3D Near-infrared Imaging Based on a Single-photon ... · 3D Near-infrared Imaging Based on a...
Transcript of 3D Near-infrared Imaging Based on a Single-photon ... · 3D Near-infrared Imaging Based on a...
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3D Near-infrared Imaging Based on a
Single-photon Avalanche Diode Sensor
Juan Mata Pavia
Cristiano Niclass
Martin Wolf
Edoardo Charbon
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Experimental Techniques
Continuous wave (CW)
Time domain (TD)
Phase
I0
I
Frequency domain (FD)
time
I0
I
I0
time
tissueinitial signal
detected signal
time
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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]
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Single-photon avalanche diode (SPAD)
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New 3D NIRI system:
• High resolution images: <1cm
• Almost real time operation
• Bedside applicable
SPAD imagers offer:
• High resolution images
• Time resolved measurements
Objective
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SPAD Image Sensor: LASP Chip
128x128
SPAD array
32 TDCs
100ns range
97ps res.
I/O interface
[Niclass et al JSSC 2008]
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Experimental Setup with Cylindrical Waves I
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Experimental Setup with Cylindrical Waves II
Telecentric
objective
Collimator
& diffuser
Collimator
& diffuserIntralipid phantom
SPAD sensor &
acquisition electronics
4 cm
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Phantom Measurements I
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Phantom Measurements II
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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
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Experimental Results: Setup 1
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Experimental Results: Reconstruction 1
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Experimental Results: Setup 2
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Experimental Results: Reconstruction 2
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Experimental Results: Setup 3
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Experimental Results: Reconstruction 3
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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
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Thanks to
Oliver SieberThomas MühlemannMartin BiallasAndreas MetzDamien de CourtenFelix ScholkmannSonja SpichtigIvo TrajkovicDaniel OstojicChristoph KuhnHans-Ulrich BucherDominik MartiMartin Frenz
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Questions ?
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Outline
Introduction
Imaging system
Image reconstruction
Preliminary results
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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
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Experimental Techniques
I0
IContinuous wave (CW)
I0
time
Time domain (TD)
tissueinitial signal
detected signal
time
Phase
I0
I
Frequency domain (FD)
time
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Scattering
Random walk theory
Photon random walk step in biological tissue ~1mm
Photon
Non-scattering medium Biological tissue
1mm
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Time Correlated Single Photon Counting (TCSPC)
LaserSubject
Single-photon
detector
Lens
Time-to-digital
converter
Histogram
builder
StartStop
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Applications
Perfusion state of tissue
Tissue oxygenation monitoring
Internal bleeding
Detection of infarcts
Tumor detection and analysis
Viability of tissue
Tissue function
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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]
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SPAD Image Sensor Architecture
[Niclass et al JSSC 2008]
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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]
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Experimental Setup with Cylindrical Waves I
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Trigger Adaptation Circuit
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Experimental Setup with Plane Waves
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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]
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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
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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]
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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]
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BHP-700 time response
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The Diffusion Equation
•Homogeneous medium
•Fourier in the time domain
Using the first Born approximation
Measurement
Object to be reconstructed
Matrix coefficients
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The Diffusion Equation for Plane Waves
Fourier in XY dimensions
Homogeneous field is XY independent
Infinite medium
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The Diffusion Equation for Cylindrical Waves
Fourier in the X dimension
Homogeneous field is X independent
Infinite medium
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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
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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
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Image Reconstruction Testbench
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Simulation Results: ART
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Simulation Results: LSQR
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Simulation Results: SP-LSQR Norm
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Simulation Results with SP-LSQR
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FPGA Architecture for fast data acquisition
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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) ]
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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
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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
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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