some of the en face images shown here, especially towards the ends of the scans. To

avoid including corrupted scans, each volume was manually screened for excessive

motion between frames before the en face image was generated. Since all en face

segmentation was manual, there is also the potential for inaccuracy of the axial position

of the segmentation band. This source of error may be significant, but to our knowledge

there is currently no automatic segmentation software that can accurately account for

139

the types of discontinuities shown here. The new imaging modality described here, split-

detector AOSLO, has slightly lower resolution than confocal and is often unable to

resolve rod and foveal cone photoreceptors [34]. This resolution may limit the

quantification of absolute photoreceptor structure in some subjects. As shown above,

however, photoreceptors are often enlarged in the vicinity of EZ disruptions, which

partially mitigate this limitation. Another limitation comes from the fact that all of the SD-

OCT data was acquired through the center of the pupil. Directional OCT techniques

[226] may provide improved visualization of abnormally waveguiding photoreceptors

within EZ lesions, and provide more accurate visualization of the lesion boundaries

[310]. Finally, use of AO-OCT techniques may allow imaging the EZ layer at a resolution

comparable to that of confocal AOSLO [130, 311-313], though it is not known how the

disruption of outer segment structure affects the visualization of photoreceptors on AO-

OCT images.

In summary, here we demonstrate the cellular anatomy within and on the

margins of EZ disruptions demonstrated with en face OCT. The results indicate that the

interpretation of EZ disruptions visualized by OCT is not as simple as once thought. The

absence or presence of reflectivity in the EZ band does not reliably predict the degree of

residual photoreceptor structure. Future studies correlating other imaging modalities,

such as AO-OCT and directional OCT [226], with clinical OCT are necessary to improve

the interpretation of OCT EZ disruption.

140

Chapter 7 Photoreceptor Inner Segment Morphology in Best Vitelliform

Macular Dystrophy

7.1 Introduction

Best Vitelliform Macular Dystrophy (BVMD), also known as Best’s disease is an

autosomal dominant macular degeneration of variable penetrance caused by the BEST1

gene. This means that it only takes one copy of the gene from either parent for a child to

have Best’s disease, but the severity of the disease varies widely even in those who do

possess the very same gene mutation. The disorder is characterized by varying

accumulation of yellowish vitelliform material that can evolve into atrophic, fibrotic

appearing lesions [237, 314]. Clinical vitelliform lesions of BVMD are usually restricted to

the macula, although lesions have been reported at more eccentric locations [315].

Mutations in the BEST1 gene located on chromosome 11q13 encoding the

protein bestophin-1 are known to cause BVMD and several other retinal degenerative

diseases [316-320]. Bestrophin-1 has been localized to the basolateral membrane of the

retinal pigment epithelium (RPE) [321] and is thought to function as a calcium sensitive

chloride channel while also influencing other channel functions [322-326]. Dysfunction of

this channel can lead to the hallmark findings of BVMD, including an abnormal

electrooculogram (EOG), an electrophysiological test that measures changes in the

transepithelial potential across the RPE throughout the retina [327]. EOG often shows

diminished light peak (LP) response in individuals with BVMD, even when no clinical

features are evident [328].

Limited histological studies of BVMD have found an abnormal accumulation of

lipofuscin granules in the RPE [329-332] and photoreceptor loss over areas of intact

RPE [333, 334]. These studies are in agreement with a knock-in mouse model of BVMD

141

that also demonstrated subretinal deposits of unphagocystosed photoreceptor outer

segments and lipofusion granules [335]. Some authors have hypothesized that

dysfunction of the RPE leads to accumulation of toxic materials which in turn leads to

degeneration of the overlying photoreceptors in BVMD [330, 333]. Some studies have

found structurally normal RPE in BVMD lesions, with the primary impact of BVMD

appearing to be subretinally to photoreceptors themselves [336].

Advanced retinal imaging techniques have given insight into the effects of BEST1

mutations on outer retinal structures in BVMD. Several optical coherence tomography

(OCT) studies have localized the vitelliform material in BVMD to the subretinal space

[159, 336-339], and some have shown increased thickening of the reflective band

corresponding to photoreceptor outer segments [336, 337]. Recently, Abramoff et al.

demonstrated what appears to be outer segment photoreceptor elongation with light

adaptation in areas of the macula outside of retinal lesions in BVMD, which suggests

photoreceptor dysfunction beyond clinically apparent lesions [340]. However, multifocal

electroretinogram (ERG) irregularities correspond to involved lesion areas [341] and

quantitative fundus autofluorescence (qAF) on patients with BVMD showed normal qAF

in nonlesion areas, suggesting no increased lipofuscin levels outside of observed retinal

lesions [342]. Previously, confocal adaptive optics scanning light ophthalmoscopy

(AOSLO) indicated that some photoreceptor structure persists over areas of Best

lesions, and photoreceptor density is normal in areas adjacent to clinical lesions in

BVMD [159]. This study employs a new imaging technique, non-confocal split-detector

AOSLO, to better delineate photoreceptor structure in BVMD. Additionally, we sought to

analyze changes in the photoreceptor mosaic over time.

142

7.2 Methods

Subjects

Research procedures followed the tenets of the Declaration of Helsinki and were

approved by the institutional review board at the Medical College of Wisconsin. Four

previously described family members [159] and one unrelated subject, all with identified

mutation (p.Arg218Cys c.652C→T) in BEST1 [343] and clinical findings consistent with

BVMD, participated in this study. See the Table for further information about each

subject. Axial length measurement (Zeiss IOL Master; Carl Zeiss Meditec), visual acuity

testing and fundus photography were performed at the time of research imaging in all

subjects.

Table 7.1 Best disease study subject demographics

Subject Age (y) Gender Lesion Type Acuity (Snellen)

KS 0325 53 M Atrophic OS, 20/80

KS 0589 61 F Atrophic OS, 20/50

KS 0599 53 F Late vitelliruptive OD, 20/30

KS 0600 18 M Early vitelliform OS, 20/20

KS 0601 20 F Vitelliform with early vitelliruptive

OS, 20/20

Y – years; M-male, F-female; OD – right eye; OS – left eye.

SD-OCT

Spectral-domain optical coherence tomography (SD-OCT) line and volumetric

scans were performed using the Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA) [208].

The location of the foveal pit was determined using the proprietary Fovea Finder function

of the Cirrus HD-OCT, and marked on the line scan ophthalmoscope (LSO) image.

Additional high-density volumetric scans acquired using the Bioptigen SD-OCT

(Bioptigen Inc., Morrisville, NC) nominally covering 7×7 mm (1000 A-scans/B-scan, 250

B-scans) were used to create en face OCT sections with custom software (Java, Oracle,

Redwood City, CA) [344]. Multiple horizontal and vertical macular B-scans nominally

covering 7 mm (1000 A-scans/B-scan; Bioptigen) were registered and averaged to

143

increase signal to noise ratio [208]. All OCT images are displayed on logarithmic

intensity scale.

AOSLO

AOSLO imaging was performed with the same instrument and technique as

described in Chapter 5 & 6. Photoreceptor image sequences were recorded at the fovea

as well as in the periphery to approximately 10° superior and temporal to fixation. In

subject KS_0589, an overlying epiretinal membrane in temporal macula obligated

imaging to 10° nasal from fixation. Image sequences were corrected for sinusoidal

distortion caused by the resonant scanner, then registered and averaged as previously

described [133]. Using a simplified Gullstrand 2 schematic eye, the predicted 291 µm per

degree of visual angle [207] was scaled linearly by the subject’s axial length to

determine the scale of AOSLO images. Averaged AOSLO images were aligned

manually in Adobe Photoshop (Adobe Systems Inc., San Jose, CA) to create a large

montage. This montage was manually aligned to the color fundus, LSO, en face OCT

and to previously acquired AOSLO images [159] (where available) using blood vessel

shadows as landmarks. The location of the fovea was marked on the AOSLO montage,

based on the subject’s fixation recorded in the Cirrus HD-OCT LSO image. All AOSLO

images are displayed on linear intensity scale.

To examine longitudinal changes (approximately two years elapsed) in the cone

mosaic, previously identified areas of normal cone density were re-analyzed in subjects

KS_0600 and KS_0601. At three locations in each subject, confocal AOSLO images

from both time points were aligned manually, registered with rigid translations using the

Stackreg plugin from ImageJ (National Institutes of Health, Bethesda, MA) and finally

cropped to the region of overlap. The Stackreg plugin corrects small rotations which are

144

not measured in the custom registration software described in Chapters 5 & 6. Cones

were identified with a previously described semi-automated algorithm [142]. Cone

density was calculated within 80 × 80 μm regions of interest (ROI).

In order to determine the effect of retinal lesions on the photoreceptor mosaic,

the cone density was measured inside and outside of macular lesions in all subjects.

Since non-waveguiding or misaligned photoreceptors are not visualized by confocal

AOSLO [34, 186], split-detector AOSLO images were chosen for analysis instead. 80 ×

80 μm ROIs were identified and analyzed for cone density across the entire span of

AOSLO imaging in each subject. A ROI was characterized as intra-lesional if any of the

ROI fell within the limits of the lesion as visualized by en-face OCT segmented at the

level of the ellipsoid zone (EZ). Then each lesion was sampled with 5-7 ROI to evaluate

for local density variations. Cell locations within the split-detector images were identified

manually. The distance between each ROI and the fovea was estimated, and cone

densities were compared to published normative in vivo values [138]. Normative data

was linearly interpolated to cover the range of measurement locations. Patient data was

pooled across eccentricity for comparison, as there is no measured difference between

temporal and nasal meridians across the eccentricities studied [33, 88] and superior and

inferior retinal loci are likely to underestimate cone photoreceptor density [33, 88].

Density data was evaluated using z-scores, calculated as the difference between the

subject measurement and the normative mean divided by the standard deviation at that

eccentricity. Z-scores of magnitude < 2.0 were considered normal, p values < 0.05 were

considered significant.

145

7.3 Results

The subjects included in this study had the same disease causing mutation in

BEST1 and demonstrate different stages of BVMD, with split-detector AOSLO providing

unprecedented views of the photoreceptor pathology (Figure 7.1, Figure 7.2, Figure 7.3).

Early in the disease the photoreceptor mosaic remains contiguous but with substantially

decreased density (Figure 7.1, Figure 7.3 first column). Later, after further photoreceptor

loss has taken place the mosaic no longer appears contiguous (Figure 7.2, Figure 7.3

2nd and 3rd column). Figure 7.3 shows the span of pathology across subjects in this study

from early vitelliform lesion to late stage atrophy and fibrosis, with the corresponding

photoreceptor mosaic changes.

Only KS_0600 and KS_0601 showed clear disease progression in OCT B-scan

over 32 and 30 months, respectively (Figure 7.4). To determine the effect of lesion

enlargement on photoreceptor number, previously analyzed areas were re-counted.

Three locations were analyzed in each subject, at approximately 1° from the fovea and

just nasal to the BVMD lesion, where cone density was previously determined to be

normal [159]. In KS_0601 the cone density was found to change -2.4%, -1.7% and 1.2%

over a period of 30 months. In KS_0600 the cone density was found to change 0.0%,

0.5% and -2.6% over a period of 32 months. These small changes in cone density are

within the 95% confidence interval for the repeatability of the method of parafoveal

density measurements (2.6-2.8%) [142], and therefore are consistent with no significant

changes.

In all subjects, the effects of the BVMD lesion on overlying photoreceptors was

assessed by comparing photoreceptor density within and outside of lesions as visualized

with en face OCT. The results are summarized in Figure 7.5. Intra-lesion cone density

146

was significantly reduced in subjects KS_0325, KS_0589 and KS_0601 (z-scores -5.0 to

-2.5). Near the fovea, KS_0589 exhibited reduced density (z-scores -5.0 to -3.6), but

with a return to normal at the edges of the lesion (z-scores -1.5 to -0.6). Intra-lesion

density in subject KS_0600 was preserved (z-scores 0.1 to 0.7). Extra-lesion cone

density is near normal in all subjects (z-scores -1.6 to 1.6) with the exception of one

measurement in KS_0589 (z-score -2.0). Within a lesion, the cone density and cone

appearance varied considerably over short distances, with some regions having almost

no photoreceptors as shown in Figure 7.2.

147

Figure 7.1 Imaging results from subject KS_0601. A. En face OCT at the level of the ellipsoid zone reveals a large ovoid retinal detachment at the location of the lesion. The area of AOSLO imaging shown below is indicated by the white square, dashed lines indicate the locations of the B-scans. B & C Horizontal and vertical B-scan OCT reveals a large vitelliform lesion just nasal to the center of the fovea. D. Confocal AOSLO imaging reveals sparse photoreceptor reflectivity. E. The split-detector AOSLO imaging reveals a near complete mosaic of cone photoreceptors. Scale bars 200 um.

148

Figure 7.2 Imaging results from subject KS_0325. A. En face OCT at the level of the ellipsoid zone reveals a very large BVMD lesion with irregular borders, approximately 2.3mm in diameter. The area of AOSLO imaging shown below is indicated by the white square, dashed lines indicate the locations of the B-scans. B. & C. Horizontal and vertical B-scan OCT reveals a large atrophic lesion including the entire perifovea centered just inferior to the fovea. D. Confocal AOSLO imaging reveals clusters of photoreceptor reflectivity. E. Split-detector AOSLO reveals numerous photoreceptors in an incomplete mosaic. Most photoreceptors have abnormal morphology, and some appear to be oriented horizontally. Scale bars 200 um.

149

Figure 7.3 Parafoveal photoreceptor imaging in remaining subjects. A-C, B-scan OCT, D-F, Confocal AOSLO imaging, G-I, Split-detector AOSLO. The earliest lesion of this cohort, from KS_0600, manifests as scattered loss of waveguiding in the confocal image. The split-detector image shows a complete and dense mosaic of photoreceptors. The B-scan from subject KS_0589 reveals a large late vitelliruptive lesion with significant subretinal debris. The confocal AOSLO image reveals scattered waveguiding photoreceptors, with a cluster of small reflective dots on top of the large debris (E.). The split-detector image reveals abnormal photoreceptor morphology and widely varying photoreceptor size over this small area. The bottom left corner of the image contains enlarged photoreceptors with local clearings. With this modality it does not appear that the debris is covered by photoreceptors as the confocal image suggests. Despite the obvious retinal atrophy and little EZ reflectivity in subject KS_0599, the split-detector AOSLO image reveals a near complete mosaic of photoreceptors at the fovea. The confocal AOSLO image fails to identify many of the photoreceptors, likely due to their abnormal waveguiding. Scale bars 100 um.

150

Figure 7.4 Longitudinal OCT follow-up of Best disease lesions for all patients. Time between imaging is listed in right panels in months. Scale bars 200 um.

151

Figure 7.5 Cone photoreceptor density inside and outside of lesions. Density was sampled in all subjects within (filled symbols) and outside their BVMD lesion (open symbols). Cone density is significantly reduced within the lesions, but returns to normal outside of lesions. Normative data [138] is shown as mean (solid line) ± two standard deviations (shaded region).

152

Figure 7.6 Co-registered AOSLO and OCT imaging from subject KS_0325, with normal AOSLO imaging for comparison spanning from 0.8 to 2.4 mm retinal eccentricity. The change in photoreceptor morphology inside and outside of the lesion is highlighted in D & F. Compared to normal (C), the confocal image from KS_0325 (D) shows abnormal intra-lesion reflectivity, which returns to normal appearing around the same eccentricity the EZ line in the OCT (E) becomes normal. The split-detector AOSLO image (F) shows that compared to normal, the cone density is reduced and cones are enlarged within the lesion. Outside the lesion, the number and appearance returns to normal. All scale bars 100 um.

153

Split-detector AOSLO imaging within BVMD lesions is repeatable, even in

subjects with advanced retinal degeneration as illustrated by the ability to track individual

cells, shown in Figure 7.7. Here the same clusters of photoreceptors were visualized

over four months follow-up. There were, however, structures that appeared and

disappeared from the images over this time scale (arrows, Figure 7.7). These round

features had a lumpy texture, were on average 20 μm in diameter, and appeared in

areas that previously contained isolated photoreceptors or apparently empty space.

Structures of similar size and appearance were also found to change in appearance on

much shorter time scales, as short as an hour. These features were only noted in

KS_0325, the subject with the most advanced disease.

7.4 Discussion

Accurate assessment of cellular structure in inherited retinal degenerations in vivo

can provide invaluable information about the pathology of these degenerations. In this

study, we employed newly developed split-detector AOSLO to further assess

photoreceptor structure associated with BVMD in five individuals with the same

previously reported BEST1 mutation (p.Arg218Cys). Compared to confocal imaging,

non-confocal split-detector AOSLO allows for a more accurate assessment of

photoreceptor structure in BVMD, especially in areas of the photoreceptor mosaic

overlying subretinal pathology (Figure 7.1, Figure 7.2, Figure 7.3, Figure 7.6).

Cone photoreceptor packing within vitelliform lesions can range from normal

appearing mosaic (Figure 7.1) to significant disruption (Figure 7.2). As highlighted in

patients KS_0589 and KS_0599 (Figure 7.3), significant intra-lesional variability also

exists, with focal areas of near normal density present next to areas with severe

disruption. In the fibrotic stages of BVMD as seen in KS_0325, cone photoreceptors

154

remain, although sparsely packed and with focal areas entirely devoid of photoreceptors

(Figure 7.2 & Figure 7.7). We hypothesize this loose packing allows some

photoreceptors to freely pivot so that they are oriented horizontally, allowing visualization

of both inner and outer segments of the photoreceptors (Figure 7.2 – teardrop shaped

structures in split-detector image). This irregular packing underscores the need for

caution when reporting cone photoreceptor densities within areas of pathology as

visualized by AOSLO, as these can vary dramatically even if measurements are taken

within 100 µm of each other.

It has been long debated whether BVMD has only focal clinically apparent fundus

effects or is a true panretinal photoreceptor disorder. The results presented here show

that within clinically apparent lesions, cone photoreceptor inner segments are enlarged

and cone density is reduced. In agreement with previous AOSLO studies [159, 345],

immediately adjacent to the lesions both density and appearance of cone inner

segments return to normal (Figure 7.5, Figure 7.6), lending support to BVMD having only

a very focal effect on the photoreceptor mosaic. Interestingly, patient KS_0325 has been

followed clinically for five years with documented detachment of the retina from the RPE

within these lesions being present that entire time. Despite this change, split-detector

AOSLO confirms photoreceptors overlying these lesions still exist and as seen by stable

central fixation during imaging, suggests an alternate pathway for maintenance of the

photoreceptors viability then from the RPE.

Split-detector imaging also revealed disc-like structures consistent in size and

location with subretinal macrophages, which have been previously found in histologic

samples from patients with BVMD [332, 346] (Figure 7.7 C&D). The significance of this

155

finding is unknown, but these may represent the first in vivo images of subretinal

macrophages in a human eye.

A potential limitation of this study is that all five subjects are affected by the same

mutation in the BEST1. While the subjects represent the spectrum of stages of BVMD,

the clinical and subclinical phenotypes described here cannot necessarily be extended

to other mutations in BEST1. Conversely, the diverse findings displayed above are more

likely related to the stage of the disease rather than differential pathophysiology.

In summary, the improved resolution possible with split-detector AOSLO allows

for increased understanding of cellular disease processes and could potentially be useful

in monitoring therapeutic response on a cellular level in diseases such as BVMD. Future

studies should be expanded to include high-resolution imaging in individuals with other

mutations in BEST1 to further explore the genotype-phenotype correlations in

photoreceptor morphology in BVMD.

(Next Page)

Figure 7.7 Short-term and long-term variability in photoreceptor layer imaging with split-detector AOSLO in Best disease. Circles indicate photoreceptor landmarks identified in both timepoints. Arrows indicates features that changed over long (A & B) or short (C1, C2, D1, D2) time scales. Images C1, C2, D1, D2 depict mobile features of size consistent with subretinal macrophages (arrows). A & B scale bar 100 um. C1, C2, D1, D2 scale bar 25 um.

156

Chapter 8 Conclusions and Future Work

This thesis aimed to improve our ability to study, diagnose and monitor the

progression of eye disease non-invasively at the microscopic scale. The outcomes can

be grouped into three major themes: a survey of hyper-reflective inner retina structures,

the demonstration and applications of dark-field ophthalmoscopy and applications of

non-confocal split detection ophthalmoscopy.

8.1 Survey of Hyper-reflective Inner Retina Structures in Confocal Imaging

Prior to the work presented here, almost all ophthalmic AO imaging was focused on

the photoreceptor mosaic. Despite the undeniable importance of photoreceptors, they

cannot contribute to vision without intact upstream connections to the brain. Thus, we

set out to apply confocal AOSLO imaging to the inner retina. The retrospective analysis

of previously recorded images, together with prospective data collection, revealed

multiple hyper-reflective microscopic and macroscopic inner retinal findings in normal

and diseased subjects, which we coarsely classified into seven categories. Among

these, nummular reflectivity and vessel associated membranes were, to the best of our

knowledge, not previously reported.

Two other categories, waxy and granular reflectivity, most likely correlate to the

clinical entity known as epiretinal membrane (ERM.) Given their large size, ERMs are

well known in conventional ophthalmic imaging [221] and histologic studies [220].

However, our ERM imaging revealed dramatic differences in microscopic texture as well

as size changes in as little as eight weeks. Future work is needed to explore the

potential clinical value in predicting the risk of progression from asymptomatic ERM to

vision disruption or retinal detachment. Despite the fact that ERMs are extremely

common, with incidence often reported over 10% [221, 347-349], only a small number

158

lead to vision loss from increased scattering, retinal distortion or detachment. The most

feared complication of ERM is a macular hole, with incidence around 0.29% [221], where

the tangential forces (traction) caused by epiretinal fibrosis lead to a central foveal retinal

detachment [350]. Given that the fovea holds our central vision responsible for reading

and fixation, macular holes often lead to significant loss of visual acuity [308]. There is

currently no clinical method to determine which of these membranes will progress to

advanced fibrosis and retinal detachment or a macular hole. Histologic studies have

separated ERMs based on their cellular and connective tissue components [218-220,

351-353], which may be possible in vivo using AOSLO. Future studies comparing the in

vivo and histologic appearance of ERM are warranted, given that ERM removal is a

relatively common procedure, patients could be imaged before surgery and the surgical

specimen could be evaluated histologically. Additionally, further ERM imaging is required

with non-confocal AOSLO and eventually multi-wavelength AOSLO to attempt to find

characteristic patterns of dangerous versus benign membranes.

One of the most remarkable findings of the survey was that these hyper-reflective

structures appeared across eyes of subjects diagnosed with unrelated ocular and

systemic conditions. This lack of specificity might indicate that despite widely varied

initial insults, many diseases might have common degeneration pathways. For example,

the responses to cell stress in glaucoma and branch vein occlusion may both lead to

formation of a waxy membrane. Similarly, retinal damage in central serous retinopathy

and Leber’s congenital amaurosis may both cause similar vascular membranes. The

work here is too preliminary to make any changes to current clinical management of

these conditions, but it does encourage further evaluation of the inner retina with

159

AOSLO. Most of all, the results motivate an evaluation of the inner retina with the non-

confocal techniques described below.

8.2 Demonstration and Application of Dark-field

The development of AOSLO dark-field imaging, was inspired by the offset confocal

pinhole imaging demonstrated by Chui et al. [194]. AOSLO dark-field imaging led to the

non-invasive visualization of the RPE mosaic near the foveal center in some normal

volunteers, as well as, in retinal lesions with photoreceptor loss. The former is important

because previous studies relied on the intrinsic fluorescence of the lipofuscin excited

with potentially phototoxic visible light, whereas dark-field AOSLO captures scattered

NIR light. The source of the scatter is currently unknown, but it is spatially correlated with

the location of brightest autofluorescence, suggesting it could be from lipofuscin

granules. The latter is highly significant because it allows for the first time to distinguish

between photoreceptors and RPE within retinal lesions with high confidence, which is

essential for any quantitative study of photoreceptor structure. Previous work has

demonstrated that when photoreceptor structures are completely degenerated, the RPE

is clearly visible with confocal AOSLO [167]. In patients with active disease, however,

the confocal signal is more difficult to interpret, likely due to debris and degenerative

tissue (Figure 8.1). In these situations dark-field AOSLO attenuates the bright reflections

of debris, revealing that the predominant source of signal is likely RPE, as opposed to

photoreceptors. This makes dark-field imaging a more practical tool to discern RPE from

photoreceptors than using the temporal intensity variations of the photoreceptors [148,

151, 354, 355].

160

Figure 8.1 Photoreceptor / RPE disambiguation with dark-field AOSLO. Disorganized reflectance in confocal image is caused by RPE cell granules and degenerating tissue debris, in a patient with macular telangiectasia. Scale bar 50 µm.

8.3 Demonstration and Application of Non-Confocal Split-detection

Motivated by the successful implementation of split-detector vascular imaging

headed by Yusufu Sulai PhD, we designed and implemented a new AOSLO imaging

paradigm allowing for simultaneous non-confocal split-detection and confocal imaging. In

this way we were able to compare confocal and non-confocal split-detector images of

the photoreceptor mosaic in perfect spatial register. In order to confirm that the split-

detector signal was derived from inner segments, the results were validated against

histology and also through imaging subjects with achromatopsia.

The first demonstrations of the photoreceptor inner segment mosaic imaging using

non-confocal split-detector AOSLO have already led to important and clinically relevant

discoveries. Most notably, the ability to discern between the true absence of

photoreceptors and their non-waveguiding or poor orientation relative to the pupil of the

eye, which is essential for the selection of candidates for upcoming human retinal gene

therapy trials in achromatopsia [356], and others in development. Also, non-waveguiding

cones were visualized for the first time within retinal lesions in ocular trauma, macular

161

telangiectasia, blue cone monochromacy and cone rod dystrophy. Finally, the

photoreceptor morphology of retinal lesions in Best’s disease was demonstrated. It is our

hope that the new information available from non-confocal imaging methods will prompt

a re-examination of many of the photoreceptor disease studies using AOSLO, since

these may have reported incorrect photoreceptor counts and inferred cell loss when this

was not necessarily the case.

It is currently unknown whether the photoreceptor inner segments visualized with

split-detector imaging are functional or even recoverable. While we have not yet

measured the function of individual photoreceptors not seen with confocal and visible

only with split-detector, recent work has shown that areas of presumed non-waveguiding

cones can provide meaningful visual sensitivity in patients with macular telangiectasia

type 2 [134]. Additionally, the patient with the most severe retinal degeneration in

Chapter 6 was able to hold steady fixation in the center of their large retinal lesion,

suggesting that severely dysmorphic photoreceptors still provide some vision. We are

optimistic that the authors of [134] will soon report simultaneous split-detector imaging

and visual function testing, in order to assess the function of individual photoreceptor

inner segments. Undoubtedly, these results will depend on the disease process of

interest. For example, congenital causes of outer segment loss, like achromatopsia, will

likely reveal little to no function, while more acute outer segment damage, such as age

related macular degeneration, could show some residual sensitivity.

Whether inner segments represent viable and recoverable photoreceptors will be

difficult to answer in human volunteers before the aforementioned gene therapy studies

(which plan to include AO imaging) report results [356, 357]. Given that animal studies of

photoreceptor recovery have been published, it is likely that animal research by our

162

group [358] or others [359] who have incorporated non-confocal split-detection into

animal imaging systems will demonstrate the ability or inability of inner segments to

regain waveguiding soon. This future work should not only include gene therapies [253-

256], but also cell transplant methods [257, 258] already attempted in preliminary human

trials [47].

The functionality and prognosis for photoreceptor inner segments is extremely

important for reporting quantitative metrics of photoreceptor structure. In achromatopsia,

it is most important to know how many photoreceptors are potentially available for

salvage gene therapy. In Best’s disease on the other hand, it is debatable whether the

total number of photoreceptors with and without outer segments are the correct metrics

to use without knowing the viability of non-waveguiding cones, some of which are

dramatically enlarged. If abnormal cones in this disease are irreversibly damaged, then

their count is not relevant. Beyond number, the size of the inner segment appears to be

a reliable indication of pathology. In nearly all examples in Chapters 5-7, the

photoreceptors that have lost their outer segment reflectivity have also increased

substantially in width. This could be a useful biomarker going forward in studies of retinal

disease, especially if this is a reversible phenomenon, and warrants further study.

Future work is required to address the limitation of split-detector AOSLO compared

to confocal imaging to visualize rod photoreceptors and small foveal photoreceptors.

This is likely related to the contrast mechanism of split-detector, namely multiple scatter

as a result of by local changes in retinal topography. There appears to be a threshold

diameter, below which the directional reflections of photoreceptor inner segments are

too small to be differentiated. This may be explained by the relatively flat cross-sectional

profile of foveal cone and rod inner segment apices, compared to the dome-like profile of

163

extra-foveal cones. The quality of the illumination is the most important determinant of

resolution in split-detector images, as shown by the sensitivity of image quality to focus,

despite the lack of any confocal detection pinhole, effectively providing axial sectioning.

Attempts to improve the lateral resolution and contrast included polarization and

wavelength modification, but did not detectable improvement for vascular structures

[206] but may improve inner segment photoreceptor contrast and/or resolution.

Further work is necessary to optimize the detection scheme for split-detector

AOSLO beyond the simple symmetric method pictured in Figure 5.1. This work is

already on-going, and may show promise to reveal other retinal cellular structure, such

as cell somas of the photoreceptors, bipolar cells and/or others as demonstrated in mice

[359]. Finally, axially resolved hyperspectral imaging (confocal or non-confocal) may

provide non-invasive contrast to show inner retinal cell structures. Technology to

mitigate ocular chromatic aberration variability across individuals and retinal locations

will be required before reliable results can be obtained.

8.4 Conclusions

In summary, the novel imaging projects and methods described in this work offer

powerful new tools for the study of retinal and neurologic disease. Although AOSLO

imaging is still far from becoming a mainstream clinical device, the ability to visualize the

RPE and photoreceptor inner segments with safe and comfortable light levels will

significantly benefit the field of ophthalmology. These techniques have already increased

our understanding of retinal disease, and are being adopted by the AO research

community [359].

164

References

1. Smith, G. and D.A. Atchison, The Eye and Visual Optical Instrumentation. 1st ed. 1997, Cambridge: Cambridge University Press.

2. Jackman, W.T. and J.D. Webster, On photographing the retina of the living human eye. The Philadelphia Photographer, 1886. xxiii(275): p. 340-341.

3. Webb, R.H., G.W. Hughes, and O. Pomerantzeff, Flying spot TV ophthalmoscope. Appl Opt, 1980. 19(17): p. 2991-2997.

4. Webb, R.H., G.W. Hughes, and F.C. Delori, Confocal scanning laser ophthalmoscope. Appl Opt, 1987. 26(8): p. 1492-1499.

5. Elsner, A.E., et al., Infrared imaging of sub-retinal structures in the human ocular fundus. Vision Res, 1996. 36(1): p. 191-205.

6. Huang, D., et al., Optical coherence tomography. Science, 1991. 254(5035): p. 1178-1181.

7. Swanson, E. and D. Huang, Ophthalmic OCT reaches $1 billion per year. Retinal Physician, 2011. 8(4): p. 45.

8. Liang, J. and D.R. Williams, Aberrations and retinal image quality of the normal human eye. J Opt Soc Am A Opt Image Sci Vis, 1997. 14(11): p. 2873-2883.

9. Campbell, F.W. and D.G. Green, Optical and retinal factors affecting visual resolution. J Physiol, 1965. 181(3): p. 576-93.

10. Venkateswaran, K., A. Roorda, and F. Romero-Borja, Theoretical modeling and evaluation of the axial resolution of the adaptive optics scanning laser ophthalmoscope. J Biomed Opt, 2004. 9(1): p. 132-138.

11. Roorda, A., et al., Adaptive optics scanning laser ophthalmoscopy. Opt Express, 2002. 10(9): p. 405-412.

12. Martin, J.A. and A. Roorda, Direct and non-invasive assessment of parafoveal capillary leukocyte velocity. Ophthalmology, 2005. 112(12): p. 2219-2224.

13. Tam, J., J.A. Martin, and A. Roorda, Noninvasive visualization and analysis of parafoveal capillaries in humans. Invest Ophthalmol Vis Sci, 2010. 51(3): p. 1691-1698.

14. Chui, T.Y., T.J. Gast, and S.A. Burns, Imaging of vascular wall fine structure in the human retina using adaptive optics scanning laser ophthalmoscopy. Invest Ophthalmol Vis Sci, 2013. 54(10): p. 7115-7124.

15. Morgan, J.I., et al., In vivo autofluorescence imaging of the human and macaque retinal pigment epithelial cell mosaic. Invest Ophthalmol Vis Sci, 2009. 50(3): p. 1350-1359.

16. Gray, D.C., et al., In vivo imaging of the fine structure of rhodamine-labeled macaque retinal ganglion cells. Invest Ophthalmol Vis Sci, 2008. 49(1): p. 467-473.

17. Geng, Y., et al., Adaptive optics retinal imaging in the living mouse eye. Biomed Opt Express, 2012. 3(4): p. 715-734.

18. Geng, Y., et al., In vivo imaging of microscopic structures in the rat retina. Invest Ophthalmol Vis Sci, 2009. 50(12): p. 5872-5879.

19. Gray, D.C., et al., In vivo fluorescence imaging of primate retinal ganglion cells and retinal pigment epithelial cells. Opt Express, 2006. 14(16): p. 7144-58.

20. Scoles, D., et al., In-vivo imaging of retinal nerve fiber layer vasculature: imaging histology comparison. BMC Ophthalmol, 2009. 9: p. 9.

165

21. Pinhas, A., et al., In vivo imaging of human retinal microvasculature using adaptive optics scanning light ophthalmoscope fluorescein angiography. Biomed Opt Express, 2013. 4(8): p. 1305-1317.

22. Dubow, M., et al., Classification of human retinal microaneurysms using adaptive optics scanning light ophthalmoscope fluorescein angiography. Invest Ophthalmol Vis Sci, 2014. 55(3): p. 1299-1309.

23. Gocho, K., et al., High-resolution en face images of microcystic macular edema in patients with autosomal dominant optic atrophy. Biomed Res Int, 2013. 2013: p. 676803.

24. Huang, G., et al., A clinical planning module for adaptive optics SLO imaging. Optom Vis Sci, 2012. 89(5): p. 593-601.

25. Takayama, K., et al., High-resolution imaging of retinal nerve fiber bundles in glaucoma using adaptive optics scanning laser ophthalmoscopy. Am J Ophthalmol, 2013. 155(5): p. 870-81.

26. Takayama, K., et al., High-resolution imaging of the retinal nerve fiber layer in normal eyes using adaptive optics scanning laser ophthalmoscopy. PLoS One, 2012. 7(3): p. e33158.

27. Lombardo, M., et al., Adaptive optics imaging of idiopathic epiretinal membranes. Ophthalmology, 2013. 120(7): p. 1508-1509 e1.

28. Vilupuru, A.S., et al., Adaptive optics scanning laser ophthalmoscopy for in vivo imaging of lamina cribrosa. J Opt Soc Am A Opt Image Sci Vis, 2007. 24(5): p. 1417-1425.

29. Ivers, K.M., et al., Reproducibility of measuring lamina cribrosa pore geometry in human and nonhuman primates with in vivo adaptive optics imaging. Invest Ophthalmol Vis Sci, 2011. 52(8): p. 5473-5480.

30. Akagi, T., et al., In vivo imaging of lamina cribrosa pores by adaptive optics scanning laser ophthalmoscopy. Invest Ophthalmol Vis Sci, 2012. 53(7): p. 4111-4119.

31. Sredar, N., et al., 3D modeling to characterize lamina cribrosa surface and pore geometries using in vivo images from normal and glaucomatous eyes. Biomed Opt Express, 2013. 4(7): p. 1153-1165.

32. Bowmaker, J.K. and H.J. Dartnall, Visual pigments of rods and cones in a human retina. J Physiol, 1980. 298: p. 501-511.

33. Curcio, C.A., et al., Human photoreceptor topography. J Comp Neurol, 1990. 292(4): p. 497-523.

34. Scoles, D., et al., In vivo imaging of human cone photoreceptor inner segments. Invest Ophthalmol Vis Sci, 2014. 55(7): p. 4244-4251.

35. Kolb, H., K.A. Linberg, and S.K. Fisher, Neurons of the human retina: a Golgi study. J Comp Neurol, 1992. 318(2): p. 147-187.

36. Dacey, D.M., Physiology, morphology and spatial densities of identified ganglion cell types in primate retina. Ciba Found Symp, 1994. 184: p. 12-28; discussion 28-34, 63-70.

37. Marc, R.E. and B.W. Jones, Molecular phenotyping of retinal ganglion cells. J Neurosci, 2002. 22(2): p. 413-427.

38. Ross, M.H. and W. Pawlina, Histology : a text and atlas : with correlated cell and molecular biology. 6th ed. 2011, Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins Health. xviii, 974 p.

166

39. Nickla, D.L. and J. Wallman, The multifunctional choroid. Prog Retin Eye Res, 2010. 29(2): p. 144-168.

40. Carrol, J., G. Yoon, and D.R. Williams, The cone photoreceptor mosaic in normal and defective color vision, in The cognitive neurosciences, M.S. Gazzaniga, Editor. 2009, MIT Press: Cambridge, Mass. p. 383-394.

41. Friedman, D.S., et al., Prevalence of age-related macular degeneration in the United States. Arch Ophthalmol, 2004. 122(4): p. 564-72.

42. Dunaief, J.L., et al., The role of apoptosis in age-related macular degeneration. Arch Ophthalmol, 2002. 120(11): p. 1435-1442.

43. Ambati, J. and B.J. Fowler, Mechanisms of age-related macular degeneration. Neuron, 2012. 75(1): p. 26-39.

44. Bok, D., Contributions of genetics to our understanding of inherited monogenic retinal diseases and age-related macular degeneration. Arch Ophthalmol, 2007. 125(2): p. 160-164.

45. Myers, C.E., et al., Cigarette smoking and the natural history of age-related macular degeneration: the Beaver Dam Eye Study. Ophthalmology, 2014. 121(10): p. 1949-1955.

46. Sui, G.Y., et al., Is sunlight exposure a risk factor for age-related macular degeneration? A systematic review and meta-analysis. Br J Ophthalmol, 2013. 97(4): p. 389-394.

47. Schwartz, S.D., et al., Human embryonic stem cell-derived retinal pigment epithelium in patients with age-related macular degeneration and Stargardt's macular dystrophy: follow-up of two open-label phase 1/2 studies. Lancet, 2015. 385(9967): p. 509-516.

48. Congdon, N., et al., Causes and prevalence of visual impairment among adults in the United States. Arch Ophthalmol, 2004. 122(4): p. 477-485.

49. Caprioli, J. and A. Coleman, Pressure fluctuation. Ophthalmology, 2009. 116(4): p. 817.

50. Nickells, R.W., From ocular hypertension to ganglion cell death: a theoretical sequence of events leading to glaucoma. Can J Ophthalmol, 2007. 42(2): p. 278-287.

51. Quigley, H.A., Reappraisal of the mechanisms of glaucomatous optic nerve damage. Eye, 1987. 1 ( Pt 2): p. 318-322.

52. Quigley, H.A., Glaucoma. Lancet, 2011. 377(9774): p. 1367-1377. 53. Weinreb, R.N. and P.T. Khaw, Primary open-angle glaucoma. Lancet, 2004.

363(9422): p. 1711-1720. 54. Mansouri, K., et al., Assessment of rates of structural change in glaucoma using

imaging technologies. Eye, 2011. 25(3): p. 269-277. 55. Leske, M.C., et al., Factors for glaucoma progression and the effect of treatment:

the early manifest glaucoma trial. Arch Ophthalmol, 2003. 121(1): p. 48-56. 56. Bouhenni, R.A., et al., Animal models of glaucoma. J Biomed Biotechnol, 2012.

2012. 57. Saidha, S., et al., Primary retinal pathology in multiple sclerosis as detected by

optical coherence tomography. Brain, 2011. 134(Pt 2): p. 518-33. 58. Garcia-Martin, E., et al., Ability and reproducibility of Fourier-domain optical

coherence tomography to detect retinal nerve fiber layer atrophy in Parkinson's disease. Ophthalmology, 2012. 119(10): p. 2161-2167.

167

59. Burggraaff, M.C., et al., The clinical spectrum of microcystic macular edema. Invest Ophthalmol Vis Sci, 2014. 55(2): p. 952-961.

60. Porter, J., et al., Monochromatic aberrations of the human eye in a large population. J Opt Soc Am A Opt Image Sci Vis, 2001. 18(8): p. 1793-803.

61. Liang, J., et al., Objective measurement of wave aberrations of the human eye with the use of a Hartmann-Shack wave-front sensor. J Opt Soc Am A Opt Image Sci Vis, 1994. 11(7): p. 1949-1957.

62. Prieto, P.M., et al., Analysis of the performance of the Hartmann-Shack sensor in the human eye. J Opt Soc Am A Opt Image Sci Vis, 2000. 17(8): p. 1388-1398.

63. Thibos, L.N., et al., Statistical variation of aberration structure and image quality in a normal population of healthy eyes. J Opt Soc Am A Opt Image Sci Vis, 2002. 19(12): p. 2329-48.

64. Castejon-Mochon, J.F., et al., Ocular wave-front aberration statistics in a normal young population. Vision Res, 2002. 42(13): p. 1611-7.

65. Thibos, L.N., et al., Theory and measurement of ocular chromatic aberration. Vision Res, 1990. 30(1): p. 33-49.

66. Llorente, L., et al., Aberrations of the human eye in visible and near infrared illumination. Optom Vis Sci, 2003. 80(1): p. 26-35.

67. Fernandez, E., et al., Ocular aberrations as a function of wavelength in the near infrared measured with a femtosecond laser. Opt Express, 2005. 13(2): p. 400-409.

68. Thibos, L.N., A. Bradley, and X.X. Zhang, Effect of ocular chromatic aberration on monocular visual performance. Optom Vis Sci, 1991. 68(8): p. 599-607.

69. Thibos, L.N., et al., The chromatic eye: a new reduced-eye model of ocular chromatic aberration in humans. Appl Opt, 1992. 31(19): p. 3594-3600.

70. Liang, J., D.R. Williams, and D.T. Miller, Supernormal vision and high-resolution retinal imaging through adaptive optics. J Opt Soc Am A Opt Image Sci Vis, 1997. 14(11): p. 2884-2892.

71. Hofer, H., et al., Wavefront sensorless adaptive optics ophthalmoscopy in the human eye. Opt Express, 2011. 19(15): p. 14160-14171.

72. Jian, Y., et al., Wavefront sensorless adaptive optics optical coherence tomography for in vivo retinal imaging in mice. Biomed Opt Express, 2014. 5(2): p. 547-559.

73. Sulai, Y.N. and A. Dubra, Non-common path aberration correction in an adaptive optics scanning ophthalmoscope. Biomed Opt Express, 2014. 5(9): p. 3059-3073.

74. Zhang, Y., S. Poonja, and A. Roorda, MEMS-based adaptive optics scanning laser ophthalmoscopy. Opt Lett, 2006. 31(9): p. 1268-1270.

75. Dubra, A. and Y. Sulai, Reflective afocal broadband adaptive optics scanning ophthalmoscope. Biomed Opt Express, 2011. 2(6): p. 1757-1768.

76. Dalimier, E. and C. Dainty, Comparative analysis of deformable mirrors for ocular adaptive optics. Opt Express, 2005. 13(11): p. 4275-4285.

77. Lombardo, M., et al., Adaptive optics photoreceptor imaging. Ophthalmology, 2012. 119(7): p. 1498-1498 e2.

78. Doble, N., et al., Use of a microelectromechanical mirror for adaptive optics in the human eye. Opt Lett, 2002. 27(17): p. 1537-1539.

79. Manzanera, S., et al., MEMS segmented-based adaptive optics scanning laser ophthalmoscope. Biomed Opt Express, 2011. 2(5): p. 1204-17.

168

80. Vargas-Martin, F., P.M. Prieto, and P. Artal, Correction of the aberrations in the human eye with a liquid-crystal spatial light modulator: limits to performance. J Opt Soc Am A Opt Image Sci Vis, 1998. 15(9): p. 2552-2562.

81. Prieto, P., et al., Adaptive optics with a programmable phase modulator: applications in the human eye. Opt Express, 2004. 12(17): p. 4059-4071.

82. Carroll, J., et al., Recent Advances in Retinal Imaging With Adaptive Optics. Opt Photonics News, 2005. 16(1): p. 36-42.

83. Roorda, A., Applications of adaptive optics scanning laser ophthalmoscopy. Optom Vis Sci, 2010. 87(4): p. 260-8.

84. Born, M. and E. Wolf, Principles of Optics. 6th (corrected) ed. 1980, Oxford: Pergamon Press.

85. Wilson, T., The Role of the Pinhole in Confocal Imaging System, in Handbook of Microscopy, J.B. Pawley, Editor. 1995, Plenum Press: New York.

86. Ooto, S., et al., High-resolution imaging of resolved central serous chorioretinopathy using adaptive optics scanning laser ophthalmoscopy. Ophthalmology, 2010. 117(9): p. 1800-9, 1809 e1-2.

87. Merino, D., et al., Observation of cone and rod photoreceptors in normal subjects and patients using a new generation adaptive optics scanning laser ophthalmoscope. Biomed Opt Express, 2011. 2(8): p. 2189-201.

88. Song, H., et al., Variation of cone photoreceptor packing density with retinal eccentricity and age. Invest Ophthalmol Vis Sci, 2011. 52(10): p. 7376-84.

89. Felberer, F., et al., Lens based adaptive optics scanning laser ophthalmoscope. Opt Express, 2012. 20(16): p. 17297-310.

90. Makiyama, Y., et al., Macular cone abnormalities in retinitis pigmentosa with preserved central vision using adaptive optics scanning laser ophthalmoscopy. PLoS One, 2013. 8(11): p. e79447.

91. Meadway, A., C.A. Girkin, and Y. Zhang, A dual-modal retinal imaging system with adaptive optics. Opt Express, 2013. 21(24): p. 29792-807.

92. Sharma, R., et al., In vivo two-photon imaging of the mouse retina. Biomed Opt Express, 2013. 4(8): p. 1285-93.

93. Gray, D.C., et al., In vivo fluorescence imaging of primate retinal ganglion cells and retinal pigment epithelial cells. Opt Express, 2006. 14(16): p. 7144-7158.

94. Sulai, Y.N. and A. Dubra, Adaptive optics scanning ophthalmoscopy with annular pupils. Biomed Opt Express, 2012. 3(7): p. 1647-1661.

95. Poonja, S., et al., Dynamic visual stimulus presentation in an adaptive optics scanning laser ophthalmoscope. J Refract Surg, 2005. 21(5): p. S575-80.

96. Grieve, K. and A. Roorda, Intrinsic signals from human cone photoreceptors. Invest Ophthalmol Vis Sci, 2008. 49(2): p. 713-9.

97. Yin, L., et al., Imaging light responses of foveal ganglion cells in the living macaque eye. J Neurosci, 2014. 34(19): p. 6596-605.

98. Rossi, E.A. and A. Roorda, The relationship between visual resolution and cone spacing in the human fovea. Nat Neurosci, 2010. 13(2): p. 156-7.

99. Arathorn, D.W., et al., Retinally stabilized cone-targeted stimulus delivery. Opt Express, 2007. 15(21): p. 13731-13744.

100. Bedggood, P., et al., Characteristics of the human isoplanatic patch and implications for adaptive optics retinal imaging. J Biomed Opt, 2008. 13(2): p. 024008.

169

101. Thaung, J., et al., Dual-conjugate adaptive optics for wide-field high-resolution retinal imaging. Opt Express, 2009. 17(6): p. 4454-67.

102. Kempen, J.H., et al., The prevalence of refractive errors among adults in the United States, Western Europe, and Australia. Arch Ophthalmol, 2004. 122(4): p. 495-505.

103. Li, K.Y., P. Tiruveedhula, and A. Roorda, Intersubject variability of foveal cone photoreceptor density in relation to eye length. Invest Ophthalmol Vis Sci, 2010. 51(12): p. 6858-67.

104. Vincent, A., et al., Phenotypic characteristics including in vivo cone photoreceptor mosaic in KCNV2-related "cone dystrophy with supernormal rod electroretinogram". Invest Ophthalmol Vis Sci, 2013. 54(1): p. 898-908.

105. Chen, D.C., et al., High-resolution adaptive optics scanning laser ophthalmoscope with dual deformable mirrors. J Opt Soc Am A Opt Image Sci Vis, 2007. 24(5): p. 1305-1312.

106. Zou, W., X. Qi, and S.A. Burns, Woofer-tweeter adaptive optics scanning laser ophthalmoscopic imaging based on Lagrange-multiplier damped least-squares algorithm. Biomed Opt Express, 2011. 2(7): p. 1986-2004.

107. Li, C., et al., A correction algorithm to simultaneously control dual deformable mirrors in a woofer-tweeter adaptive optics system. Opt Express, 2010. 18(16): p. 16671-84.

108. Chui, T.Y., H. Song, and S.A. Burns, Individual variations in human cone photoreceptor packing density: variations with refractive error. Invest Ophthalmol Vis Sci, 2008. 49(10): p. 4679-87.

109. Hofer, H., et al., Dynamics of the eye's wave aberration. J Opt Soc Am A Opt Image Sci Vis, 2001. 18(3): p. 497-506.

110. Diaz-Santana, L., et al., Benefit of higher closed-loop bandwidths in ocular adaptive optics. Opt Express, 2003. 11(20): p. 2597-2605.

111. Dubra, A., C. Paterson, and C. Dainty, Study of the tear topography dynamics using a lateral shearing interferometer. Opt Express, 2004. 12(25): p. 6278-6288.

112. Dubra, A., Wavefront sensor and wavefront corrector matching in adaptive optics. Opt Express, 2007. 15(6): p. 2762-2769.

113. Song, H., et al., Dual electro-optical modulator polarimeter based on adaptive optics scanning laser ophthalmoscope. Opt Express, 2010. 18(21): p. 21892-904.

114. Zhang, Y. and A. Roorda, Photon signal detection and evaluation in the adaptive optics scanning laser ophthalmoscope. J Opt Soc Am A Opt Image Sci Vis, 2007. 24(5): p. 1276-83.

115. Hammer, D.X., et al., Adaptive optics scanning laser ophthalmoscope for stabilized retinal imaging. Opt Express, 2006. 14(8): p. 3354-3367.

116. Gomez-Vieyra, A., et al., First-order design of off-axis reflective ophthalmic adaptive optics systems using afocal telescopes. Opt Express, 2009. 17(21): p. 18906-18919.

117. Dubra, A. and Y.N. Sulai, First-order design of a reflective viewfinder for adaptive optics ophthalmoscopy. Opt Express, 2012. 20(24): p. 26596-605.

118. Ferguson, R.D., et al., Adaptive optics scanning laser ophthalmoscope with integrated wide-field retinal imaging and tracking. J Opt Soc Am A Opt Image Sci Vis, 2010. 27(11): p. A265-77.

119. Olsen, T., On the calculation of power from curvature of the cornea. Br J Ophthalmol, 1986. 70(2): p. 152-4.

170

120. Kiely, P.M., L.G. Carney, and G. Smith, Diurnal variations of corneal topography and thickness. Am J Optom Physiol Opt, 1982. 59(12): p. 976-82.

121. Guillon, M., D.P. Lydon, and C. Wilson, Corneal topography: a clinical model. Ophthalmic Physiol Opt, 1986. 6(1): p. 47-56.

122. Budak, K., et al., Evaluation of relationships among refractive and topographic parameters. J Cataract Refr Surg, 1999. 25(6): p. 814-20.

123. Stenstrom, S., Investigation of the variation and the correlation of the optical elements of human eyes. Am J Optom Physiol Opt, 1948. 25(5).

124. Rynders, M.C., R. Navarro, and M.A. Losada, Objective measurement of the off-axis longitudinal chromatic aberration in the human eye. Vision Res, 1998. 38(4): p. 513-22.

125. Perez-Merino, P., et al., In vivo chromatic aberration in eyes implanted with intraocular lenses. Invest Ophthalmol Vis Sci, 2013. 54(4): p. 2654-61.

126. Bedford, R.E. and G. Wyszecki, Axial chromatic aberration of the human eye. J Opt Soc Am A Opt Image Sci Vis, 1957. 47(6): p. 564-5.

127. Fernández, E.J., et al., Chromatic aberration correction of the human eye for retinal imaging in the near infrared. Opt Express, 2006. 14(13): p. 6213-6225.

128. Powell, I., Lenses for correcting chromatic aberration of the eye. Appl Opt, 1981. 20(24): p. 4152-5.

129. Zawadzki, R.J., et al., Ultrahigh-resolution optical coherence tomography with monochromatic and chromatic aberration correction. Opt Express, 2008. 16(11): p. 8126-43.

130. Torti, C., et al., Adaptive optics optical coherence tomography at 120,000 depth scans/s for non-invasive cellular phenotyping of the living human retina. Opt Express, 2009. 17(22): p. 19382-400.

131. Harmening, W.M., et al., Measurement and correction of transverse chromatic offsets for multi-wavelength retinal microscopy in the living eye. Biomed Opt Express, 2012. 3(9): p. 2066-2077.

132. Stevenson, S.B. and A. Roorda. Correcting for miniature eye movements in high resolution scanning laser ophthalmoscopy. in Ophthalmic Technologies XV. 2005. San Jose, CA, USA: SPIE.

133. Dubra, A. and Z. Harvey. Registration of 2D images from fast scanning ophthalmic instruments. in The 4th International Workshop on Biomedical Image Registration. 2010. Lubeck, Germany: Springer-Verlag.

134. Wang, Q., et al., Adaptive optics microperimetry and OCT images show preserved function and recovery of cone visibility in macular telangiectasia type 2 retinal lesions. Invest Ophthalmol Vis Sci, 2015. 56(2): p. 778-86.

135. Yang, Q., et al., Closed-loop optical stabilization and digital image registration in adaptive optics scanning light ophthalmoscopy. Biomed Opt Express, 2014. 5(9): p. 3174-91.

136. Miller, D.T., et al., Images of cone photoreceptors in the living human eye. Vision Res, 1996. 36(8): p. 1067-1079.

137. Dubra, A., et al., Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope. Biomed Opt Express, 2011. 2(7): p. 1864-1876.

138. Wilk, M.A., et al., Relationship between foveal cone specialization and pit morphology in albinism. Invest Ophthalmol Vis Sci, 2014. 55(7): p. 4186-98.

171

139. Li, K.Y. and A. Roorda, Automated identification of cone photoreceptors in adaptive optics retinal images. J Opt Soc Am A Opt Image Sci Vis, 2007. 24(5): p. 1358-1363.

140. Cooper, R.F., et al., Automatic detection of modal spacing (Yellott's ring) in adaptive optics scanning light ophthalmoscope images. Ophthalmic Physiol Opt, 2013. 33(4): p. 540-9.

141. Chiu, S.J., et al., Automatic cone photoreceptor segmentation using graph theory and dynamic programming. Biomed Opt Express, 2013. 4(6): p. 924-37.

142. Garrioch, R., et al., Repeatability of in vivo parafoveal cone density and spacing measurements. Optom Vis Sci, 2012. 89(5): p. 632-643.

143. Carroll, J., et al., The effect of cone opsin mutations on retinal structure and the integrity of the photoreceptor mosaic. Invest Ophthalmol Vis Sci, 2012. 53(13): p. 8006-15.

144. Park, S.P., et al., A study of factors affecting the human cone photoreceptor density measured by adaptive optics scanning laser ophthalmoscope. Exp Eye Res, 2013. 108: p. 1-9.

145. Duncan, J.L., et al., High-Resolution Imaging with Adaptive Optics in Patients with Inherited Retinal Degeneration. Invest Ophthalmol Vis Sci, 2007. 48(7): p. 3283-3291.

146. Syed, R., et al., High-resolution images of retinal structure in patients with choroideremia. Invest Ophthalmol Vis Sci, 2013. 54(2): p. 950-61.

147. Yoon, M.K., et al., Adaptive optics scanning laser ophthalmoscopy images in a family with the mitochondrial DNA T8993C mutation. Invest Ophthalmol Vis Sci, 2009. 50(4): p. 1838-47.

148. Cooper, R.F., et al., Spatial and temporal variation of rod photoreceptor reflectance in the human retina. Biomed Opt Express, 2011. 2(9): p. 2577-2589.

149. Pallikaris, A., D.R. Williams, and H. Hofer, The Reflectance of Single Cones in the Living Human Eye. Invest Ophthalmol Vis Sci, 2003. 44(10): p. 4580-4592.

150. Jonnal, R.S., et al., Imaging outer segment renewal in living human cone photoreceptors. Opt Express, 2010. 18(5): p. 5257-70.

151. Masella, B.D., J.J. Hunter, and D.R. Williams, New wrinkles in retinal densitometry. Invest Ophthalmol Vis Sci, 2014. 55(11): p. 7525-34.

152. Godara, P., et al., Assessing retinal structure in complete congenital stationary night blindness and Oguchi disease. Am J Ophthalmol, 2012. 154(6): p. 987-1001 e1.

153. Genead, M.A., et al., Photoreceptor structure and function in patients with congenital achromatopsia. Invest Ophthalmol Vis Sci, 2011. 52(10): p. 7298-308.

154. Sundaram, V., et al., Retinal structure and function in achromatopsia: implications for gene therapy. Ophthalmology, 2014. 121(1): p. 234-45.

155. Hansen, S.O., et al., Selective cone photoreceptor injury in acute macular neuroretinopathy. Retina, 2013. 33(8): p. 1650-8.

156. Mkrtchyan, M., et al., Outer retinal structure in patients with acute zonal occult outer retinopathy. Am J Ophthalmol, 2012. 153(4): p. 757-68, 768 e1.

157. Boretsky, A., et al., In vivo imaging of photoreceptor disruption associated with age-related macular degeneration: A pilot study. Lasers Surg Med, 2012. 44(8): p. 603-10.

172

158. Zayit-Soudry, S., et al., Cone structure imaged with adaptive optics scanning laser ophthalmoscopy in eyes with nonneovascular age-related macular degeneration. Invest Ophthalmol Vis Sci, 2013. 54(12): p. 7498-509.

159. Kay, D.B., et al., Outer retinal structure in best vitelliform macular dystrophy. JAMA ophthalmology, 2013. 131(9): p. 1207-1215.

160. Akagi-Kurashige, Y., et al., Retinal Microstructural Changes in Eyes with Resolved Branch Retinal Vein Occlusion: An Adaptive Optics Scanning Laser Ophthalmoscopy Study. Am J Ophthalmol, 2014.

161. Bae, E.J., et al., Retinal damage in chloroquine maculopathy, revealed by high resolution imaging: a case report utilizing adaptive optics scanning laser ophthalmoscopy. Korean J Ophthalmol, 2014. 28(1): p. 100-7.

162. Ratnam, K., et al., Relationship between foveal cone structure and clinical measures of visual function in patients with inherited retinal degenerations. Invest Ophthalmol Vis Sci, 2013. 54(8): p. 5836-47.

163. Morgan, J.I., et al., High-resolution adaptive optics retinal imaging of cellular structure in choroideremia. Invest Ophthalmol Vis Sci, 2014. 55(10): p. 6381-97.

164. Rossi, E.A., et al., Visual function and cortical organization in carriers of blue cone monochromacy. PLoS One, 2013. 8(2): p. e57956.

165. Carroll, J., et al., Deletion of the X-linked opsin gene array locus control region (LCR) results in disruption of the cone mosaic. Vision Res, 2010. 50(19): p. 1989-99.

166. Wolfing, J.I., et al., High-Resolution Retinal Imaging of Cone–Rod Dystrophy. Ophthalmology, 2006. 113(6): p. 1014-1019.

167. Roorda, A., Y. Zhang, and J.L. Duncan, High-Resolution In Vivo Imaging of the RPE Mosaic in Eyes with Retinal Disease. Invest Ophthalmol Vis Sci, 2007. 48(5): p. 2297-2303.

168. Tam, J., et al., Disruption of the retinal parafoveal capillary network in type 2 diabetes before the onset of diabetic retinopathy. Invest Ophthalmol Vis Sci, 2011. 52(12): p. 9257-66.

169. Tam, J., et al., Subclinical capillary changes in non-proliferative diabetic retinopathy. Optom Vis Sci, 2012. 89(5): p. E692-703.

170. Ooto, S., et al., High-resolution imaging of the photoreceptor layer in epiretinal membrane using adaptive optics scanning laser ophthalmoscopy. Ophthalmology, 2011. 118(5): p. 873-81.

171. Makiyama, Y., et al., Cone Abnormalities in Fundus Albipunctatus Associated With RDH5 Mutations Assessed Using Adaptive Optics Scanning Laser Ophthalmoscopy. Am J Ophthalmol, 2014. 157(3): p. 558-570 e4.

172. Ooto, S., et al., Photoreceptor damage and foveal sensitivity in surgically closed macular holes: an adaptive optics scanning laser ophthalmoscopy study. Am J Ophthalmol, 2012. 154(1): p. 174-186 e2.

173. Yokota, S., et al., Objective assessment of foveal cone loss ratio in surgically closed macular holes using adaptive optics scanning laser ophthalmoscopy. PLoS One, 2013. 8(5): p. e63786.

174. Ooto, S., et al., High-resolution photoreceptor imaging in idiopathic macular telangiectasia type 2 using adaptive optics scanning laser ophthalmoscopy. Invest Ophthalmol Vis Sci, 2011. 52(8): p. 5541-50.

173

175. Ooto, S., et al., Comparison of cone pathologic changes in idiopathic macular telangiectasia types 1 and 2 using adaptive optics scanning laser ophthalmoscopy. Am J Ophthalmol, 2013. 155(6): p. 1045-1057 e4.

176. Duncan, J.L., et al., Identification of a novel mutation in the CDHR1 gene in a family with recessive retinal degeneration. Arch Ophthalmol, 2012. 130(10): p. 1301-8.

177. Duncan, J.L., et al., Cone structure in retinal degeneration associated with mutations in the peripherin/RDS gene. Invest Ophthalmol Vis Sci, 2011. 52(3): p. 1557-66.

178. Vincent, A.L., et al., F45L Allele Does Not Cause Autosomal Dominant Retinitis Pigmentosa in a Large Caucasian Family. Transl Vis Sci Technol, 2013. 2(2): p. 4.

179. Hong, I.H., et al., Cone photoreceptor abnormalities correlate with vision loss in a case of acute posterior multifocal placoid pigment epitheliopathy. Ophthalmic Surg Lasers Imaging, 2014. 45(1): p. 74-8.

180. Talcott, K.E., et al., Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment. Invest Ophthalmol Vis Sci, 2011. 52(5): p. 2219-26.

181. Chen, Y., et al., Cone photoreceptor abnormalities correlate with vision loss in patients with Stargardt disease. Invest Ophthalmol Vis Sci, 2011. 52(6): p. 3281-92.

182. Joeres, S., et al., Retinal imaging with adaptive optics scanning laser ophthalmoscopy in unexplained central ring scotoma. Arch Ophthalmol, 2008. 126(4): p. 543-7.

183. Ooto, S., M. Hangai, and N. Yoshimura, Photoreceptor restoration in unilateral acute idiopathic maculopathy on adaptive optics scanning laser ophthalmoscopy. Arch Ophthalmol, 2011. 129(12): p. 1633-5.

184. Ratnam, K., et al., Cone structure in patients with usher syndrome type III and mutations in the Clarin 1 gene. JAMA ophthalmology, 2013. 131(1): p. 67-74.

185. Duncan, J.L., et al., Abnormal cone structure in foveal schisis cavities in X-linked retinoschisis from mutations in exon 6 of the RS1 gene. Invest Ophthalmol Vis Sci, 2011. 52(13): p. 9614-23.

186. Carroll, J., et al., Adaptive optics retinal imaging--clinical opportunities and challenges. Curr Eye Res, 2013. 38(7): p. 709-21.

187. Grieve, K., et al., Multi-wavelength imaging with the adaptive optics scanning laser Ophthalmoscope. Opt Express, 2006. 14(25): p. 12230-12242.

188. Morgan, J.I., et al., Light-induced retinal changes observed with high-resolution autofluorescence imaging of the retinal pigment epithelium. Invest Ophthalmol Vis Sci, 2008. 49(8): p. 3715-29.

189. Morgan, J.I., et al., The reduction of retinal autofluorescence caused by light exposure. Invest Ophthalmol Vis Sci, 2009. 50(12): p. 6015-22.

190. Delori, F.C., et al., In vivo fluorescence of the ocular fundus exhibits retinal pigment epithelium lipofuscin characteristics. Invest Ophthalmol Vis Sci, 1995. 36(3): p. 718-729.

191. Rossi, E.A., et al., In vivo imaging of retinal pigment epithelium cells in age related macular degeneration. Biomed Opt Express, 2013. 4(11): p. 2527-39.

192. Yin, L., et al., Imaging light responses of retinal ganglion cells in the living mouse eye. J Neurophysiol, 2013. 109(9): p. 2415-21.

174

193. Elsner, A.E., et al., Detecting AMD with multiply scattered light tomography. Int Ophthalmol, 2001. 23(4-6): p. 245-50.

194. Chui, T.Y., D.A. Vannasdale, and S.A. Burns, The use of forward scatter to improve retinal vascular imaging with an adaptive optics scanning laser ophthalmoscope. Biomed Opt Express, 2012. 3(10): p. 2537-2549.

195. Burns, S.A., et al., In vivo adaptive optics microvascular imaging in diabetic patients without clinically severe diabetic retinopathy. Biomed Opt Express, 2014. 5(3): p. 961-974.

196. Chui, T.Y., et al., Comparison of adaptive optics scanning light ophthalmoscopic fluorescein angiography and offset pinhole imaging. Biomed Opt Express, 2014. 5(4): p. 1173-1189.

197. Carroll, J., et al., Functional photoreceptor loss revealed with adaptive optics: An alternate cause of color blindness. Proc Natl Acad Sci U S A, 2004. 101(22): p. 8461-8466.

198. Carroll, J., S.S. Choi, and D.R. Williams, In vivo imaging of the photoreceptor mosaic of a rod monochromat. Vision Res, 2008. 48(26): p. 2564-2568.

199. Flatter, J.A., et al., Outer retinal structure after closed-globe blunt ocular trauma. Retina, 2014. 34(10): p. 2133-46.

200. Zhong, Z., et al., In vivo measurement of erythrocyte velocity and retinal blood flow using adaptive optics scanning laser ophthalmoscopy. Opt Express, 2008. 16(17): p. 12746-56.

201. Wang, Q., et al., Imaging retinal capillaries using ultrahigh-resolution optical coherence tomography and adaptive optics. Invest Ophthalmol Vis Sci, 2011. 52(9): p. 6292-9.

202. Bedggood, P. and A. Metha, Direct visualization and characterization of erythrocyte flow in human retinal capillaries. Biomed Opt Express, 2012. 3(12): p. 3264-77.

203. Kurokawa, K., et al., Three-dimensional retinal and choroidal capillary imaging by power Doppler optical coherence angiography with adaptive optics. Opt Express, 2012. 20(20): p. 22796-812.

204. Dubis, A.M., et al., Relationship between the foveal avascular zone and foveal pit morphology. Invest Ophthalmol Vis Sci, 2012. 53(3): p. 1628-36.

205. Chui, T.Y., et al., Foveal avascular zone and its relationship to foveal pit shape. Optom Vis Sci, 2012. 89(5): p. 602-10.

206. Sulai, Y.N., et al., Visualization of retinal vascular structure and perfusion with a nonconfocal adaptive optics scanning light ophthalmoscope. J Opt Soc Am A Opt Image Sci Vis, 2014. 31(3): p. 569-579.

207. Hirsch, J. and C.A. Curcio, The spatial resolution capacity of human foveal retina. Vision Res, 1989. 29(9): p. 1095-1101.

208. Tanna, H., et al., Retinal imaging using commercial broadband optical coherence tomography. Br J Ophthalmol, 2010. 94(3): p. 372-6.

209. ANSI, American National Standard for Safe Use of Lasers in Research, Development, or Testing. 2007, Laser Institute of America: Orlando, FL.

210. Delori, F.C., R.H. Webb, and D.H. Sliney, Maximum permissible exposures for ocular safety (ANSI 2000), with emphasis on ophthalmic devices. J Opt Soc Am A Opt Image Sci Vis, 2007. 24(5): p. 1250-65.

211. Wilson, T. and A.R. Carlini, Size of the detector in confocal imaging systems. Opt Lett, 1987. 12(4): p. 227-9.

175

212. Li, Z.Y., D.E. Possin, and A.H. Milam, Histopathology of bone spicule pigmentation in retinitis pigmentosa. Ophthalmology, 1995. 102(5): p. 805-16.

213. Feeney, L., Lipofuscin and melanin of human retinal pigment epithelium. Fluorescence, enzyme cytochemical, and ultrastructural studies. Invest Ophthalmol Vis Sci, 1978. 17(7): p. 583-600.

214. Burns, S.A., et al., Imaging the Inner Retina Using Adaptive Optics. Invest Ophthalmol Vis Sci, 2008. 49(5): p. 4512-.

215. Gunn, R.M. and E.A. Schafer, A Contribution to the Minute Anatomy of the Human Retina. J Anat Physiol, 1877. 11(Pt 3): p. 357-516.

216. Ballantyne, A.J., The Reflexes of the Fundus Oculi: (Section of Ophthalmology). Proc R Soc Med, 1940. 34(1): p. 19-42.

217. Sakamoto, T. and T. Ishibashi, Hyalocytes: essential cells of the vitreous cavity in vitreoretinal pathophysiology? Retina, 2011. 31(2): p. 222-8.

218. Kohno, R.I., et al., Possible contribution of hyalocytes to idiopathic epiretinal membrane formation and its contraction. Br J Ophthalmol, 2009. 93(8): p. 1020-6.

219. Schumann, R.G., et al., Immunocytochemical and ultrastructural evidence of glial cells and hyalocytes in internal limiting membrane specimens of idiopathic macular holes. Invest Ophthalmol Vis Sci, 2011. 52(11): p. 7822-34.

220. Oberstein, S.Y., et al., Cell proliferation in human epiretinal membranes: characterization of cell types and correlation with disease condition and duration. Mol Vis, 2011. 17: p. 1794-805.

221. Klein, R., et al., The epidemiology of epiretinal membranes. Trans Am Ophthalmol Soc, 1994. 92: p. 403-25; discussion 425-30.

222. Heegaard, S., et al., An unusual retinal vascular morphology in connection with a novel AIPL1 mutation in Leber's congenital amaurosis. Br J Ophthalmol, 2003. 87(8): p. 980-3.

223. Gills, J.P., Jr. and J.A. Wadsworth, Degeneration of the inner nuclear layer of the retina following lesions of the optic nerve. Trans Am Ophthalmol Soc, 1966. 64: p. 66-88.

224. Gelfand, J.M., et al., Microcystic macular oedema in multiple sclerosis is associated with disease severity. Brain, 2012. 135(Pt 6): p. 1786-93.

225. Langlo, C.S., et al., A lensing effect of inner retinal cysts on images of the photoreceptor mosaic. Retina, 2014. 34(2): p. 421-2.

226. Lujan, B.J., et al., Revealing Henle's fiber layer using spectral domain optical coherence tomography. Invest Ophthalmol Vis Sci, 2011. 52(3): p. 1486-92.

227. Shin, Y.U. and B.R. Lee, Retro-mode Imaging for retinal pigment epithelium alterations in central serous chorioretinopathy. Am J Ophthalmol, 2012. 154(1): p. 155-163 e4.

228. Yamamoto, M., et al., Visualization of cystoid macular oedema using a scanning laser ophthalmoscope in the retro-mode. Clin Experiment Ophthalmol, 2010. 38(1): p. 27-36.

229. Scoles, D., Y.N. Sulai, and A. Dubra, In vivo dark-field imaging of the retinal pigment epithelium cell mosaic. Biomed Opt Express, 2013. 4(9): p. 1710-23.

230. Pircher, M., C.K. Hitzenberger, and U. Schmidt-Erfurth, Polarization sensitive optical coherence tomography in the human eye. Prog Retin Eye Res, 2011. 30(6): p. 431-51.

176

231. Mellem-Kairala, M.B., et al., Improved contrast of peripapillary hyperpigmentation using polarization analysis. Invest Ophthalmol Vis Sci, 2005. 46(3): p. 1099-106.

232. Spitznas, M. and M.J. Hogan, Outer segments of photoreceptors and the retinal pigment epithelium. Interrelationship in the human eye. Arch Ophthalmol, 1970. 84(6): p. 810-9.

233. Bok, D., Retinal photoreceptor-pigment epithelium interactions. Friedenwald lecture. Invest Ophthalmol Vis Sci, 1985. 26(12): p. 1659-94.

234. Steinberg, R.H., Interactions between the retinal pigment epithelium and the neural retina. Doc Ophthalmol, 1985. 60(4): p. 327-46.

235. Simo, R., et al., The retinal pigment epithelium: something more than a constituent of the blood-retinal barrier--implications for the pathogenesis of diabetic retinopathy. J Biomed and Biotech, 2010. 2010: p. 190724.

236. Glazer, L.C. and T.P. Dryja, Understanding the etiology of Stargardt's disease. Ophthalmology, 2002. 15(1): p. 93-100, viii.

237. Blodi, C.F. and E.M. Stone, Best's vitelliform dystrophy. Ophthalmic Paediatr Genet, 1990. 11(1): p. 49-59.

238. Marlhens, F., et al., Mutations in RPE65 cause Leber's congenital amaurosis. Nature Genet, 1997. 17(2): p. 139-41.

239. Greenstein, V.C., et al., Structural and functional changes associated with normal and abnormal fundus autofluorescence in patients with retinitis pigmentosa. Retina, 2012. 32(2): p. 349-57.

240. Schmitz-Valckenberg, S., et al., Fundus autofluorescence imaging: review and perspectives. Retina, 2008. 28(3): p. 385-409.

241. Kellner, U., et al., Lipofuscin- and melanin-related fundus autofluorescence visualize different retinal pigment epithelial alterations in patients with retinitis pigmentosa. Eye, 2009. 23(6): p. 1349-59.

242. Kellner, U., S. Kellner, and S. Weinitz, Fundus autofluorescence (488 NM) and near-infrared autofluorescence (787 NM) visualize different retinal pigment epithelium alterations in patients with age-related macular degeneration. Retina, 2010. 30(1): p. 6-15.

243. Spaide, R.F. and C.A. Curcio, Anatomical correlates to the bands seen in the outer retina by optical coherence tomography: literature review and model. Retina, 2011. 31(8): p. 1609-19.

244. Fleckenstein, M., et al., High-resolution spectral domain-OCT imaging in geographic atrophy associated with age-related macular degeneration. Invest Ophthalmol Vis Sci, 2008. 49(9): p. 4137-44.

245. Morgan, J.I.W., et al., Light-Induced Retinal Changes Observed with High-Resolution Autofluorescence Imaging of the Retinal Pigment Epithelium. Invest Ophthalmol Vis Sci, 2008. 49(8): p. 3715-3729.

246. Cideciyan, A.V., et al., In vivo dynamics of retinal injury and repair in the rhodopsin mutant dog model of human retinitis pigmentosa. Proc Natl Acad Sci U S A, 2005. 102(14): p. 5233-8.

247. Yoshida, A., et al., Radiating retinal folds detected by scanning laser ophthalmoscopy using a diode laser in a dark-field mode in idiopathic macular holes. Graefes Arch Clin Exp Ophthalmol, 1998. 236(6): p. 445-50.

248. Wayne, R., Light and video microscopy. 2009, Amsterdam ; Boston: Academic Press/Elsevier.

177

249. Stiles, W. and B. Crawford, The luminous efficiency of rays entering the eye pupil at different points. P Roy Soc Lond B Bio, 1933. 112(778): p. 428-450.

250. Baraas, R.C., et al., Adaptive optics retinal imaging reveals S-cone dystrophy in tritan color-vision deficiency. J Opt Soc Am A Opt Image Sci Vis, 2007. 24(5): p. 1438-1447.

251. Dorey, C.K., et al., Cell loss in the aging retina. Relationship to lipofuscin accumulation and macular degeneration. Invest Ophthalmol Vis Sci, 1989. 30(8): p. 1691-9.

252. Wilson, T. and D. Hamilton, Differential amplitude contrast imaging in the scanning optical microscope. Appl Phys B, 1983. 32(4): p. 187-191.

253. Alexander, J.J., et al., Restoration of cone vision in a mouse model of achromatopsia. Nat Med, 2007. 13(6): p. 685-7.

254. Pang, J.J., et al., AAV-mediated cone rescue in a naturally occurring mouse model of CNGA3-achromatopsia. PLoS One, 2012. 7(4): p. e35250.

255. Mancuso, K., et al., Gene therapy for red-green colour blindness in adult primates. Nature, 2009. 461(7265): p. 784-7.

256. Komaromy, A.M., et al., Gene therapy rescues cone function in congenital achromatopsia. Hum Mol Genet, 2010. 19(13): p. 2581-93.

257. Pearson, R.A., et al., Restoration of vision after transplantation of photoreceptors. Nature, 2012. 485(7396): p. 99-103.

258. MacLaren, R.E., et al., Retinal repair by transplantation of photoreceptor precursors. Nature, 2006. 444(7116): p. 203-7.

259. Maclaren, R.E., et al., Retinal gene therapy in patients with choroideremia: initial findings from a phase 1/2 clinical trial. Lancet, 2014.

260. Jacobson, S.G., et al., Identifying photoreceptors in blind eyes caused by RPE65 mutations: Prerequisite for human gene therapy success. Proc Natl Acad Sci U S A, 2005. 102(17): p. 6177-82.

261. Kocaoglu, O.P., et al., Imaging cone photoreceptors in three dimensions and in time using ultrahigh resolution optical coherence tomography with adaptive optics. Biomed Opt Express, 2011. 2(4): p. 748-763.

262. Hamilton, D.K. and C.J.R. Sheppard, Differential phase-contrast in scanning optical microscopy. J Microsc, 1984. 133(11): p. 27-39.

263. Amos, W.B., et al., Re-evaluation of differential phase contrast (DPC) in a scanning laser microscope using a split detector as an alternative to differential interference contrast (DIC) optics. J Microsc, 2003. 210(2): p. 166-175.

264. Calver, R., et al., Peripheral refraction for distance and near vision in emmetropes and myopes. Ophthalmic Physiol Opt, 2007. 27(6): p. 584-593.

265. Mallen, E.A.H. and P. Kashyap, Technical Note: Measurement of retinal contour and supine axial length using the Zeiss IOLMaster. Ophthalmic Physiol Opt, 2007. 27(4): p. 404-411.

266. Ford, T.N., K.K. Chu, and J. Mertz, Phase-gradient microscopy in thick tissue with oblique back-illumination. Nat Methods, 2012. 9(12): p. 1195-7.

267. Curcio, C.A., O. Packer, and R.E. Kalina, A whole mount method for sequential analysis of photoreceptor and ganglion cell topography in a single retina. Vision Res, 1987. 27(1): p. 9-15.

268. Curcio, C.A., et al., Aging of the human photoreceptor mosaic: evidence for selective vulnerability of rods in central retina. Invest Ophthalmol Vis Sci, 1993. 34(12): p. 3278-96.

178

269. Weibel, E.R., Stereological methods. 1979, New York: Academic Press. 270. Packer, O., A.E. Hendrickson, and C.A. Curcio, Photoreceptor topography of the

retina in the adult pigtail macaque (Macaca nemestrina). J Comp Neurol, 1989. 288(1): p. 165-83.

271. Drasdo, N., et al., The length of Henle fibers in the human retina and a model of ganglion receptive field density in the visual field. Vision Res, 2007. 47(22): p. 2901-11.

272. Kolb, H. and P. Gouras, Electron microscopic observations of human retinitis pigmentosa, dominantly inherited. Invest Ophthalmol Vis Sci, 1974. 13(7): p. 487-498.

273. Szamier, R.B. and E.L. Berson, Retinal ultrastructure in advanced retinitis pigmentosa. Invest Ophthalmol Vis Sci, 1977. 16(10): p. 947-962.

274. Gao, J., et al., Progressive photoreceptor degeneration, outer segment dysplasia, and rhodopsin mislocalization in mice with targeted disruption of the retinitis pigmentosa-1 (Rp1) gene. Proc Natl Acad Sci U S A, 2002. 99(8): p. 5698-703.

275. Johnson, P.T., et al., Drusen-associated degeneration in the retina. Invest Ophthalmol Vis Sci, 2003. 44(10): p. 4481-8.

276. Curcio, C.A., N.E. Medeiros, and C.L. Millican, Photoreceptor loss in age-related macular degeneration. Invest Ophthalmol Vis Sci, 1996. 37(7): p. 1236-49.

277. Syed, N., et al., Evaluation of retinal photoreceptors and pigment epithelium in a female carrier of choroideremia. Ophthalmology, 2001. 108(4): p. 711-20.

278. Hood, D.C., et al., The inner segment/outer segment border seen on optical coherence tomography is less intense in patients with diminished cone function. Invest Ophthalmol Vis Sci, 2011. 52(13): p. 9703-9.

279. Thomas, M.G., et al., Early signs of longitudinal progressive cone photoreceptor degeneration in achromatopsia. Br J Ophthalmol, 2012. 96(9): p. 1232-6.

280. Cideciyan, A.V., et al., Human cone visual pigment deletions spare sufficient photoreceptors to warrant gene therapy. Hum Gene Ther, 2013. 24(12): p. 993-1006.

281. Takayama, K., et al., Three-Dimensional Imaging of Lamina Cribrosa Defects in Glaucoma Using Swept-Source Optical Coherence Tomography. Invest Ophthalmol Vis Sci, 2013. 54(7): p. 4798-4807.

282. Lee, E.J., et al., Three-dimensional evaluation of the lamina cribrosa using spectral-domain optical coherence tomography in glaucoma. Invest Ophthalmol Vis Sci, 2012. 53(1): p. 198-204.

283. Jia, Y., et al., Quantitative optical coherence tomography angiography of choroidal neovascularization in age-related macular degeneration. Ophthalmology, 2014. 121(7): p. 1435-44.

284. Kim, D.Y., et al., Noninvasive imaging of the foveal avascular zone with high-speed, phase-variance optical coherence tomography. Invest Ophthalmol Vis Sci, 2012. 53(1): p. 85-92.

285. Jia, Y., et al., Split-spectrum amplitude-decorrelation angiography with optical coherence tomography. Opt Express, 2012. 20(4): p. 4710-25.

286. Rosen, R.B., et al., Multidimensional en-face OCT imaging of the retina. Opt Express, 2009. 17(5): p. 4112-33.

179

287. Coscas, F., et al., En face enhanced depth imaging optical coherence tomography of fibrovascular pigment epithelium detachment. Invest Ophthalmol Vis Sci, 2012. 53(7): p. 4147-51.

288. Lumbroso, B., et al., Morphologic differences, according to etiology, in pigment epithelial detachments by means of en face optical coherence tomography. Retina, 2011. 31(3): p. 553-8.

289. Lehmann, M., et al., Retinal and choroidal changes observed with 'En face' enhanced-depth imaging OCT in central serous chorioretinopathy. Br J Ophthalmol, 2013. 97(9): p. 1181-6.

290. Ferrara, D., et al., En face enhanced-depth swept-source optical coherence tomography features of chronic central serous chorioretinopathy. Ophthalmology, 2014. 121(3): p. 719-26.

291. Wanek, J., et al., Feasibility of a method for en face imaging of photoreceptor cell integrity. Am J Ophthalmol, 2011. 152(5): p. 807-814.

292. Wolff, B., et al., En Face OCT Imaging for the Diagnosis of Outer Retinal Tubulations in Age-Related Macular Degeneration. J Ophthalmol, 2012. 542417.

293. Zweifel, S.A., et al., Outer retinal tubulation: a novel optical coherence tomography finding. Arch Ophthalmol, 2009. 127(12): p. 1596-602.

294. Sallo, F.B., et al., "En face" OCT imaging of the IS/OS junction line in type 2 idiopathic macular telangiectasia. Invest Ophthalmol Vis Sci, 2012. 53(10): p. 6145-52.

295. Oh, J., et al., Photoreceptor inner/outer segment defect imaging by spectral domain OCT and visual prognosis after macular hole surgery. Invest Ophthalmol Vis Sci, 2010. 51(3): p. 1651-8.

296. Gorczynska, I., et al., Projection OCT fundus imaging for visualising outer retinal pathology in non-exudative age-related macular degeneration. Br J Ophthalmol, 2009. 93(5): p. 603-9.

297. Wojtkowski, M., et al., Comparison of reflectivity maps and outer retinal topography in retinal disease by 3-D Fourier domain optical coherence tomography. Opt Express, 2009. 17(5): p. 4189-207.

298. Nunes, R.P., et al., Predicting the progression of geographic atrophy in age-related macular degeneration with SD-OCT en face imaging of the outer retina. Ophthalmic Surg Lasers Imaging, 2013. 44(4): p. 344-359.

299. A Phase 2 Multicenter Randomized Clinical Trial of Ciliary Neurotrophic Factor (CNTF) for Macular Telangiectasia Type 2 (MacTel). 2014.

300. Kitiratschky, V.B., et al., Mutation analysis identifies GUCY2D as the major gene responsible for autosomal dominant progressive cone degeneration. Invest Ophthalmol Vis Sci, 2008. 49(11): p. 5015-23.

301. Yannuzzi, L.A., et al., Idiopathic macular telangiectasia. Arch Ophthalmol, 2006. 124(4): p. 450-60.

302. Staurenghi, G., et al., Proposed Lexicon for Anatomic Landmarks in Normal Posterior Segment Spectral-Domain Optical Coherence Tomography: The IN*OCT Consensus. Ophthalmology, 2014.

303. Marmor, M.F., et al., ISCEV Standard for full-field clinical electroretinography (2008 update). Doc Ophthalmol, 2009. 118(1): p. 69-77.

304. Rangaswamy, N.V., et al., A comparison of visual field sensitivity to photoreceptor thickness in retinitis pigmentosa. Invest Ophthalmol Vis Sci, 2010. 51(8): p. 4213-9.

180

305. Park, S.J., et al., Morphologic photoreceptor abnormality in occult macular dystrophy on spectral-domain optical coherence tomography. Invest Ophthalmol Vis Sci, 2010. 51(7): p. 3673-9.

306. Spaide, R.F., H. Koizumi, and K.B. Freund, Photoreceptor outer segment abnormalities as a cause of blind spot enlargement in acute zonal occult outer retinopathy-complex diseases. Am J Ophthalmol, 2008. 146(1): p. 111-20.

307. Sano, M., et al., Restored photoreceptor outer segment and visual recovery after macular hole closure. Am J Ophthalmol, 2009. 147(2): p. 313-318.

308. Chang, L.K., H. Koizumi, and R.F. Spaide, Disruption of the photoreceptor inner segment-outer segment junction in eyes with macular holes. Retina, 2008. 28(7): p. 969-75.

309. Wakabayashi, T., et al., Foveal microstructure and visual acuity in surgically closed macular holes: spectral-domain optical coherence tomographic analysis. Ophthalmology, 2010. 117(9): p. 1815-24.

310. Panorgias, A., et al., Multimodal assessment of microscopic morphology and retinal function in patients with geographic atrophy. Invest Ophthalmol Vis Sci, 2013. 54(6): p. 4372-84.

311. Kocaoglu, O.P., et al., Adaptive optics optical coherence tomography with dynamic retinal tracking. Biomed Opt Express, 2014. 5(7): p. 2262-84.

312. Felberer, F., et al., Adaptive optics SLO/OCT for 3D imaging of human photoreceptors in vivo. Biomed Opt Express, 2014. 5(2): p. 439-56.

313. Lee, S.-H., J.S. Werner, and R.J. Zawadzki, Improved visualization of outer retinal morphology with aberration cancelling reflective optical design for adaptive optics - optical coherence tomography. Biomed Opt Express, 2013. 4(11): p. 2508-2517.

314. Gass, J.D.M., Best's Disease, in Stereoscopic atlas of macular diseases; a funduscopic and angiographic presentation. 1997, C. V. Mosby Co.: Saint Louis,. p. 304-311.

315. Boon, C.J., et al., Autosomal recessive bestrophinopathy: differential diagnosis and treatment options. Ophthalmology, 2013. 120(4): p. 809-820.

316. Marquardt, A., et al., Mutations in a novel gene, VMD2, encoding a protein of unknown properties cause juvenile-onset vitelliform macular dystrophy (Best's disease). Hum Mol Genet, 1998. 7(9): p. 1517-25.

317. Petrukhin, K., et al., Identification of the gene responsible for Best macular dystrophy. Nat Genet, 1998. 19(3): p. 241-247.

318. Stone, E.M., et al., Genetic linkage of vitelliform macular degeneration (Best's disease) to chromosome 11q13. Nat Genet, 1992. 1(4): p. 246-250.

319. Johnson, A.A., et al., Disease-causing mutations associated with four bestrophinopathies exhibit disparate effects on the localization, but not the oligomerization, of Bestrophin-1. Exp Eye Res, 2014. 121: p. 74-85.

320. Boon, C.J., et al., The spectrum of ocular phenotypes caused by mutations in the BEST1 gene. Prog Retin Eye Res, 2009. 28(3): p. 187-205.

321. Marmorstein, A.D., et al., Bestrophin, the product of the Best vitelliform macular dystrophy gene (VMD2), localizes to the basolateral plasma membrane of the retinal pigment epithelium. Proc Natl Acad Sci U S A, 2000. 97(23): p. 12758-12763.

322. Fischmeister, R. and H.C. Hartzell, Volume sensitivity of the bestrophin family of chloride channels. J Physiol, 2005. 562(Pt 2): p. 477-491.

181

323. Hartzell, H.C., et al., Molecular physiology of bestrophins: multifunctional membrane proteins linked to best disease and other retinopathies. Physiol Rev, 2008. 88(2): p. 639-72.

324. Qu, Z. and H.C. Hartzell, Bestrophin Cl- channels are highly permeable to HCO3. Am J Physiol Cell Physiol, 2008. 294(6): p. C1371-7.

325. Rosenthal, R., et al., Expression of bestrophin-1, the product of the VMD2 gene, modulates voltage-dependent Ca2+ channels in retinal pigment epithelial cells. FASEB J, 2006. 20(1): p. 178-180.

326. Sun, H., et al., The vitelliform macular dystrophy protein defines a new family of chloride channels. Proc Natl Acad Sci U S A, 2002. 99(6): p. 4008-4013.

327. Arden, G.B. and P.A. Constable, The electro-oculogram. Prog Retin Eye Res, 2006. 25(2): p. 207-248.

328. Cross, H.E. and L. Bard, Electro-oculography in Best's macular dystrophy. Am J Ophthalmol, 1974. 77(1): p. 46-50.

329. Frangieh, G.T., W.R. Green, and S.L. Fine, A histopathologic study of Best's macular dystrophy. Arch Ophthalmol, 1982. 100(7): p. 1115-1121.

330. Mullins, R.F., et al., Late development of vitelliform lesions and flecks in a patient with best disease: clinicopathologic correlation. Arch Ophthalmol, 2005. 123(11): p. 1588-1594.

331. O'Gorman, S., et al., Histopathologic findings in Best's vitelliform macular dystrophy. Arch Ophthalmol, 1988. 106(9): p. 1261-1268.

332. Weingeist, T.A., J.L. Kobrin, and R.C. Watzke, Histopathology of Best's macular dystrophy. Arch Ophthalmol, 1982. 100(7): p. 1108-1114.

333. Bakall, B., et al., Enhanced accumulation of A2E in individuals homozygous or heterozygous for mutations in BEST1 (VMD2). Exp Eye Res, 2007. 85(1): p. 34-43.

334. Mullins, R.F., et al., Differential macular and peripheral expression of bestrophin in human eyes and its implication for best disease. Invest Ophthalmol Vis Sci, 2007. 48(7): p. 3372-80.

335. Zhang, Y., et al., Suppression of Ca2+ signaling in a mouse model of Best disease. Hum Mol Genet, 2010. 19(6): p. 1108-18.

336. Kay, C.N., et al., Three-dimensional distribution of the vitelliform lesion, photoreceptors, and retinal pigment epithelium in the macula of patients with best vitelliform macular dystrophy. Arch Ophthalmol, 2012. 130(3): p. 357-364.

337. Ferrara, D.C., et al., Multimodal fundus imaging in Best vitelliform macular dystrophy. Graefes Arch Clin Exp Ophthalmol, 2010. 248(10): p. 1377-1386.

338. Pianta, M.J., et al., In vivo micropathology of Best macular dystrophy with optical coherence tomography. Exp Eye Res, 2003. 76(2): p. 203-11.

339. Querques, G., et al., High-resolution spectral domain optical coherence tomography findings in multifocal vitelliform macular dystrophy. Surv Ophthalmol, 2009. 54(2): p. 311-6.

340. Abramoff, M.D., et al., Human photoreceptor outer segments shorten during light adaptation. Invest Ophthalmol Vis Sci, 2013. 54(5): p. 3721-3728.

341. Glybina, I.V. and R.N. Frank, Localization of multifocal electroretinogram abnormalities to the lesion site: findings in a family with Best disease. Arch Ophthalmol, 2006. 124(11): p. 1593-1600.

182

342. Duncker, T., et al., Quantitative fundus autofluorescence and optical coherence tomography in best vitelliform macular dystrophy. Invest Ophthalmol Vis Sci, 2014. 55(3): p. 1471-1482.

343. Caldwell, G.M., et al., Bestrophin gene mutations in patients with Best vitelliform macular dystrophy. Genomics, 1999. 58(1): p. 98-101.

344. Scoles, D., et al., Assessing photoreceptor stucture associated with ellipsoid zone disruptions visualized with optical coherence tomography. Retina, 2015. In Press.

345. Duncan, J.L., et al., Cone structure in patients with BEST1 mutations. Invest Ophthalmol Vis Sci, 2010. 51: p. ARVO E-Abstract 4328.

346. Zhang, Q., K.W. Small, and H.E. Grossniklaus, Clinicopathologic findings in Best vitelliform macular dystrophy. Graefes Arch Clin Exp Ophthalmol, 2011. 249(5): p. 745-51.

347. McCarty, D.J., et al., Prevalence and associations of epiretinal membranes in the visual impairment project. Am J Ophthalmol, 2005. 140(2): p. 288-94.

348. Ng, C.H., et al., Prevalence and risk factors for epiretinal membranes in a multi-ethnic United States population. Ophthalmology, 2011. 118(4): p. 694-9.

349. Aung, K.Z., et al., The prevalence and risk factors of epiretinal membranes: the Melbourne Collaborative Cohort Study. Retina, 2013. 33(5): p. 1026-34.

350. Kampik, A., Pathology of epiretinal membrane, idiopathic macular hole, and vitreomacular traction syndrome. Retina, 2012. 32 Suppl 2: p. S194-8; discussion S198-9.

351. Hiscott, P.S., et al., Epiretinal membranes in vitro. Trans Ophthalmol Soc U K, 1983. 103 ( Pt 1): p. 89-102.

352. Morino, I., et al., Variation in epiretinal membrane components with clinical duration of the proliferative tissue. Br J Ophthalmol, 1990. 74(7): p. 393-9.

353. Hiscott, P., et al., Pathobiology of epiretinal and subretinal membranes: possible roles for the matricellular proteins thrombospondin 1 and osteonectin (SPARC). Eye, 2002. 16(4): p. 393-403.

354. Rha, J., et al., Spectral Domain Optical Coherence Tomography and Adaptive Optics: Imaging Photoreceptor Layer Morphology to Interpret Preclinical Phenotypes, in Retinal Degenerative Diseases. 2010, Springer: New York. p. 309-316.

355. Bedggood, P. and A. Metha, Optical imaging of human cone photoreceptors directly following the capture of light. PLoS One, 2013. 8(11): p. e79251.

356. Applied Genetic Technologies Corp; National Eye Institute. Clinical and Genetic Characterization of Individuals With Achromatopsia. In: ClinicalTrials.gov [Internet]. Bethesda (MD): National Libarary of Medicine (US). 2000 [cited 2015]. Available from: http://ClinicalTrials.gov/show/NCT01846052 NLM Identifier: NCT01846052

357. Spark Therapeutics; Children's Hospital of Philadelphia; University of Pennsylvania. Safety and Dose Escalation Study of AAV2-hCHM in Subjects With CHM (Choroideremia) Gene Mutations. In: ClinicalTrials.gov [Internet]. Bethesda (MD): National Libarary of Medicine (US). 2000 [cited 2015]. Available from: https://clinicaltrials.gov/ct2/show/NCT02341807 NLM Identifier: NCT02341807

358. Sajdak, B.S., et al., Non-invasive adaptive optics imaging of the ground squirrel retina. Invest Ophthalmol Vis Sci, 2015. 56: p. ARVO E-Abstract 4099.

183

359. Guevara-Torres, A., D.R. Williams, and J.B. Schallek, Imaging translucent cell bodies in the living mouse retina without contrast agents. Biomed Opt Express, 2015. 6(6): p. 2106-2119.