Quantitative analysis of multi-temporal and multi-modal in-vivo images in small animal models

[email protected]

06/10/2009

Ir. Janaki Raman RangarajanPromoter: Prof. Dr. Ir. Frederik Maes

Quantitative analysis of multi-temporal and multi-modal in-vivo images

in small animal models

•

18.12.2008

Janaki Raman R

Slide 2

Overview

• Introduction– Quantitative Image analysis– Small animal models

• Image analysis pipeline:– Multi temporal, multi

modal registration & segmentation methods

– Quantification tools

• Results & Discussion– Applications

• Conclusion– Future work

Image Acquisition (IAq)

Image Pre-processing (IPp)

Image Registration (IRg)

Image Quantification (IQn)

•

30.09.2008

Janaki Raman R

Slide 4

Quantitative image analysis

• Image quantification - From ‘seeing’ to ‘measuring’

Requires object delineation or “image segmentation”

5w PI 8w PI

14w PI 30w PI

•

30.09.2008

Janaki Raman R

Slide 5

Fusion of complimentary information

Requires spatial alignment or “image registration”

5w PI

30w PI

Multi-temporal: Follow up over time

Multi-modal: Anatomical & functional info

•

30.09.2008

Janaki Raman R

Slide 6

In vivo Small Animal Image Analysis

• Image artifacts– RF in homogeneity

• Animal models– Transgenic/Wild-type– Rats - Wistor, SD..– Mice - C57BL6J,

ob/ob, ..

• Images– Group: 10– Sequence : 2– Time point: 5 + 1(exvivo)– Images to analyze: 60 x 2

•

30.09.2008

Janaki Raman R

Slide 7

MoSAIC – QUANTIVIAM collaborations

•

06/10/2009

Janaki Raman R

Slide 9

Image analysis pipeline: ESAT/PSI

• Automated (semi) methods– Segmentation, registration, quantification of small animal images

• Multi-temporal & multi-modal– µMR-µMR, µMR-µMRTemplate, µMR-µPET, …..

• Applications– MRI reporters, Morphological phenotyping, Planning acquisition

•

30.09.2008

Janaki Raman R

Slide 10

PhD Goal

• Develop (semi) automated methods– Segmentation– Registration– Quantification in small animals

• Multi-temporal & multi-modal– µMR-µMR, µMR-µPET, µMR-µCT…..

• Registration– Rigid, affine, non-rigid registration

(mesh..)• Segmentation

– Atlas/Template based segmentation

• Application driven– MR reporters, Fat quantification,

morphological phenotyping, ……

Year 1: Build pipeline, evaluate existing methods

Year 2: Multi-temporal to Multi-modality Rigid to Non-rigid registration

Year 3:Multi-modality extension, Applications

Year 4:Validation, applications, report

•

06/10/2009

Janaki Raman R

Slide 11

Micro-MRI acquisition:

Bruker

conversion:bruker-dicom-

analyze

Bias field correction

Segmentation:Brain mask

Motion correction:

bt. repetitions

Normalization:Follow up to

base line

Co-registration:template / atlas

Atlas based segmentation

Image Analysis Pipeline





Intensity normalization

Segmentation:VOI‘s

Quantification: MR constrast

TOPIM, Jan 2009

•

06/10/2009

Janaki Raman R

Slide 12

Micro-MRI acquisition:Bruker

9.4T

conversion:bruker-dicom-analyze

Image Analysis Pipeline - Acquisition


– Resolution ~156 µm or 32 µm (ex-vivo)

– FOV [3.0x5.0x1.5] cm

@ µNMR lab MoSAIC, KUL

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06/10/2009

Janaki Raman R

Slide 13


Bruker

conversion:bruker-dicom-

analyze



Motion correction:

bt. repetitions

Normalization:Follow up to

base line

Co-registration:template / atlas









Quantification: MR constrast

TOPIM, Jan 2009

•

06/10/2009

Janaki Raman R

Slide 14



Motion correction:

bt. repetitions

Image Analysis PipelineImage Pre-processing

(IPp)

Raw data

Motion corrected,averaged,N=12

• RF field in-homogeneity– Source: RF coil /static field in-homogeneity, patient anatomy or

position

– Effect : Intensity variations of same tissue type

• Derails - segmentation, registration or quantification

• MR Inhomogeneity correction

06/10/2009

Janaki Raman R

Slide 15

Original Corrected(masked)

Bias field

OriginalCorrected

Multiplicative model,3D 4th order polynomial,entropy minimization,mean preserving(Likar et al.)

Intensity distribution

=> Background separation

Speed up from polynomial order 1 to 4

Corrected



Motion correction

bt. repetitions

Image Pre-processing

•

06/10/2009

Janaki Raman R

Slide 16

Multi-temporal study – ROI delineation

Baseline

Follow-up

ROI manually delineated in

baseline/atlas scan and automatically

propagated to co-registered follow-up

scans

allows voxel-wise analysis

MIRIT - affineMaximization of Mutual Information (Collignon A, Maes F, et. al)

•

06/10/2009

Janaki Raman R

Slide 17

Alignment:Follow up to

base line

Normalization:template / atlas


Image registration – Multi-temporal

TOPIM, Jan 2009

Image Registration

Before registration After registration

Baseline

Follow-upImage difference:- different animal position- anatomical difference

5w PI

8w PI

30w PI

Maximization of Mutual Information (Collignon A, Maes F, et. al)- Mutual information of corresponding voxel pairs is maximal if the images are geometrically aligned. -12 parameter(translation/rotation/shear/scaling) affine transformation

3w PI

• Spatial alignment – multi-temporal

06/10/2009

Janaki Raman R

Slide 18

5 months

MI based registration (affine): µMRI-µMRI (after bias correction)

Alignment:Follow up to base line



Image Registration

Rat brain - Sprague Dawley

2 months

•

02/06/2009

Janaki Raman R

Slide 19


Bruker

conversion:bruker-analyze


Multi-modality registration: µPET- µMR






Coregistration/Normalization

MR as prior:PET reco.

(J Nuyts, K.U.L)

Motion correction:

bt. repetitions


QuantificationµPET- µMR


(MR)


• Multi-modal registration

06/10/2009

Janaki Raman R

Slide 20

10 months

2 months

10 months

2 months

Without bias field correction & mask

With bias field correction & mask

J. Nuyts, A. Atre, K.Vunckx Nuclear medicine, KUL

ML reconstruction + MRI (prior) -> Bowscher Reconstruction

• Spatial normalization to template

06/10/2009

Janaki Raman R

Slide 21

Alignment:Follow up to base line



Image Registration

Study

Atlas

MI based registration (affine): µMRI-µMRT (after bias correction)

Atlas

AtlasP. Schweinhardt 2003, rat brain templateMBIRN, MDA 2006, mouse brain template

• Atlas based segmentation

04/01/2010

Janaki Raman R

Slide 22

MBIRN data base – C57BL6 MDA

Control injection

Reference region

Test injection

Study

Atlas

TOPIM, Jan 2009

• Inter-scan intensity normalization

06/10/2009

Janaki Raman R

Slide 23

Control injection Test injection

Reference region

•

02/06/2009

Janaki Raman R

Slide 24


Bruker

conversion:bruker-analyze


Multi-modality registration: µPET- µMR






Coregistration/Normalization

MR as prior:PET reco.

(J Nuyts, K.U.L)

Motion correction:

bt. repetitions


QuantificationµPET- µMR


(MR)


•

06/10/2009

Janaki Raman R

Slide 25

Case Study1: Imaging Neurogenesis

– Viral vector based genetic labeling

• Luciferase (BLI) vs Ferritin (MRI)

G Vande Velde, A. Ibrahimi, V. Baekelandt, Z Debyser

5wPI

Division of Molecular MedicineDivision of Molecular Medicine

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06.10.2009

Janaki Raman R

Slide 26

Viral vector based MRI reporter genesFerritin Study

E.No. Vector Left/control Right

8 LV eGFP eGFP+Ferritin

11 LV eGFP FerrH-I-FerrL eGFP

PBSLentiviral Vector(LV)

Adeno associated Vector(AAV)

NOD-SCID

12 LV eGFP Ferritin

13 LV PBS eGFP

14 AAV PBS Ferritin

15 AAV PBS eGFP-T2A-fLuc

16 AAV eGFP-T2A-fLuc FerrH-T2A-fLuc

17 AAV PBS Medium- OMEM

18 LV eGFP-T2A-fLuc FerrH-T2A-fLuc

19 AAV eGFP FerrH-T2A-fLuc

20 AAV eGFP FerrH-T2A-fLuc

ISMRM, Hawaai Apr 2009; WMIC Sep 2009.

• Image analysis of MRI reporters

• Conventional quantification– Qualitative visual examination– Manual delineation of ROI’s – Parametric maps

• Disadvantages– Manual analysis is tedious and error-prone (user,

artifacts)– Low resolution of parametric maps & hypo-intense

contrast of MRI reporters

06/10/2009

Janaki Raman R

Slide 27

5w PI 8w PI 14w PI 30w PI

• MRI reporter - Pre-processing step

06/10/2009

Janaki Raman R

Slide 28



Source Initial mask Bias field Bias corrected MRI Final mask Brain mask

C57bL6/J black mice exp 15: AAV06c

• MRI reporter - Registration step

06/10/2009

Janaki Raman R

Slide 29

MI based Image alignment & normalization ofC57bL6/J black mice with MBIRN atlas exp 15: AAV06a vs. AAV 06c

• MRI reporter : Quantification step

• Visualization of segmented hypo-intense MR contrast (Ferritin) in axial & coronal planes

06/10/2009

Janaki Raman R

Slide 30

Visualization of segmented hypo-intense MR contrast (Ferritin) in Paxinos reference frame

• Results : LV immune response

06/10/2009

Janaki Raman R

Slide 31

World Molecular Imaging Conference, Sep 2009

• Results : LV immune response

06/10/2009

Janaki Raman R

Slide 32


• Results: LV vs AAV background

06/10/2009

Janaki Raman R

Slide 33


• Results: AAV immune response

06/10/2009

Janaki Raman R

Slide 34


• Conclusions of MRI reporter study

• LV vector contributes significantly to background contrast

• Backgound contrast challenges SNR of potential vector based MR reporters(e.g. Ferritin)

• AAV vector results in very low background contrast in comparison to LV

AAV is promising for other potential MR reporter genes!

• Publications- Ferritin case study– G. Vande Velde, J.R. Rangarajan, T. Dresselaers et. al Evaluation of lentiviral and adeno-associated viral vector systems for ferritin

expression as MRI reporter gene in mouse brain. Journal of NeuroImaging (in preparation)

– G. Vande Velde, J.R. Rangarajan, T. Dresselaers, J. Toelen, Z. Debyser, V. Baekelandt, U. Himmelreich, Quantification of 3D T2*-weighted MR images allows evaluation of different viral vectors for stable MR reporter gene expression in the rodent brain , ISMRM - ESMRMB joint annual eeting, May 1-7, 2010, Stockholm, Sweden (accepted)

– G. Vande Velde, J.R. Rangarajan, T. Dresselaers, A. Ibrahimi, Z. Debyser, V. Baekelandt, U. Himmelreich, Comparison of lentiviral and adeno-associated viral vectors for stable MRI reporter gene expression in the rodent brain, 2009 world molecular imaging congress Sep. 2009, Montréal, Canada

– G. Vande Velde, J.R. Rangarajan, T. Dresselaers, O. Krylyshkina, A. Ibrahimi, Z. Debyser, V. Baekelandt, U. Himmelreich, Evaluation of LV and AAV vector systems for stable delivery of MRI reporter genes to the rodent brain, ISMRM April 2009, Honolulu, Hawaii

– J.R. Rangarajan, G. Vande Velde, U. Himmelreich, T. Dresselaers, C. Casteels, A. Atre, D. Loeckx, J. Nuyts, F. Maes, An image analysis pipeline for quantitative analysis of multi-temporal and multi-modal in vivo small animal images , TOPIM 2009 - dual and innovative imaging modalities, January 26-30, 2009, Les Houches, France

– “Quantitative multi-temporal image analysis of MRI reporters in rodent brain” (in preparation)

06/10/2009

Janaki Raman R

Slide 35

• Longitudinal MPIO quantification

06/10/2009

Janaki Raman R

Slide 37

NeuroImage 2009

• Pilot study – MPIO vs. reporter gene

– MPIO in RMS using the pipeline, on best known protocol– MPIO quantification both within in vivo and ex vivo – Compare MPIO vs. Vector based MRI reporter – In progress :

• Pre-processing (done); Registration & Quantification (ongoing);

06/10/2009

Janaki Raman R

Slide 38

• MPIO @Rostral Migratory System

06/10/2009

Janaki Raman R

Slide 39

Registration to MBIRN MDA atlas, overlayed with labels

• Atlas based segmentation of RMS

06/10/2009

Janaki Raman R

Slide 40

LabelOutline

LabelMask

•

06/10/2009

Janaki Raman R

Slide 41

Case study 2: Multimodal imaging in HR

• Morphological phentoyping– Transgenic (HD)– Wild type (control)

• Multi-temporal quantification– 2M, 5M, 10M, 18M…– Morphological changes

• Within & between phenotypes

• Multi-modal study– µMR-µPET (rigid)– µMR-µMR template (affine,

non-rigid)

Wild type (control) Transgenic (HD)

2 M 5 M 10 M

WT

Tg

µMRI : RARE 3D, T2

µPET : FDG, CB1

HD

von Horsten, S. et al. Hum. Mol. Genet. 2003

18 M

C Casteels, J Nuyts, K Van Laere, Nuclear Medicine, KU Leuven

• Image analysis Huntington rats

06/10/2009

Janaki Raman R

Slide 42

10 months

2 months

10 months

2 months

Casteels C 2006

Casteels C

•

06/10/2009

Janaki Raman R

Slide 43

Multi-temporal, multi-modal registration

MMI based Registration

MR atlas in Paxinos space

A) RIGID(R1) B) AFFINE (R2) NON-RIGID (R3)

C) R1 (R2, R3)

PET MR

• Pre-processing step

06/10/2009

Janaki Raman R

Slide 44

2 months 5 months 10 months 18 months

2 months 5 months 10 months 18 months

No Bias correction

Bias corrected & brain mask

• Multi-temporal spatial alignment in HR

06/10/2009

Janaki Raman R

Slide 45

10 months

2 months

•

06/10/2009

Janaki Raman R

Slide 46

Multi-modal registration: PET-MR

MI based registration (rigid): µPET-µMR Influence of bias field correction and brain mask selection

µMRI µPET (no BFC) µPET (with BFC)

• Morphological phenotyping

06/10/2009

Janaki Raman R

Slide 47

18M

5M

Transgenic – Huntington rats

Wild Type – control group

18M

I > mean + 2*stddev (- not robust )- Good BFC & registration can help better segmentation

• Results

• Initial results– Successful PET- MRI registration– Benefits PET reconstruction– MRI - MRI temporal & template

registration– A good brain mask & bias field

correction(BFC) is important

• In progress– Improve BFC in cooperation with VisionLab– Building individual template/ time point–

affine/NRR– Robust segmentation of both striatum &

ventricles

06/10/2009

Janaki Raman R

Slide 49

• Future work: Image analysis

• Summary– The image analysis pipeline with image registration

framework facilitates quantification of multi-temporal & multi-modal study in small animal models

• Future work• Planning of stereotactic surgery

– Plan optimal trajectory.

• Variability in needle tracts– Possibility of missing anatomical ROI– Trajectory could hit vasculature=> immune response

• MR acquisition– Localize better acquisition plane during image acquisition of

time series images.

04/01/2010

Janaki Raman R

Slide 51

•

04/01/2010

Janaki Raman R

Slide 52

Neuro Anatomical Surgical Planning

• Functional neurosurgery– Anorexia (e.g. Septal

nucleus)

• Neuromodulation– Injections

– Electrical stimulation

– Lesions

• Bleeding– Burr holes (Visible)

– Other brain regions(?)

• Influences..– Systematic unwanted side

effectsSource: U. Himmelreich, KUL

Kris van Kuyck, Bart Nuttin. Lab. of experimental & functional neurosurgery, KUL

• Planning optimal trajectory using MRA

06/10/2009

Janaki Raman R

Slide 53

• Register/Overlay– Anatomical MRI with MRA

– Anatomical MRI with Paxinos Atlas

– Anatomical variability

– Probabilistic atlas of vasculature in PaxinosKris van Kuyck, Bart Nuttin. Lab. of experimental & functional neurosurgery, KUL

•

04/01/2010

Janaki Raman R

Slide 54

Neuronal connectivity @Neuromodulation

1. Optimal trajectory in Paxinos space

• Lesions (e.g. septal nucleus)

2. ROI based image plane positioning

3. Tractography along Neuromodulation regions

Kris van Kuyck, Bart Nuttin. Lab. of experimental & functional neurosurgery, KUL

1. Trajectory: Lesion/Stimulation Electrode

2. ROI: Lesion

3. Fiber tract running along ROI

Investigate

• Registration (ESAT)

• Atlas construction (ESAT)

• Quantification of brain connnectivity(VLAB/ESAT)

•

04/01/2010

Janaki Raman R

Slide 55

3 mm

2 mm

Lateral 1.6 mm Lateral 1.6 mm

1 1

2 2

Localization: SOI in paxinos2MR

SOI – Site of injection

• Planned vs actual trajectory

04/01/2010

Janaki Raman R

Slide 56

• Asymmetric needle tracts

04/01/2010

Janaki Raman R

Slide 57

• Variability in Injection trajectory

• Localization of injection axis in 3D– Principal component analysis on voxel coordinates(P)

– First Eigen vector(A) => axis direction– Visualize within & across group

• Variability of tip of axis, height, radius

04/01/2010

Janaki Raman R

Slide 58

C+max[P.A]

H

C+min[P.A]

C

• Next 3-6 months: Prospective registration

04/01/2010

Janaki Raman R

Slide 59

• Need: Image plane positioning– Localization of region of interests– Define image plane using baseline/atlas as reference

Tripilot 2D 3D MSME DWI

Tripilot 2D 3D MSME DWI

5w PI

8w PI

•

06/10/2009

Janaki Raman R

Slide 60

Acknowledgment

• KU Leuven– Prof. Dr. Ir. F. Maes, Prof. Dr. Ir. P. Suetens– Prof. Dr. U. Himmelreich, Dr. Ir. T. Dresselaers– Prof. Dr. V. Baekelandt, G. Vande Velde– Prof. Dr. Z. Debyser, Dr. A Ibrahimi– Prof. Dr. Ir. J. Nuyts, A. Atre– Prof. Dr. K. Van Laere, C. Casteels

• University of Antwerp– Prof. Dr. A. Van der Linden, Ruth Vreys, Dr. M. Verhoye,

Ir. J. Van Audekerke– Prof. Dr. Ir. J. Sijbers, M. Zhenhua

Quantitative analysis of multi-temporal and multi-modal in-vivo images in small animal models

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