3D Near-infrared Imaging Based on a
Single-photon Avalanche Diode Sensor
Juan Mata Pavia
Cristiano Niclass
Martin Wolf
Edoardo Charbon
2
Experimental Techniques
Continuous wave (CW)
Time domain (TD)
Phase
I0
I
Frequency domain (FD)
time
I0
I
I0
time
tissueinitial signal
detected signal
time
3
Near Infrared Imaging (NIRI)
Low spatial resolution (~1cm resolution)
3D images require long acquisition times
CW systems can be miniaturized
TD and FD systems are bulky
Low number of sources/detectors
[Wells K et al Proc. SPIE 1997][Muehlemann T et al Opt.
Express 2008]
4
Single-photon avalanche diode (SPAD)
5
New 3D NIRI system:
• High resolution images: <1cm
• Almost real time operation
• Bedside applicable
SPAD imagers offer:
• High resolution images
• Time resolved measurements
Objective
6
SPAD Image Sensor: LASP Chip
128x128
SPAD array
32 TDCs
100ns range
97ps res.
I/O interface
[Niclass et al JSSC 2008]
7
Experimental Setup with Cylindrical Waves I
8
Experimental Setup with Cylindrical Waves II
Telecentric
objective
Collimator
& diffuser
Collimator
& diffuserIntralipid phantom
SPAD sensor &
acquisition electronics
4 cm
9
Phantom Measurements I
10
Phantom Measurements II
11
The Diffusion Equation
•Homogeneous medium
•Fourier in the time domain
•Born approximation
Measurement
Object to be reconstructed
Matrix coefficients
tts
2 ω,rUrO=ω,rUk+
0
2
'rdω,'rU'rOω,'r,rG=ω,rUttts
0
txtxyzsx
2
yzω,rUrOF=ω,ω,rUwk+
0
22
yztyzxyztxyzyztxyzs'rdω,'rUω,'rOω,ω,'r,rG=ω,ω,rU
0
Fourier in the X dimension
X independent
The triple integral is reduced to a double integral
Measurement
Object to be reconstructed
Photon density in the
homogeneous medium
12
Experimental Results: Setup 1
13
Experimental Results: Reconstruction 1
14
Experimental Results: Setup 2
15
Experimental Results: Reconstruction 2
16
Experimental Results: Setup 3
17
Experimental Results: Reconstruction 3
18
Summary & Outlook
Design a NIRI system based on a SPAD image
sensor
Develop the image reconstruction algorithm
Build the system
Performance evaluation
• Design a new SPAD image sensor
• Pre-clinical trials
• SPADs imagers make possible time-resolved high spatial resolution measurements for 3D NIRI
19
Thanks to
Oliver SieberThomas MühlemannMartin BiallasAndreas MetzDamien de CourtenFelix ScholkmannSonja SpichtigIvo TrajkovicDaniel OstojicChristoph KuhnHans-Ulrich BucherDominik MartiMartin Frenz
Questions ?
22
Outline
Introduction
Imaging system
Image reconstruction
Preliminary results
23
Absorption
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
600 700 800 900 1000
Wavelength [nm]
O2H
b,
HH
b [
1/(
mM
*mm
)]
0
0.01
0.02
0.03
0.04
0.05
0.06
H2O
, L
ipid
[1
/mm
]
HHb
O2Hb
Lipid
H2O
24
Experimental Techniques
I0
IContinuous wave (CW)
I0
time
Time domain (TD)
tissueinitial signal
detected signal
time
Phase
I0
I
Frequency domain (FD)
time
25
Scattering
Random walk theory
Photon random walk step in biological tissue ~1mm
Photon
Non-scattering medium Biological tissue
1mm
26
Time Correlated Single Photon Counting (TCSPC)
LaserSubject
Single-photon
detector
Lens
Time-to-digital
converter
Histogram
builder
StartStop
27
Applications
Perfusion state of tissue
Tissue oxygenation monitoring
Internal bleeding
Detection of infarcts
Tumor detection and analysis
Viability of tissue
Tissue function
28
SPAD Image Sensor: LASP Chip
CMOS 0.35µm technology
3.2mm x 3.2mm active area
Fill factor 6%
Microlenses improve the fill factor up to 50%
128x128
SPAD
array
32 TDCS
100ns range
97ps res.
I/O
in
terf
ac
e
[Niclass et al JSSC 2008]
29
SPAD Image Sensor Architecture
[Niclass et al JSSC 2008]
30
Laser: Becker & Hickl BHL-700
780nm wavelength
Repetition rate 80MHz (fixed)
Pulse width ~100ps at 1mW
0.2mW to 10mW adjustable average CW power
300mW typical peak power
[www.becker-hickl.de]
31
Experimental Setup with Cylindrical Waves I
32
Trigger Adaptation Circuit
33
Experimental Setup with Plane Waves
34
Results with Plane Waves
Time gating-window (GW) does not improve the IRF
SPAD’s Impulse Response
Function (IRF) is too slow
[Torricelli et al. 2005]
[Niclass C et al JSSC 2008]
[Schwartz D et al JSSC 2008]
35
Time-to-digital Converter Linearity
0 128 256 384 512 640 768 896 1024-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
TDC Differential Non-linearity
No dithering
Dithering
Improved dithering
TDC Code
LS
B
0 128 256 384 512 640 768 896 1024-3.0
-2.0
-1.0
0.0
1.0
2.0
TDC Integral Non-linearity
No dithering
Dithering
Improved dithering
TDC Code
LS
B
36
Detector: Photon Detection Probability (PDP)
PDP increases with the excess bias voltage, but so does the DCR.
The shallow p+ / n-well junction explains why the SPAD is more effective at blue/UV wavelengths than at red/IR.
[Niclass C et al JSSC 2008]
37
Dark Count Rate (DCR)
Pulses generated in the SPAD in the absence of light.
Causes:
• Thermal generation of carriers
• Electron-hole generation due to tunneling effects.
[Niclass C et al JSSC 2008]
[Niclass C et al JSSC 2008]
38
BHP-700 time response
39
The Diffusion Equation
•Homogeneous medium
•Fourier in the time domain
Using the first Born approximation
Measurement
Object to be reconstructed
Matrix coefficients
40
The Diffusion Equation for Plane Waves
Fourier in XY dimensions
Homogeneous field is XY independent
Infinite medium
41
The Diffusion Equation for Cylindrical Waves
Fourier in the X dimension
Homogeneous field is X independent
Infinite medium
42
Reconstruction Algorithm
Semi-infinite medium: the method of images can be applied
Multiple sources: superposition principle
TD measurements: high number of equations for only one acquisition
43
The Inverse Problem: Regularization
The problem is ill-posed by its nature.
Regularization defines restrictions in the solution’s complexity: smoothness, norm …
Tikhonov regularization: equivalent to minimize:
Linear operator
Problem's solution (object
to be reconstructed)Matrix to
be inverted
Regularization parameter
Measurement
Sub-space preconditioned LSQR (SP-LSQR):
• Iterative method
• Tikhonov regularization
• Predefined sub-space of possible solutions
44
Image Reconstruction Testbench
45
Simulation Results: ART
46
Simulation Results: LSQR
47
Simulation Results: SP-LSQR Norm
48
Simulation Results with SP-LSQR
49
FPGA Architecture for fast data acquisition
50
High definition imaging with NIR
CW systems with CCD cameras.
Long acquisition times: Full scan of the object.
Low depth resolution.
Only applied to small spaces. E.g. small animal imaging.
[A. Martin, Mol. Img. (2008) ]
[A. Martin, Mol. Img. (2009) ]
51
Future Modifications of the SPAD Image Sensor
Solve the Linearity problem in the TDC
Reduce the total time range of the TDC
Increase the number of pixels
Modify the TDC/pixel clustering to reduce the number of acquisitions per frame
52
Outlook
Design a NIRI system based on a SPAD image sensor
Develop an image reconstruction algorithm based on the system
Build a system to perform measurements on phantoms
Evaluate the performance of the new setup
Design a customized SPAD image sensor
Pre-clinical trials
53
Conclusions
SPADs enable the acquisition of time-resolved measurements with high spatial resolution for NIRI
They make possible the development of more efficient algorithms:
• Higher resolution images
• Less computation power
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