OPTIMIZING NEAR-INFRARED SPECTRAL TOMOGRAPHY FOR DIAGNOSTIC
IMAGING AND MONITORING OF BREAST CANCER TREATMENT
A Thesis
Submitted to the Faculty
in partial fulfillment of the requirements for the
degree of
Doctor of Philosophy
by
YAN ZHAO
Thayer School of Engineering
Dartmouth College
Hanover, New Hampshire
November 2017
Examining Committee:
Chairman_______________________
Shudong Jiang, PhD
Member________________________
Brian W. Pogue, PhD
Member________________________
Keith D. Paulsen, PhD
Member________________________
Bruce J. Tromberg, PhD
___________________
F. Jon Kull, Ph.D.
Dean of Graduate and Advanced Studies
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Abstract
Near-infrared spectral tomography (NIRST) has been intensively investigated for clinical
application in breast imaging, by providing functional information about physiologically
related biomarkers such as oxy- and deoxy-hemoglobin, water, lipid and scatter
component. In this thesis, a series of studies on system development and reconstruction
algorithm were completed to improve the imaging quality of MR-guide NIRST and to
predicate breast cancer response to neo-adjuvant chemotherapy. To optimize image
recovery which maximizes difference between malignant and benign lesions, non-linear
iterative reconstructions of MR-Guided NISRT images were recovered using an L-curve
based algorithm, and applied to clinical trial data. The statistical analyses have shown
that the new approach dramatically improved the statistical significance for
differentiating malignant from benign lesions. While MRI guide NIRST has been utilized
to detect breast cancer, NIRST is also used to predicate and monitor breast tumor
responses in patients with locally advanced breast cancer undergoing neoadjuvant
treatment.
Based on an existing hybrid NIRST system developed at Dartmouth, a compact
and portable NIRST system has been developed for imaging patients in the infusion unit
while patients are awaiting or undergoing infusion. This system can acquire frequency-
domain and continuous-wave data simultaneously at 12 wavelengths in the wavelength
range of 660nm to 1064nm. Novel soft gel based homogenous and heterogamous tissue-
mimicking phantoms with sphere-shape inclusions have been developed, to mimic human
breasts. The phantom experiments indicate that the reconstructed optical images highly
depend on the position of imaging plane, especially in the case of small inclusions.
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Tomographic images of breast collagen content have been recovered for the first time,
and image reconstruction approaches with and without collagen content included have
been validated in simulation studies, which indicate that including collagen content into
the reconstruction procedure can significantly reduce the overestimation in total
hemoglobin, water and lipid, and underestimates in oxygen saturation. A group of 10
normal subjects were imaged, and significantly higher (p<0.05) total hemoglobin and
water were estimated in the high-density relative to low-density groups. The performance
of the NIRST system was validated in an ongoing clinical trial, and the recovered optical
biomarkers were correlated with pathologic response to neoadjuvant chemotherapy.
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Acknowledgements
I would like to thank all of the people who have made my time at Dartmouth such
a great and amazing journey. Without the individuals acknowledged here, I would not
have been able to finish the work presented in this thesis.
First of all, I would like to thank my advisor Prof. Shudong Jiang. She gave me
great support and encouragement over the last five years and played a key role in my
research. Shudong has been a dedicated mentor, taking time out of her busy schedule to
work with me in the lab, having numerous discussions on issues related to the imaging
system, and giving me step-by-step instructions. I am impressed by her enthusiasm in
managing clinical trials and abundance of knowledge. Her patience and valuable advice
helped me stay on the right track and become a qualified researcher. I could not have
found a better research advisor and mentor for my PhD.
My special thanks go to Prof. Brian Pogue, who is the leader of the Optics in
Medicine group. I was brought to the world of biomedical optics by Brian, and inspired
by his tireless work ethic, expertise and kindness. He was always willing to meet with
me, read my work and give me insightful advice.
I am grateful to Prof. Keith Paulsen for his constant support through my PhD
work. Keith has been leading several large research projects simultaneously, and he still
found time to give thorough edits of my papers. I also learned a lot from his constructive
suggestions on my research ideas.
I want to thank Prof. Bruce Tromberg for serving on my committee, and his
thoughtful suggestions on my thesis proposal.
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Though not on this committee, Dr. Scott Davis and Dr. Steven Chad Kanick
deserve my sincere thanks. I had a great time working with them on the dosimetry project
in my first year. I would like to thank Dr. Junqing Xu for her help during my stay in
Xi’an. I learned a lot from the intensive discussions with Dr. Limin Zhang and Dr.
Jinchao Feng on the DRI project. I would like to thank Dr. Michael Mastanduno and Dr.
Fadi El-Ghussein for their guidance and help on the reconstruction program and imaging
system.
I appreciate the help of everyone from the entire Optics in Medicine lab. My
sincere thanks go to the following people: Dr. Kristian Sexton, Dr. Kelly Michaelsen, Dr.
Venkataramanan Krishnaswamy, Dr. Alisha Dsouza, Dr. Rongxiao Zhang, Dr. Robert
Holt, Mingwei Zhou and William Burger.
Finally, I would like to thank my friends and family for providing encouragement
during my PhD study. The solid support and endless love from my parents and girlfriend
Xuan, gives me all the motivation to move forward.
This work has been founded by NIH grant R01 CA069544 and R01 CA176086.
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Table of Contents
Abstract .............................................................................................................................. ii
Acknowledgements .......................................................................................................... iv
Table of Contents ............................................................................................................. vi
List of Tables .................................................................................................................... ix
List of Figures ................................................................................................................... xi
List of Acronyms .......................................................................................................... xxiii
Chapter 1: Introduction ....................................................................................................1
1.1. Project overview ...............................................................................................1 1.2. The current state of clinical breast cancer imaging .........................................1 1.3. NIRS/NIRST imaging of breast cancer ...........................................................4 1.4. NIRST imaging of breast cancer at Dartmouth ...............................................8 1.4.1. Breast cancer diagnosis ............................................................................9 1.4.2. Monitoring treatment response to NAC .................................................10 1.5. Organization of this thesis .............................................................................12
Chapter 2: Theory and Image Reconstruction Methods..............................................14
2.1. Introduction .....................................................................................................14 2.2. Modeling of photon propagation in highly scattering medium .....................17
2.2.1. Optical characteristics of biological tissues ..........................................17 2.2.2. Photon diffusion equation .....................................................................17 2.3. Numerical modeling of the forward problem .................................................19 2.4. Inverse problem solver ....................................................................................20 2.5. Spectral prior reconstruction ...........................................................................23 2.6. Spatial prior reconstruction .............................................................................25 2.6.1. Hard-prior reconstruction......................................................................25 2.6.2. Soft-prior reconstruction .......................................................................29 Chapter 3: Optimization of Image Reconstruction in MRI-guided NIRST for Breast
Cancer Diagnosis .............................................................................................................31
3.1. Introduction .....................................................................................................31 3.2. Optimization of regularization parameter in MRI-guided NIRST for breast cancer diagnosis .....................................................................................................33
3.2.1. Reconstruction and visualization of optical images in MRI-guided NIRST .............................................................................................................33 3.2.2. Fixed regularization parameter of 0.1 and 1 using only amplitude data ........................................................................................................................35 3.2.3. Optimal regularization using only amplitude data................................36 3.2.4. Fixed regularization parameter of 0.1 and 1 using both amplitude and phase data ........................................................................................................38 3.2.5. Optimal regularization parameter using both amplitude and phase data ........................................................................................................................39
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3.2.6. Optimal regularization leads to better separation between malignant and benign lesions ...........................................................................................41 3.2.7. Discussions ...........................................................................................43
3.3. Direct regularization from co-registered anatomical images for MRI guided NIRST image reconstruction .................................................................................47
3.4. Discussions ....................................................................................................51 Chapter 4: A Hybrid Frequency-Domain/Continuous-Wave NIRST System with
Simultaneous Measurements at Twelve Wavelengths .................................................53
4.1. Introduction .....................................................................................................53 4.2. Imaging system and patient exam settings ......................................................56
4.3. Laser source sub-systems ................................................................................58 4.3.1. 6-wavelength FD source module .........................................................58 4.3.2. 6-wavelength CW source module ........................................................60 4.4. Hybrid PMT-PD detector sub-system .............................................................61 4.4.1. Detector array of 15 PMT and PD detectors ........................................61 4.4.2. Calibration of PMT/PD detectors ........................................................62 4.5. Adjustable parallel breast interface .................................................................64 4.5.1. Classical fiber-breast interfaces ...........................................................64 4.5.2. Design of adjustable parallel breast interface ......................................66 4.5.3. Phantom imaging with the parallel breast interface .............................68 4.6. Simultaneous acquisition at twelve FD+CW wavelengths .............................70 4.6.1. Simultaneous acquisition at twelve wavelengths .................................70 4.6.2. Hybrid gain setting of PMT detector ...................................................72 4.6.3. Data acquisition GUI ...........................................................................75 4.7. Systematic characterization of the system ......................................................75 4.7.1. Comparison between sequential and simultaneous acquisitions .........75 4.7.2. Variation of phase and amplitude data.................................................77 Chapter 5: Tissue Simulating Phantoms for NIRST Imaging ....................................79
5.1. Introduction .....................................................................................................79 5.2. Comparison between major tissue mimicking phantoms ..............................80
5.3. Phantom Preparation .......................................................................................83 5.3.1. Preparation of homogenous silicone soft gel phantom .......................83 5.3.2. Preparation of heterogeneous silicone soft gel phantom ....................84 5.4. Characterization of homogenous silicone soft gel phantom ...........................86
5.5. Validating the performance of the NIRST system using heterogeneous breast mimicking phantoms ..............................................................................................91
5.5.1. NIRST imaging of heterogeneous phantoms at different depths .......91 5.5.2. NIRST imaging using partial transmission/reflectance data ..............94
Chapter 6: In vivo Collagen Quantification in Breast Tissue .....................................99
6.1. Introduction .....................................................................................................99 6.2. Simulation .....................................................................................................100
6.2.1. Homogeneous phantom simulation..................................................100 6.2.2. Heterogeneous phantom simulation .................................................102 6.3. In vivo collagen quantification in breast tissue .............................................105 6.3.1. In vivo collagen quantification in normal subjects ..........................105
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6.3.2. In vivo collagen quantification in cancer patients ..........................107 6.4. Discussions ...................................................................................................108 Chapter 7: Imaging Normal Subjects ..........................................................................109
7.1. Introduction ...................................................................................................109 7.2. Imaging setup ................................................................................................110
7.3. Imaging normal subjects with various breast sizes .......................................111 7.4. Continuous imaging of normal subjects .......................................................112 7.5. Intra-subject and inter-subject variations ......................................................113 7.6. Discussions ...................................................................................................116 Chapter 8: Towards monitoring breast cancer response to neoadjuvant
chemotherapy ................................................................................................................118
8.1. Introduction ...................................................................................................118 8.2. Optimal workflow of NIRST breast imaging ..............................................120
8.3. Case studies of imaging breast cancer patients .............................................122 8.3.1. Imaging breast cancer patients .........................................................122 8.3.2. Monitoring breast response to NAC ................................................126 8.4. Discussions ...................................................................................................129 Chapter 9: Conclusions and Future Directions...........................................................132
9.1. Completed work ...........................................................................................132 9.1.1. Optimization of MRI-guided NIRST image reconstruction ............132 9.1.2. A hybrid FD/CW system with simultaneous measurements at twelve wavelengths ................................................................................................133 9.1.3. Silicone soft-gel based tissue mimicking phantoms for NIRST imaging .......................................................................................................133 9.1.4. Collagen quantification using the NIRST system ............................134 9.1.5. Imaging normal subjects ..................................................................134 9.1.6. Imaging breast cancer patients .........................................................135
9.2. Future Directions ..........................................................................................135 9.2.1. Optimization of NIRST system for monitoring patient response to NAC ..........................................................................................................135
9.2.2. Optimization of the sampling geometry in MRI-guided NIRST .....136 9.2.3. Imaging small tumors using MRI-guided NIRST ...........................143 Appendices ......................................................................................................................147
Appendix A: LabVIEW Acquisition Program ............................................................147 Appendix B: Matlab Code ..........................................................................................149 Appendix C: Itemized Components List .....................................................................150
References .......................................................................................................................151
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List of Tables
Table 3.1: Comparison of statistics using fixed regularization of 0.1 and 1, and optimal
regularization. Only amplitude data (AMPL) was used for image reconstruction. ...........38
Table 3.2: Comparison of statistics using fixed regularization of 0.1 and 1, optimal
regularization, and fixed regularization of 0.1 for amplitude and 2 for phase. Both
amplitude and phase data (AMPL/PH) were used for image reconstruction.....................40
Table 3.3: Comparison of the three regularization approaches in all clinical exams,
relative to selected exams when the optimal regularization parameter was found. The
group of all exams included 16 malignant and 9 benign pathology-confirmed diagnoses
whereas the selected exams included 15 malignant and 7 benign cases (3 exams from the
former did not have optimal regularization at the 1st iteration). Both amplitude and phase
data were used during image reconstruction. .....................................................................45
Table 5.1: Comparison of major tissue-mimicking optical phantoms. ..............................80
Table 5.2: Maximum recovered (10-3/mm) at different depths for phantoms with sphere
shape inclusions with diameter of 12mm, 18mm and 24mm. ...........................................93
Table 5.3: Comparison of the reconstructed inclusion/background contrast using different
subsets of measurement data acquired at different depths. Four subsets of measurement
data are compared: (A) full dataset, i.e., both transmission and reflectance; (B) two sides
of reflectance data; (C) upper side of reflectance data and (D) only transmission data. ...97
Table 5.4: Comparison of the sensitivity of the inclusion (%) using different subsets of
measurement data acquired at different depths. Four subsets of measurement data are
compared: (A) full dataset, i.e., both transmission and reflectance; (B) two sides of
reflectance data; (C) upper side of reflectance data and (D) only transmission data. .......98
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Table 7.1: Mean and standard deviation of optical parameters of both sides of the breast.
..........................................................................................................................................114
Table 7.2: Mean, standard deviation and total range of physiological and optical
parameters of 10 normal subjects. ..................................................................................115
Table 8.1: Reconstructed optical contrast in terms of HbT, StO2, water, lipid, SA and SP
for three visits of before treatment, on day 9 of cycle 1, and after treatment, respectively.
..........................................................................................................................................127
Table 8.2: Reconstructed optical contrast in terms of HbT, StO2, water, lipid, SA and SP
for three visits of before treatment, on day 19 of cycle 1, and after therapy, respectively.
..........................................................................................................................................129
Table 9.1: Initial guess of SA and SP for four categories grouped by MRI-identified
breast density. ..................................................................................................................140
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List of Figures
Figure 1.1: Three common types of sources used in diffuse optical imaging. The far-left
figures show the “banana patterns” of light sampling path for transmission and
reflectance geometries. The detected light intensity over time is illustrated for continuous
wave (CW), frequency-domain (FD), and time-domain (TD) measurement in (a), (b) and
(c), respectively [1]. ............................................................................................................5
Figure 1.2: Six NIRS/NIRST systems for breast imaging. (a) The DSP based CW NIRST
system developed at Columbia University. (b) The CW NIRST system developed by
Philips Healthcare. (c) The stand-alone clinical NIRST system developed at University of
Pennsylvania, utilizing a CCD-based heterodyne detection scheme. (d) The DOSI system
developed at University of California, Irvine. (e) Combined US and NIRS systems and a
handheld probe developed at University of Connecticut. (f) The MRI-guided NIRST
system developed at Dartmouth College. ...........................................................................8
Figure 1.3: Images from a patient with a 11x21x14mm biopsy-confirmed Invasive Ductal
Carcinoma (IDC) in her right breast. (a) non-contrast T1 MRI with tumor location
indicated (arrow); (b) Reconstructed images for HbT, (c) StO2, (d) water, (e) lipid, (f)
scattering amplitude, and (g) scattering power is overlaid on the MR scan. The value of
each parameter in the adipose region is suppressed for clarity of visualization. ..............10
Figure 1.4: (a) Reconstructed optical images of a pCR case prior, during and post NAC.
(b) Axial post contrast subtraction MRI before NAC shows and enhancing mass indicated
by arrow [2]. ......................................................................................................................11
Figure 2.1: FEM meshes with nodes and elements created in NIRFAST for three
geometries: (a) 2D circle, (b) 2D football-shape and (c) 3D actual breast. ......................20
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Figure 2.2: Image segmentation in NIRFAST-Slicer. ......................................................25
Figure 2.3: (a) Flowchart outlining the sequence for the optimization algorithm. In (b),
the L2 norm of the prior property error vs. L2 norm of the model error creates the L-
curve of values for each regularization parameter value from 0.001 to 100. The
optimization algorithm is implemented at each iteration, and optimal regularization
parameter from previous iteration is used once the L metric falls behind the threshold. .28
Figure 3.1: Images from a 33-year-old patient with a 11x21x14mm biopsy-confirmed
Invasive Ductal Carcinoma (IDC) in her right breast. (a) non-contrast T1 MRI with tumor
location is indicated (arrow); Reconstructed images of (b) HbT, (c) StO2, (d) water, (e)
lipid, (f) scattering amplitude, and (g) scattering power are overlaid on the MR scan. The
value of each parameter in the adipose region is suppressed for clarity of visualization. 34
Figure 3.2: Tumor-to-adipose contrast in HbT vs. regularization for (a) benign, and (b)
malignant cases. Circles have a regularization of 0.1, and asterisks have a regularization
of 1. Box plots of contrast for the two pathologies are shown with fixed regularizations of
(c) 0.1 and (d) 1 for all patients. ........................................................................................35
Figure 3.3: L-curves are shown for the first 3 iterations of a single case with a malignant
tumor. The regularization was varied from 0.001 to 100 at each iteration, and the optimal
regularization was (a) 0.18, (b) 0.22, and (c) undefined for the three iterations,
respectively. Histograms of optimal regularization parameter for the 1st and 2nd iteration
are inserted to Figs. 3-3(a) and (b), respectively. .............................................................36
Figure 3.4: (a) Log scale of projection error vs. number of iterations. (b) Tumor/adipose
contrast in HbT vs. number of iterations. .........................................................................37
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Figure 3.5: Tumor-to-adipose contrast of HbT vs. regularization for (a) benign conditions
and (b) malignant tumors. Circles have a regularization of 0.1, and asterisks have a
regularization of 1. Box plots of contrast are shown for regularization of (c) 0.1, and (d) 1
for all patients. Both amplitude and phase data were used in the image reconstructions. 38
Figure 3.6: ROC curve for fixed regularization of 1 (black) and 0.1 (blue), and optimal
regularization (red), when HbT and TOI are combined. Both amplitude and phase data
were used for image reconstruction. .................................................................................41
Figure 3.7: Box plots of the contrast for (a) total hemoglobin (HbT), (b) oxygen
saturation (StO2), (c) Tissue optical index (TOI), (d) scattering power (SA), and (e)
scattering power (SP), as recovered using the optimal regularization and amplitude and
phase data. aSignificant difference. ...................................................................................42
Figure 3.8. MR images from a patient with a malignant lesion (20mm 27mm 33mm)
seen on DCE MRI. (a): Screenshot of the Nirview 3D surface rendering of the T1 MRI.
Fiducial markers and fiber bundle positions are shown; (b): Standard T1 image; and (c):
Dynamic contrast-enhanced MRI. ....................................................................................49
Figure 3.9: The reconstructed HbT images overlaid in three planes with x=-100.0, y=-
19.8 and z=-26.6 respectively. (a) Segmented images from corresponding T1 and DCE
images. Optical images reconstructed by hard-prior reconstruction using L-curve based
optimization of regularization parameter (b), and DRI with λ=1 and σg=0.001 (c),
respectively. ......................................................................................................................50
Figure 4.1: The 12-wavelength FD-CW NIRST system. (a) A photo of the NIRST
system. (b) and (c) Recline chair with two groups of fibers in the imaging suite. (d)
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Adjustable fiber-breast interface. (e) Subject being imaged with the system. (f) Surface
image of breast-interface. ..................................................................................................56
Figure 4.2: 6-wavelength FD source module. (a) System diagram; (b) Photo of the sub-
system with major components labeled. (c) Photo of the customized 6-wavlength source
module and multi-channel synthesizer. Raw data acquired from one PMT detector after
heterodyned with reference signal, for f1=100.0004MHz (d), f2=100.0007MHz (e),
f3=100.0011MHz, and mixed signal including frequency components at all three
frequencies (g). .................................................................................................................58
Figure 4.3: 6-wavelength CW source module. (a) System diagram. (b) Photo of the sub-
system with major components labeled. Raw signal acquired from a PD detector with
source light modulated at 30Hz (c), 50Hz (d), 80Hz (e), and mixed signal including all
three modulated laser sources (f). .....................................................................................60
Figure 4.4: Photos of the hybrid detector sub-system. The actual assembly of bottom
plate and hybrid detector array are shown in (a) and (b), respectively [3]. ......................61
Figure 4.5: PMT calibration. Input power (log10) (a) and phase (degree) (b) versus PMT
AC amplitude for different gain settings from 0.5 to 1.1. .................................................63
Figure 4.6: Uncalibrated and calibrated amplitude/phase data. Uncalibrated amplitude
(a1) and phase (a2) versus source-detector distance. Calibrated amplitude (b1) and phase
(b2) versus source-distance distance. ................................................................................64
Figure 4.7: Three typical fiber-breast interfaces: (a) circular interface, (b) dual-plate
interface and (c) triangular interface. ................................................................................65
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Figure 4.8: Adjustable parallel fiber-breast interface. (a) Solidworks file showing
dimensions of the interface. (b) A soft gelatin breast phantom being imaged with the
interface. (c) Corresponding FEM mesh with fibers marked in red circles. .....................68
Figure 4.9: Experimental setup and reconstructed optical images for two heterogeneous
phantoms with 1-inch diameter inclusions. The corresponding interface had deep
curvature (a) and flat curvature (b). For both phantoms, the blood concentrations inside
and outside the inclusion were 1.5% and 1%, respectively. .............................................69
Figure 4.10: System diagram for simultaneous acquisition. FD source module, CW
source module, and data acquisition/processing module are highlighted in blue, green,
and violet blocks, respectively. The flow of low frequency electrical signal, high
frequency electrical signal, and light is shown by the black, blue and red solid lines,
respectively. ......................................................................................................................70
Figure 4.11: Hybrid gain adjustment of PMT detectors. (a) Flow chart illustrating the
hybrid gain adjustment scheme. (b) A photo of the adjustable fiber-breast interface (c)
Corresponding football shape mesh created with 16 fibers assigned along the surface. (d)
Amplitude data acquired at source position #1 using automatic gain adjustment scheme.
(e) Amplitude predicted for the other source-detector pairs, based on the parameters fitted
from (d). The actual amplitude and phase data acquired using the gain from the lookup
table for the rest of source-detector pairs, shown in (f) and (g) respectively. ..................72
Figure 4.12: Standard deviation of amplitude (a) and phase (b) of 30 measurements for
two gain adjustment schemes. ...........................................................................................74
Figure 4.13: LabVIEW GUI for data acquisition, pre-processing and display. ...............75
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Figure 4.14: Reconstructed optical images for the same heterogeneous phantom with a 1-
inch diameter inclusion. The optical images were reconstructed using boundary data
acquired from sequential measurement (a), and simultaneous measurement (b),
respectively. The blood concentrations inside and outside the inclusion were 2% and 1%,
respectively. ......................................................................................................................77
Figure 4.15: Standard deviation of phase (a) and AC amplitude (b) versus AC amplitude
for different gain settings from 0.7 to 1.1. ........................................................................77
Figure 5.1: Major tissue mimicking phantoms developed at Dartmouth. From left to right,
gelatin phantom with ink (a), gelatin phantom with blood (b), resin phantom (c), RTV
silicone phantom (d) and silicone soft gel phantom (e) are presented, respectively. .......81
Figure 5.2: The preparation of silicone soft gel phantom. (a) Base materials of A-341:
Silicone Soft Gel. (b) Silicone coloring materials which are used as absorber/scatter. (c)
The mixing of base and silicone coloring materials. ........................................................83
Figure 5.3: Detailed steps of making breast mimicking phantoms. (a) The base material
was mixed with coloring materials in a food mixer. (b) The mixed solution was poured
into 3D printed molds. (c) Three sphere-shape inclusions were taken from the molds after
curing, with radius of 6mm, 9mm and 12mm, respectively. (d) One sphere-shape
inclusion was fixed inside a large mold, which was filled with mixed solution later. The
optical properties of the inclusion are different from those of the background, in order to
create inclusion/background contrast. (e) A group of heterogeneous breast mimicking
phantoms, with either sphere shape inclusion inside, or cylindrical cavity. .....................86
Figure 5.4: DOSI measurement of a silicone soft gel phantom. (a) Measured (blue points)
and fitted (red line) amplitude and phase at four wavelengths, while the laser modulation
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frequency was scanned from 50 to 400MHz. (b) Measured at four wavelengths (red
points) and fitted scattering spectrum (blue line) using Mie theory. (c) Fitted broadband
absorption spectrum. .........................................................................................................87
Figure 5.5: The fitted a (a) and s (b) at 661nm are plotted with error bars representing
the standard deviation among different measurements, for five phantoms. Five silicone
soft gel phantoms were made using the same recipe, and each phantom was measured 10
times at randomly selected positions on the phantom, using the DOSI system. ..............88
Figure 5.6: Measured a (a) and s (b) at 661nm of one phantom in 10 days. Each time
the same silicone soft gel phantom was measured 10 times at randomly selected positions
on the phantom, using the DOSI system. ..........................................................................88
Figure 5.7: Measured a (a)) and s (b) are plotted versus pink paint concentration for
the group of 0.3ml (blue points) and 0.8ml (black points) white paint, respectively. Two
groups of seven phantoms were made, using 50g of base A and 5g of catalyst B as base
material for each phantom. 0.3ml and 0.8ml of white paint were added into each of the
seven phantoms in the 1st and 2nd group, respectively. An increasing amount of pink
paint, from 0.1ml to 0.7ml with an increment of 0.1ml, was added into corresponding
phantom in each group. Each phantom was measured 5 times. ........................................90
Figure 5.8: The measured a (Fig. 5-8(a)) and s (Fig. 5-8(b)) are plotted versus white
paint concentration for the group of 0.3ml (blue points) and 0.5ml (black points) pink
paint, respectively. Two groups of seven phantoms were made, using 50g of base A and
5g of catalyst B as base material for each phantom. 0.3ml and 0.5ml of pink paint were
added into each of the seven phantoms in the 1st and 2nd group, respectively. An
increasing amount of white paint, from 0.5ml to 1.1 ml with an increment of 0.1ml, was
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added into corresponding phantom in each group. Each phantom was measured 5 times.
............................................................................................................................................91
Figure 5.9: Phantom experiments using a silicone soft gel phantom with a sphere
inclusion. (a) Reconstructed absorption images from the data acquired at different depths
of 0mm, -3mm, -6mm, -9mm, and -12mm, respectively. The depth of 0mm corresponds
to the case where imaging plane was placed across the center of the sphere inclusion. (b)
Profiles of the reconstructed a along the X-axis, crossing the center of the inclusion
projected on the surface, at 830nm. (c) The sphere has a diameter of 24mm. The actual
inclusion/background contrast is 2. ...................................................................................92
Figure 5.10: Phantom experiments using a silicone soft gel phantom with a sphere
inclusion, which has a diameter of 24mm. Reconstructed absorption images from the data
acquired at different depths of -9mm, -6mm, -3mm, 0mm, 3mm, 6mm, 9mm, and 12mm
respectively, using (a) both reflection and transmission data; (b) both sides of reflectance
data; (c) one side (upper side) of reflectance data; and (d) two sides of transmission data.
The plane at 0mm corresponds to the case where imaging plane was placed across the
center of the sphere inclusion. The actual inclusion/background contrast is 2.5. .............95
Figure 6.1: Recovered HbT (a), StO2 (b), water (c) and lipid (d) in a simulated
homogeneous phantom, with collagen content increased from 0 to 10%. .......................101
Figure 6.2: Reconstructed images of a simulated heterogeneous phantom. (a) Images with
true values. The diameter of the circular inclusion is 20 mm. An inclusion/background
contrast of 2 is assigned to HbT, with homogeneous background value of 75%, 45%,
45%, 10%, 0.8 and 0.3 assigned for StO2, water, lipids, collagen, SA and SP,
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respectively. Reconstructed images without collagen (b) and with collagen included (c).
..........................................................................................................................................103
Figure 6.3: Image reconstruction in the presence of collagen, which was not included in
the reconstruction. A similar simulation setup was used as Figure 2, except that the
contrast was assigned in HbT (a), StO2 (b), water (c), and lipid (d), respectively. The
extracted inclusion/background contrast was plotted versus collagen concentration
accordingly. .....................................................................................................................104
Figure 6.4: Contents of breast tissue recovered for HbT (a), StO2 (b), Water (c) and
Lipids (d), with and without collagen included in reconstruction. The radiographic
density type of subject #1 and #2 is heterogeneously dense (HD) and scattered
fibroglandular dense (Scattered), respectively. ...............................................................105
Figure 6.5: MRI T2 images of a patient with invasive cancer in the left breast: (a) Axial
view, (b) sagittal view and (c) coronal view. Reconstructed optical images without (d)
and with (e) collagen included. Recovered optical images are displayed in the same
orientation in (d) and (e) as in (c). ..................................................................................107
Figure 7.1: The setup of human subject imaging. (a) The NIRST system placed outside
the exam/infusion room. (b) Exam room. (c) A female subject being imaged on the left
breast. ..............................................................................................................................110
Figure 7.2: Reconstructed optical images of three normal subjects. Maximum separation
between the two fiber holders in the interface was 63mm (a), 85mm (b), and 40mm (c),
respectively. ....................................................................................................................112
Figure 7.3: Continuous measurements of (a) HbT; (b) StO2; (c) water; (d) lipid; (e) SA
and (f) SP for two normal subjects. ................................................................................113
xx
Figure 7.4: Comparison between radiographic dense and non-dense groups in terms of
HbT (a), StO2 (b), Water (c), Lipid (d), SA (e) and SP (e). ...........................................116
Figure 8.1: Optimal workflow for NIRST patient imaging. ...........................................120
Figure 8.2: Case study #1. Dynamic Contrast Enhanced MR Images (DCE-MRI) of a
patient with invasive cancer in the right breast: (a) Axial view, (b) sagittal view and (c)
coronal view. (d) Recovered optical images of HbT, StO2, water, lipid, SA, and SP for
right breast. .....................................................................................................................122
Figure 8.3: Case study #2. DCE-MRI of a patient with invasive cancer in the right breast:
(a) Axial view, (b) sagittal view and (c) coronal view. (d) Recovered optical images of
HbT, StO2, water, lipid, SA, and SP for right breast. .....................................................123
Figure 8.4: Case study #3. DCE-MRI images of a patient with invasive cancer in the left
breast: (a) Axial view, (b) sagittal view and (c) coronal view. Recovered optical images
of HbT, StO2, water, lipid, SA, and SP for right breast (d). ...........................................124
Figure 8.5: Case study #4. DCE-MRI images of a patient with invasive cancer in the left
breast: (a) Axial view, (b) sagittal view and (c) coronal view. Recovered optical images
of HbT, StO2, water, lipid, SA, and SP for right breast (d). ...........................................125
Figure 8.6: Case study #5. Clinical images of a 63-year-old patient with pathological
confirmed pIR. Postcontrast T2-weighted MRI images prior to initiation: (a) axial view,
(b) sagittal view and (c) coronal view. Pathological findings showed pIR to neoadjuvant
chemotherapy. Reconstructed optical images of HbT (uM), StO2 (%), water (%), lipid
(%), scattering amplitude (SA) and scattering power (SP) before treatment (d), on day 9
of cycle 1, and after therapy (29 days prior to surgery) are shown for abnormal breast.
..........................................................................................................................................126
xxi
Figure 8.7: Case study #6. Clinical images of a 54-year-old patient with radiologic
findings and pIR. Postcontrast T2-weighted MRI images prior to initiation: (a) axial
view, (b) sagittal view and (c) coronal view. Pathological findings showed pIR to
neoadjuvant chemotherapy. Reconstructed optical images of HbT (uM), StO2 (%), water
(%), lipid (%), scattering amplitude (SA) and scattering power (SP) before treatment (d),
on day 19 of cycle 1, and after therapy (24 days prior to surgery) are shown for abnormal
breast. ..............................................................................................................................128
Figure 9.1: Flowchart outlining the sequence for two reconstruction methods. .............137
Figure 9.2: Triangular interface with different sampling geometries. Three strategies of
choosing phase measurements, (a) with full transmission across many sources and
detectors, (b) with just partial reflectance data, and (c) with just partial transmittance data.
Fiber locations are shown as blue dots. ..........................................................................138
Figure 9.3: (a) Illustration of triangular patient interface for optical fiber placement. (b)
Relative difference versus detector number. ...................................................................139
Figure 9.4: (a) Relative difference of optical contrast versus number of FD detectors used
for homogeneous fitting of initial guess. ROC curves with 4 detectors (b), 6 detectors (c)
and 15 detectors (d) for estimating initial guess. ............................................................140
Figure 9.5: ROC curves of AMPL reconstruction (a), Lookup table I (b), and lookup table
II (c) for HbT, and AMPL reconstruction (d), lookup table I (e), and lookup table II (f)
for TOI. ...........................................................................................................................142
Figure 9.6: Equivalent tumor diameter of 12.2mm (a), 37.7mm(b) and 46.8mm (c). Three
regions of tumor, fibroglandular and adipose are represented in white (region 3), yellow
(region 2) and red (region 1), respectively. .....................................................................143
xxii
Figure 9.7: Recovered HbT (black asteroid) of tumor and adipose (black circle) are
plotted versus equivalent tumor contrast, for patient #51 (a) and #30 (b), respectively.
The corresponding tumor sensitivity is represented by red square. ................................144
Figure 9.8: Recovered tumor/adipose contrast versus actual tumor/adipose contrast for
the tumor with equivalent tumor diameter of 8mm, 10mm, 12mm, 20mm and 40mm,
respectively. A fixed regularization parameter of 1 was used in the image reconstruction.
..........................................................................................................................................145
Figure 9.9: Comparison between FD/CW reconstruction (a) and CW reconstruction (b),
with increasing equivalent tumor diameter. The recovered HbT of tumor and adipose are
represented by blue asteroid and red square, respectively. .............................................146
xxiii
List of Acronyms
Continuous Wave CW
Dynamic Contrast Enhanced Magnetic Resonance Imaging DCE-MRI
Diffuse Optical Tomography DOT
Estrogen Receptor ER
Frequency Domain FD
Finite Element Mesh FEM
Deoxygenated hemoglobin Hb
Oxygenated hemoglobin HbO2
Total Hemoglobin HbT
Human Epidermal Growth Factor Receptor 2 HER-2
Invasive Ductal Carcinoma IDC
Locally Advanced Breast Cancer LABC
Magnetic Resonance Imaging MRI
Near Infrared Spectral Tomography NIRST
Near Infrared Spectroscopy NIRS
Neoadjuvant Chemotherapy NAC
Pathologic Complete Response pCR
Pathologic Incomplete Response pIR
Photomultiplier Tube PMT
Photodiode PD
Progesterone Receptor PR
Region of Interest ROI
xxiv
Radiative Transport Equation RTE
Scatter Amplitude SA
Scatter Power SP
Time domain TD
Thayer Formatting Guidelines TFG
Tissue Optical Index TOI
Triple-Negative Breast Cancer TNBC
1
Chapter 1: Introduction
1.1 Project overview
The primary goal of this thesis is to improve the performance of near infrared
spectral tomography (NIRST) for diagnostic imaging and treatment monitoring of breast
cancer, from the perspective of development of both the imaging system design and the
nonlinear image reconstruction methods. Breast cancer is the 2nd most commonly
diagnosed cancer among women in the United States, which presents a constant
challenge for both early diagnosis, management and treatment. In 2017, it is estimated
that 252,710 new cases of invasive breast cancer will be diagnosed in women in the U.S.,
together with 63,410 new cases of non-invasive (in situ) breast cancer. Studies clearly
show that the mortality rate can be reduced by early detection and appropriate treatment
prior to tumor metastasis [4]. Accurate imaging can play a critical role in both diagnosis
and clinical management of breast cancer. This chapter briefly introduces the current
state of clinical breast cancer imaging and the role of optical NIRST imaging.
1.2 The current state of clinical breast cancer imaging
The typical clinical progression of breast cancer includes screening
mammography as the first step in most western countries including the U.S. Patients with
a suspicious screening mammogram will be asked to have callback mammography,
ultrasound, and/or MRI as the second step. Next a biopsy sample would be sent to
pathology and the patients who have been diagnosed with cancer would either be enrolled
in a neoadjuvant chemotherapy or go straight to surgery for mastectomy or lumpectomy.
X-ray mammography has been used as the standard of care for annual breast
cancer imaging screening, which is important since some women with breast cancer do
2
not have symptoms. It is recommended by the American Cancer Society that all women
should begin having yearly mammograms by age 45. Mammography has an overall high
specificity of 97%, but with low sensitivity of 77%, as reported in a randomized multi-
center clinical trial [5]. The performance of X-ray mammography can vary on various
factors such as age, hormonal status, and breast density. Mammographic sensitivity for
breast cancer decreases significantly for women with higher breast density, which are
related to higher increased breast cancer risk [6]. In addition, the ionizing radiation from
X-ray mammography could be another concern for young age women.
Ultrasound (US) imaging is commonly used to evaluate specific abnormalities
discovered either at clinical examination or on mammography [7]. It has different
contrast mechanisms from mammography, which makes it possible to detect small, node-
negative lesions not seen on mammography, especially in the case of imaging dense
breast tissue. It has been used mainly for differentiating cystic from solid lesion, and
imaging young and pregnant women with palpable masses. An example of combining
different contrast mechanisms was shown by Berg et al, where the combination of
ultrasound and mammography had higher sensitivity than ultrasound alone [8]. The real-
time capability of US also makes it an ideal candidate for guiding breast biopsies and
other interventional procedures.
Magnetic Resonance Imaging (MRI) is a rapidly developing imaging technique in
breast imaging, with high spatial and temporal resolutions. The standard breast MRI
protocol includes T1 gradient-echo sequences, T2 sequences, and injected dynamic 3D
sequences. The high spatial resolution makes it possible for morphology-based analysis
of the lesion [9], while the high temporal resolution has been used to provide functional
3
information from the dynamic enhancement curve of the lesions [10]. Several studies
have shown that women at high risk of developing breast cancer will benefit from the
combination of MRI and mammography screening [11-13]. Dynamic contrast-enhanced
MRI (DCE-MRI) has been used in staging local tumor, where the enhancement comes
from various factors such as the amount and relaxivity of contrast agent, T1 contrast of
the pulse sequence, and baseline T1 relaxation time of different tissues [14]. DCE-MRI
has a reported sensitivity of nearly 100% in detecting invasive cancer [15]. Breast cancer
detection and differential diagnosis using MR imaging mainly comes from the angiogenic
activity of cancers. However, angiogenic activity is not only found in malignant tumors,
but also some other conditions. The enhancement in such conditions such as
inflammatory changes will result in a high false-positive rate with relatively low
specificity and lead to unnecessary biopsies.
Besides the indispensable role in detection and diagnosis of breast cancer,
imaging techniques have been widely used in management of clinical treatments. The
heterogeneity of breast cancer is subdivided into three subtypes of breast cancer, ER-
positive/HER2-negative (ER-positive), HER2-positive, and triple-negative. Different
subtypes correspond to various expected outcomes and treatment options [16]. Therefore,
it is of great interest to individualize patient treatment plans at an early stage of treatment
or even before treatment starts. Neoadjuvant chemotherapy (NAC) is used to treat
patients with locally advanced cancers, which account for 6-10% of new cases of breast
cancer [17, 18]. Pathologic response to NAC has been regarded to be the best predictor of
long-term outcome [19]. Conventional imaging methods including ultrasonography and
mammography were found only moderately useful for monitoring neoadjuvant
4
chemotherapy [20]. DCE-MRI and Fluorine 18 fluorodeoxyglucose positron emission
tomography (FDG-PET) have been successfully used by several groups to quantify
changes of breast tumors during treatment [21-23]. In a clinical trial involving 28 patients
reported by Ah-See [24], significant correlation was found between DCE-MRI kinetic
features and final clinical and pathologic response (p<0.01). Berriolo-Riedinger [25]
showed that the relative decrease in FDG uptake after the first course of NAC was a
significant indicator (p < 0.000066) in predicting patient response. However, both MRI
and PET imaging require the injection of contrast agents and the cost of the procedures
could be prohibitive.
1.3 NIRS/NIRST imaging of breast cancer
The earliest utilization of light in breast imaging can date back to 1929, when
Cutler first used continuous illumination of the breast to produce shadow images, which
was later called “diaphanography” [26] and briefly commercialized in the 1980s. In
recent decades, the development of breast optical imaging modalities has focused on the
investigation of spectroscopy/imaging using near infrared light. Near infrared
spectroscopy (NIRS) imaging is an emerging functional technique, which estimates the
intrinsic biophysical composition of tissue, in terms of the concentrations of total
hemoglobin and oxy-hemoglobin, water and lipids [27-30]. In addition, the ultra-
structural cellular density and size ensemble associated with the extracellular matrix and
subcellular constituents of breast tissue can be interrogated from the NIRS scattering
spectrum [31, 32]. NIRS shows potential advantages over other imaging candidates
because of its noninvasive nature and relatively low cost and portable size, which makes
possible repeatable imaging procedures under various patient conditions. Near infrared
5
spectral tomography (NIRST) is an important subtype of NIRS, which reconstructs
tomographic 2D/3D images of major absorbers and scattering coefficients. Three are
three major source modulation/acquisition techniques used by NIRS/NIRST in breast
imaging: continuous wave (CW), time domain (TD), and frequency domain (FD). Figure
1.1 illustrates the detected light intensity over time for CW (a), FD (b) and TD (c),
respectively.
Figure 1.1. Three common types of sources used in diffuse optical imaging. The far-left figures show the “banana patterns” of light sampling path for transmission and reflectance geometries. The detected light intensity over time is illustrated for continuous wave (CW), frequency-domain (FD), and time-domain (TD) measurement in (a), (b) and (c), respectively [1].
CW imaging systems measure only the changes in amplitude after light
transmitted through the tissue. The source light either has constant intensity or is
modulated at low frequency to increase signal to noise ratio. Detectors without radio
frequency response can also be used to acquire transmittance light, which significantly
simplifies the instrumentation design and overall cost. As shown in Fig. 1.2(a), a DSP
based CW NIRST system was developed at Columbia University, with a large number of
imaging data acquired through 32 sources and 64 detectors per breast with four
wavelengths, at a frame rate of 1.7 Hz [33]. As shown in Fig. 1.2(b), another CW NIRST
system was developed by Philips Healthcare, with a total of 507 optical fibers mounted
on the surface of a scanning cup, operating at four discrete wavelengths [34]. A total of
6
253 source fibers and 254 detector fibers were used to deliver source light to the breast
tissue and collect transmittance light, respectively. CW NIRST systems have been used
by a number of research groups in breast cancer diagnosis and treatment monitoring,
which shows promising results [35, 36]. However, the effect of absorption and scattering
in CW imaging cannot be well separated, and prior information/guess of scattering
coefficient was required to initialize the image reconstruction procedure [37].
TD imaging systems usually utilize picosecond pulsed diode lasers in the NIR
range and compact detector with extended spectral sensitivity. The photon distribution of
time-of-flight or temporal point spread function, TPSF, was measured after a light pulse
with temporal spread below a nanosecond was injected into the scattering medium. Both
the absorption and scattering coefficients can be obtained by fitting the acquired TPSF
into a diffusion model. Extensive clinical breast imaging data have been collected using
time domain imaging systems by the research groups in Milan [38] and Berlin [39]. Both
groups developed TD scanning optical mammograms using a transmittance geometry.
FD imaging systems using radio-frequency (RF) modulated light, measure the
changes in both amplitude and phase of the transmittance/reflectance light. Several
research groups have developed FD NIRS/NIRST imaging systems with different
features [40-42]. In particular, the group at the University of Pennsylvania developed a
stand-alone clinical NIRST system, utilizing a CCD-based heterodyne detection scheme
[42], shown in Fig. 1.2(c). A large number of source-detector pairs (106) were acquired
and breast boundary segmentation was realized by a fringe profilometry system. The
group led by Bruce Tromberg at the University of California, Irvine (UCI) developed a
Diffuse Optical Spectroscopic Imaging (DOSI) system, which used a hand-held probe to
7
scan over 50 to 100 discrete locations on the breast (Fig. 1.2(d)). The system was tested
in an independently-executed, prospective multicenter clinical trial of breast cancer NAC,
with promising results for early detection of changes in tumor response [43].
NIRS/NIRST imaging in general suffers from relatively low spatial resolution due
to the diffusive nature of photon transport in scattering medium, which also comes with
the challenge of solving ill-posed systems in the image reconstruction. One solution is
multi-modality imaging, where the spatial information from another imaging method
(MRI, ultrasound, CT or mammogram) was encoded into the optical image
reconstruction procedure. For instance, a hybrid imager (Fig. 1.2(e)) was developed by
Zhu et al for simultaneous ultrasound and NIRS imaging [44]. A customized hand-held
probe was constructed, where a commercial US transducer was located in the middle, and
optical source and detector fibers were placed at the periphery. Besides ultrasound
imaging, NIRS was also combined with MRI imaging. A hybrid MRI-guided multi-
wavelength FD-CW NIRST system was developed by El-Ghussein et al at Dartmouth
College (Fig. 1.2(f)), which acquired simultaneous optical scan with DCE-MRI imaging
[3]. The system was used in a clinical trial conducted in Xijing Hospital, XI’an, China,
involving 44 subjects in total, of whom 28 had malignant pathological diagnoses, and 16
had benign lesions. The results of this study suggest that MRI-guided NIRST can
distinguish malignant lesions from benign conditions in women with undiagnosed breast
abnormalities via optical imaging biomarkers of total hemoglobin concentration and a
tissue optical index [45].
8
Figure 1.2. Six NIRS/NIRST systems for breast imaging. (a) The DSP based CW NIRST system developed at Columbia University. (b) The CW NIRST system developed by Philips Healthcare. (c) The stand-alone clinical NIRST system developed at University of Pennsylvania, utilizing a CCD-based heterodyne detection scheme. (d) The DOSI system developed at University of California, Irvine. (e) Combined US and NIRS systems and a handheld probe developed at University of Connecticut. (f) The MRI-guided NIRST system developed at Dartmouth College.
1.4 NIRST imaging of breast cancer at Dartmouth
The Dartmouth group has worked on developing NIRST systems and
reconstruction algorithms during the past two decades. The instrumentation of NIRST has
been developed and upgraded by McBride and El-Ghussein et al [3, 46, 47]. Srinivasan,
Dehghani and Jermyn et al developed and iteratively improved the reconstruction
algorithm and package, NIRFAST [48-50]. Wang et al combined FD and CW to acquire
broadband NRIST imaging [51, 52]. Mastanduno and Carpenter et al developed the
9
methodology for three-dimensional MR-guided NIRST imaging [45, 53-56]. Significant
clinical progresses have been made in the following two areas of breast imaging using
NIRST: breast cancer diagnosis and treatment monitoring to NAC, as outlined below.
1.4.1Breast cancer diagnosis
The performance of a MRI-guided NIRST system has been validated through a
recent clinical trial, of which the results suggested that MR-guided NIRST can increase
diagnostic performance of breast MRI [45]. Figure 1.3 shows the reconstructed optical
images overlaid on T1 MRI image for a patient with IDC breast cancer. The whole breast
was segmented into three regions of tumor, fibroglandular and fat, from MRI images.
Each region was supposed to have the same optical properties, and the contrast was
defined as the ratio between tumor to the surrounding normal tissue in terms of
concentration of different optical biomarkers. The contrast was then used to separate
malignant tumors from benign lesions.
Since the image reconstruction in NIRST is an ill-posed problem, an L-2 norm
regularization technique has been added into the objective function in the inversion
procedure, which can be optimized from different perspectives, as will be discussed in
later chapters. One of the major goals in this thesis was to develop optimal regularization
strategies in this MRI-guided NIRST image reconstruction.
10
Figure 1.3 Images from a patient with a 11x21x14mm biopsy-confirmed Invasive Ductal Carcinoma (IDC) in her right breast. (a) non-contrast T1 MRI with tumor location indicated (arrow); (b) Reconstructed images for HbT, (c) StO2, (d) water, (e) lipid, (f) scattering amplitude, and (g) scattering power is overlaid on the MR scan. The value of each parameter in the adipose region is suppressed for clarity of visualization.
1.4.2 Monitoring treatment response to Neoadjuvant Chemotherapy
A series of clinical trials have been conducted by Jiang et al [2, 30, 57], using the
stand-alone system for monitoring treatment response to NAC. In one previous study [2],
a group of breast cancer patients undergoing NAC were imaged with NIRS before, during
and after the treatment. Significant differences were found between pathologic complete
response (pCR) versus pathologic incomplete response (pIR) group, based on the relative
change in tumor HbT within the first cycle of chemotherapy treatment. Figure 1.4 shows
the reconstructed NIRST images for a pCR case prior, during and post NAC. It is clearly
11
seen from the HbT images that the tumor contrast decreased after the first cycle of
treatment.
Moreover, pretreatment HbT relative to the contralateral breast showed potential
to separate pCR from pIR group as well. Since pCR patients were reported to have higher
disease-free survival rates [58, 59], early monitoring and prediction of pCR/pIR category
has the potential to individualize patient treatment plan even before treatment starts. One
major logistical problem in adoption of imaging is that if the imaging requires an
additional patient visit, then the cost to the system and to the patient time may inhibit its
use. Thus, one of the major goals of this thesis was to develop a system which could
work by imaging patients in the chemotherapy infusion suit, and image quickly, such that
the time and effort involved in this exam does not compromise its value.
Figure 1.4. (a) Reconstructed optical images of a pCR case prior, during and post NAC. (b) Axial post contrast subtraction MRI before NAC shows and enhancing mass indicated by arrow [2].
12
1.5 Organization of this thesis
The structure of this thesis is listed as follows:
Chapter 2 introduces the physics between light-tissue interactions in scattering
medium, and the derivation of the diffusion equation and its implementation using the
finite element method (FEM). The basic theory about model inversion is explained, with
spectral and spatial prior information encoded during the inversion procedure. An
optimization algorithm of regularization parameter based on L-curve analysis is
developed for MRI-guided NIRST image reconstruction. Another optimal regularization
technique is also introduced, which directly encodes the spatial information into the
inversion matrix, without manual segmentation of MRI images.
Chapter 3 discusses the design of a 12-wavelength FD-CW portable NIRST
system, with simultaneous acquisition of three FD and three CW wavelengths. The
system provides wavelength coverage between 661nm to 1064nm, with one complete
data acquisition involving 12 wavelengths acquired in less than 2 minutes. A customized
breast-fiber interface is introduced as well.
Chapter 4 details the optimization of patient image reconstruction for MRI-guided
NIRST, using a dataset obtained in the clinical trial of 50 surgical patients in Xi’an,
China. The L-curve based optimization algorithm of regularization parameter, as
discussed in Chapter 2, is applied in the patient dataset. The performance of the other
optimal regularization strategy is also discussed in a case study.
Chapter 5 presents extensive phantom studies using the NIRST system. Typical
tissue simulating phantoms are compared. The making and characterization of a novel
soft gel phantom are discussed in detail. The soft gel phantom enables the design of
13
heterogeneous phantom with sphere shape inclusion, which better mimics the natural
shape of breast tumor than other existing phantoms. The reconstructed images of HbT,
StO2, water, lipids, and scattering properties are shown for a gelatin phantom made with
porcine blood.
Chapter 6 discusses collagen quantification in breast tissue. Tomographic images
of breast collagen content are shown for the first time, and image reconstruction
approaches with and without collagen content included have been validated in simulation
studies and normal subject exams. The reconstructed collagen image of a breast cancer
patient is presented, and the recovered tumor/background contrast in total hemoglobin
increases from 1.5 to 1.7 when collagen is included in reconstruction.
Chapter 7 presents an imaging study on a group of healthy volunteers with
various breast sizes and radiographic densities. Statistical analyses are performed using
the reconstructed optical biomarkers. The adjustable breast-fiber interface is tested and
validated as well.
Chapter 8 presents case studies of monitoring patient response to NAC. The
optical images are reconstructed prior, during and post NAC for two pIR cases. HbT
shows to be the strongest biomarker in predicting breast tumor response to NAC.
Chapter 9 summarizes conclusions learned from the work presented in this thesis,
and discusses the future directions.
The Appendix lists the reconstruction programs and data acquisition programs
developed in this thesis.
14
Chapter 2: Theory and Image Reconstruction Methods
2.1 Introduction
The goal of near infrared spectral tomography (NIRST) or more generally, diffuse
optical tomography (DOT), is to reconstruct a spatial map of optical absorption
coefficients, or absorption related chromophore concentrations, and scattering
coefficients, from fluence measurement, using a forward model for mathematically
describing photon propagation [60]. The most common approach to this model is the
neutral radiative transport equation (RTE), which has been used to simulate light
transport in a wide range of media, where light is treated as composed of distinct photons,
propagating in a medium with characteristic absorption and scattering properties [61]. In
the case of scattering medium such as breast tissues, where the scattering probability is
much higher than absorption, the RTE can be approximated by the photon diffusion
equation [62, 63], the derivation of which was presented in section 2.2.
Since photon diffusion equation is a nonlinear equation, the recovery of unknown
optical properties usually requires a two-step procedure, forward and inverse models. In
the forward model, boundary fluence was calculated given existing optical properties.
The difference between measured fluence and computed fluence was minimized during
the inverse model, giving an update to the optical properties.
The are several approaches for solving the forward RTE problem. Analytical
solutions are only available for simple geometries such as circles and rectangles, but
difficult to get in real clinical settings [64]. Statistical methods such as Monte Carlo has
shown to be the most accurate approach in modeling light transport [65], which is
performed by tracing individual photon histories [66]. Photons are treated as distinct
15
particles with given probability of scattering and absorption, at every point in discrete
geometry [67]. A large number of individual photons, usually millions, are launched from
the source position, and statistical sample results are collected at the detector positions.
However, in order to get precise results, a very large number of photon histories are
required, which can be expensive in computational time and complexity. Another
approach is numerical approximation of Partial Differential Equations (PDEs) that
approximate the RTE, such as the diffusion approximation. This PDE can be efficiently
solved with numerical approaches such as the finite element method (FEM) or the
boundary element method (BEM) [48, 68]. Section 2.3 discusses the implementation of
solving forward model using FEM approaches, which was used throughout this thesis.
Due to the diffusion nature of light transport in scattering medium, which is a
non-linear second order PDE, the problem in NIRST image reconstruction is also
nonlinear between the coefficients and the fluence, and so the inverse problem is highly
ill-posed and ill-conditioned [69]. Regularization and optimization techniques have been
applied in solving the inverse problem, in order to stabilize inversion of forward models
[70-72]. In this thesis, the inversion was achieved by a modified-Tikhonov minimization,
discussed in section 2.4.
The absorption and scattering coefficient can be recovered using fluence
measurement data acquired at a single wavelength. Given a set of fluence measurements
acquired at multiple wavelengths, and the absorption spectrum of absorbers of interest in
soft tissues, the concentrations of major chromophores including oxy- and deoxy-
hemoglobin, water, and lipid, can be recovered through NIRST image reconstruction. A
direct spectral reconstruction method has been used, where the spectral priors are directly
16
encoded into the inversion matrix, which is more accurate and robust than traditional
indirect spectral reconstruction approach [73, 74], and the details were outlined in section
2.5. The implementation of building a spectral Jacobian matrix combining FD
measurement data including both amplitude and phase data, and CW measurement data
including only amplitude data, was discussed as well.
Multi-modality imaging such as positron emission tomography/computed
tomography (PET/CT) has matured into an important diagnostic tool [75]. Similarly, the
combination of MRI and NIRST is has been studied by several research groups [53, 76-
78]. The excellent soft tissue contrast provided by MRI can be used as prior spatial
information to guide the reconstruction of optical images. In addition, the functional
information provided by NIRST shows potential in increasing the specificity of MRI in
breast cancer diagnosis [45]. Two approaches of incorporating MRI information into
NIRST image reconstruction were discussed in section 2.6. In the first “hard prior”
method, computational meshes were created for the whole breast consisting of three
regions composed of adipose, fibroglandular and suspected tumor tissue, using the
anatomical information provided by the MR images. Each region was assumed to have
uniform optical properties, and the absorption and scattering parameters were estimated
for all three regions. The number of unknown parameter reduces to three, which
significantly simplifies the image reconstruction. The other method encodes MRI
structural information into the NIRST reconstruction in a soft way, which has the benefit
of eliminating user intervention such as image segmentation of distinct regions.
Specifically, the Dynamic Contrast Enhanced Magnetic Resonance (DCE-MR) image
17
intensity value differences within the anatomical image were used to implement an
exponentially-weighted regularization function between the image pixels [79, 80].
2.2 Modeling of photon propagation in highly scattering medium
2.2.1 Optical characteristics of biological tissues
The optical properties of soft tissue have been investigated intensively [81-83].
The wavelength-dependent optical properties are usually described in terms of absorption
coefficient (µa (cm−1)), scattering coefficient (µs (cm−1)), scattering function (p(θ,ψ)
(sr−1)) where θ is the deflection angle of scatter and ψ is the azimuthal angle of scatter,
and the real refractive index n'. In thicker tissues where multiple scattering occurs and the
ψ-dependence is averaged and then ignored, scattering is always described as reduced
scattering coefficient µ’s = (1-g) µs. The g value is an anisotropy factor defined as the
averaged cosine of the scattering angle θ. Absorption coefficients and reduced scattering
coefficients are recovered as spatially distributed maps through NIRST image
reconstruction. In the near infrared (NIR) wavelengths (650nm-1000nm), absorption
coefficient of major absorbers in soft tissue is significantly lower than that in the other
wavelength range, which allows the light to penetrate soft tissues up to 8-10cm and be
measured at a detectable level [84].
2.2.2 Photon diffusion equation
The behavior of interactions among a large number of photons in turbid media
can be described by the radiative transport or Boltzmann transport equation:
1
𝑣
∂𝐿(𝑟,�̂�,𝑡)
∂𝑡+ ∇ ⋅ 𝐿(𝑟, �̂�, 𝑡)�̂� + 𝜇𝑡𝐿(𝑟, �̂�, 𝑡) = 𝜇𝑠 ∫ 𝑓(�̂�, �̂�′)𝐿(𝑟, �̂�, 𝑡)
4𝜋𝑑�̂�′ + 𝑄(𝑟, �̂�, 𝑡), (2.1)
where v is the speed of photons in the medium, and 𝐿(𝑟, �̂�, 𝑡) is the radiance (power per
unit area and unit solid angle) as a function of position r, in the direction Ω̂ at time 𝑡.
18
𝜇𝑡 = 𝜇𝑎 + 𝜇′𝑠 is the optical transport coefficient. Here 𝑓(�̂�, �̂�′) is the scattering phase
function, and 𝑄(𝑟, �̂�, 𝑡) is the radiant source function. The left-hand side of equation
(2.1) accounts for photons leaving a small element in phase space, and the right-hand side
accounts for photons entering it. The radiative transport equation can be simplified based
on diffusion theory if the scattering probability is much larger than that of absorption, or
𝜇′𝑠 ≫ 𝜇𝑎, with an isotropic source. Calculation of the diffusion model frequency domain
data then follows from
( ) ( , ) ( ) / ( , ) ( , )a oi c Q D r r r r r , (2.2) where an isotropic source, 0Q , with source frequency at position r delivers light
through turbid media. Here, represents the fluence rate at position r observed at
frequency . Also, ( )a r is the optical absorption coefficient and ( )D r is the optical
diffusion coefficient which is defined as 𝐃(𝐫) =1
3[𝜇𝑎(𝐫)+𝜇′𝑠(𝐫)]
.
To solve the diffusion equation in frequency domain, certain boundary conditions
need to be assigned. The air-tissue boundary can be represented by an index of refraction-
mismatched mixed type-III boundary condition, in which the fluence at the edge of the
tissue exists but does not return [68]. The relationship is described in the following
equation:
ˆ, 2An , 0 (2.3)
where is a point on the boundary, and n̂ is a vector pointing outwards, normal to the
surface. A can be derived from Fresnel’s law:
3
02
2 / 1 1 cos1 cos
c
c
RA
, (2.4)
19
where 1arcsin( / )c airn n is the angle at which internal reflection occurs, and
2 2
0 1 1/ 1 / / 1 .air airR n n n n At the external boundaries, refractive index (RI) airn is
generally assumed to be equal to that of free space, so that airn =1.
The assumption is that 𝜇′𝑠 ≫ 𝜇𝑎 is satisfied in breast tissues with typical 𝜇𝑎
between 0.002 and 0.1 mm-1, and 𝜇′𝑠 between 0.5 and 2 mm-1. The other assumption of
isotropic source is valid when source-detector separation is higher than three to five
reduced scattering lengths. In practice, the source is treated as an isotropic point source
and located one scattering distance interior to the actual source position.
2.3 Numerical modeling of the forward problem
The finite element method (FEM) has been widely used as a general and flexible
method in solving forward problem in arbitrary geometries [68]. In the FEM framework,
the diffusion equation in (2.2) can be expressed as a system of linear algebraic equations,
where the source term is defined as distributed Gaussian source. The imaging domain is
discretized into a series of small regions called elements, each of which consists of two or
more local nodes associated with piecewise linear basis functions. The photon fluence is
calculated at each location in the region in the forward model. The difference between
calculated fluence and measured fluence at the boundary detectors is minimized during
the inversion.
A MATLAB-based FEM software package, NIRFAST (Near Infrared
Fluorescence, Absorption and Scatter Tomography), has been developed in the optics in
medicine group at Dartmouth College [49]. This package includes toolboxes for creating
meshes of simple and complex geometries, calibrating raw measurement dataset, solving
frequency-domain NIRST reconstruction problem at single-wavelength or multi-
20
wavelength with spectral priors, encoding spectral priors and fluorescence imaging
problems. Figure 2.1 shows FEM meshes created in NIRFAST for three geometries: (a)
2D circle, (b) 2D customized football-shape interface, and (c) 3D actual breast.
Figure 2.1. FEM meshes with nodes and elements created in NIRFAST for three geometries: (a) 2D circle, (b) 2D football-shape and (c) 3D actual breast.
2.4 Inverse problem solver
The goal of the inverse problem is to reconstruct a spatial map of optical
absorption coefficients, or absorption related chromophore concentrations, and scattering
coefficients, from using measurements of light fluence from the tissue surface. This is
realized by minimizing the difference between measured and calculated fluence. An
objective function is defined as:
2 2
1
( )NM
M Ci i
i
, (2.5)
where Mi and C
i is the measured and calculated fluence at detector i, respectively. NM
represents the number of measurements. The inversion can be achieved by using a
21
modified-Tikhonov minimization. A penalty term is added into the original objective
function:
2 20
1 1
( ) ( )NM NN
M Ci i j
i j
, (2.6)
where NN is the number of FEM nodes or unknown optical parameters, and j is the
optical parameter at node j. 0 symbolizes the initial estimates of NIRS properties in the
tissue and it can be obtained from the homogeneous fit in the calibration procedure. Here,
is the regularization parameter which balances the relative magnitudes of the two parts
of the objective function – the data-model mismatch is represented by the first term, and
the difference between the current estimates of optical properties and the initial starting
values is expressed by the 2nd term.
In practice will not be equal to zero, and we are interested in finding the value
of j such that
is close to zero. We first expand this term using Taylor series
expansion method based on for some nearby point 0 :
0 0 0 ....dd
, (2.7)
Then after setting
=0 and ignoring higher order terms, we can get:
1
1i i i id
d
, (2.8)
22
This is an update equation of , where i and 1i represents updated at ith
and (i+1)th iteration, respectively. Solving for i
and i
dd
from equation
(2.7), we will get:
02 2Tc
c Mi i
, (2.9)
and 2
22 2 2T Tc c c
c Mi
d d dd d d
. (2.10)
The second order derivative term 2
22Tc
c Mdd
is very small compared
with the first term, and then ignored here. Plugging equation (2.9) and (2.10) back into
equation (2.8), the update equation of becomes:
1
1 0
T Tc c cc M
i i iI
, (2.11)
where I is an identity matrix, and c
is usually called Jacobian Matrix J using standard
terminology. The update vector 1i i is replaced with . The last term in equation
(2.11) comes from the penalty term in the objective equation, and can be ignored if i is
close to 0 . The update equation of (2.11) can then be written as:
1T T C MJ J I J
, (2.12)
The matrix TJ J , also called Hessian Matrix, is highly ill-conditioned and ill-
posed. The inversion of TJ J is stabilized by the addition of J , which makes the matrix
23
more diagonally dominant. For FD measurement, the fluence consists of both
amplitude I and phase . Jacobian matrix J is built as:
1 1 1 1 1 1
1 2 1 2
1 1 1 1 1 1
1 2 1 2
2 2 2 2 2 2
1 2 1 2
2 2
1 2
ln ln ln ln ln ln... ; ...
ln ln ln ln ln ln... ; ...
ln ln ln ln ln ln... ; ...
ln ln ..
NN a a aNN
NN a a aNN
NN a a aNN
I I I I I ID D D
D D DI I I I I I
D D D
JD D
2 2 2 2
1 2
1 2 1 2
1 2 1 2
ln ln ln ln. ; ...
ln ln ln ln ln ln... ; ...
ln ln ln ln ln ln... ; ...
NN a a aNN
NM NM NM NM NM NM
NN a a aNN
NM NM NM NM NM NM
NN a a aNN
D
I I I I I ID D D
D D D
(2.13)
In NIRFAT, the Jacobian matrix is built at each iteration using the adjoint
method, which is computationally efficient because it takes advantage of reciprocity [85].
2.5 Spectral prior reconstruction
The single-wavelength reconstruction outlined in previous sections provides
estimate of a and s at single wavelength. Once multiple-wavelength data are obtained
from the NIRST measurement, 2D/3D image of chromophore/absorber concentration can
be reconstructed. One approach is using constrained linear square fit to the Beer’s law
relation:
a i ii
C , (2.14)
where i and iC represents molar absorption coefficient and concentration of
chromophore with index of i, respectively. Similarly, the s spectrum can fit into an
empirical power law approximation to Mie scattering theory:
24
bs a , (2.15)
where scattering amplitude (a) and scattering power (b) are estimated [86, 87]. Instead of
reconstructing spatial map of a and s first at multiple wavelengths, and then applying
equation (2.14) and (2.15) in post-processing fitting, these constraints can be directly
incorporated into the inversion matrix for direct reconstruction of chromophore
concentrations and scattering properties. The modified Jacobian matrix becomes:
1 1 2 1 1 1 1
1 2 2 2 2 2 2
1 2
;
;
;
c c cM A b
c c cM A b
c N c N cM N A N b N
J J J J J
J J J J JJ
J J J J J
, (2.16)
where N is the number of wavelengths, and M is number of chromophores. The update
equation is also modified accordingly:
1 1
1
1
N N
C M
T TM
C M
c
c J J I Jab
, (2.17)
The ill-posedness of the modified matrix is improved because multiple-
wavelength data are used to reconstruct all parameters simultaneously, which also
reduces the number of unknown parameters. Compared with the traditional method, the
direct spectral prior method is more accurate and more robust to noise, and also shows
better performance in both phantom and patient experiments [88].
In this thesis, the dataset was acquired from a hybrid multi-wavelength FD-CW
NIRST system consisting of FD data (amplitude and phase) at six wavelengths, and CW
data (amplitude) at six wavelengths. Since there is no phase in CW data, the Jacobian
25
matrix must be adjusted by padding certain rows with zeros where phase data would be,
in order to include both two data types into the reconstruction.
2.6 Spatial prior reconstruction
2.6.1 Hard-prior reconstruction
Reduction of the recovered parameter space, , into a smaller number of larger
regions segmented from MRI scans is known as encoding hard-prior information into the
inversion [89]. The NIRST solutions were obtained with a three-dimensional image
reconstruction algorithm [69] and prior information extracted from the co-registered
breast MR images. Here, the assumption was made that the segmented regions from MRI
– adipose, fibroglandular, and suspected tumor – had relatively homogeneous NIRS
properties, and the goal of MRI/NIRST was to recover the corresponding region-based
values. Figure 2.2 shows an example of segmentation of breast MRI images using
NIRFAST-Slicer.
Figure 2.2. Image segmentation in NIRFAST-Slicer.
Once segmentation of the breast is finished, the spatial information can be
incorporated into the inverse process, and a region mapping matrix K is constructed [90]:
26
1 1 11,1 1,2 1,
1 1 12,1 2,2 2,
1 1 1,1 ,2 ,
2 2 21,1 1,2 1,
2 2 22,1 2,2 2,
2 2 2,1 ,2 ,
1,1 1,2 1,
,1 ,2 ,
NC NC NC
NC NC NC
c c cNR
c c cNR
c c cNN NN NN NR
c c cNR
c c cNR
c c cNN NN NN NR
c c cNR
c c cNN NN NN NR
k k k
k k k
k k k
k k k
k k kK
k k k
k k k
k k k
, (2.18)
where NC, NN, NR represents number of concentrations, number of nodes, and number
of regions, respectively. The element in K is defined as:
,
10
ci j
if i region jk
if i region j
, (2.19)
Then the Jacobian matrix is mapped into region space:
J JK , (2.20)
The update equation of becomes:
1T T C MJ J I J
, (2.21)
Hard-prior reconstruction significantly reduces the number of unknown
parameters from number of nodes to number of regions, and the image reconstruction
becomes well-determined. Though the ill-posedness of the problem is improved, it’s still
ill-conditioned, which means a small permutation in the boundary measurement data or
initial guess can have much larger effects in the estimated optical properties, because of
the diffusive nature of photon transport in scattering medium. Therefore, the
27
regularization technique is still indispensable and proper choice of regularization
parameter is critical during the inversion.
A common approach to selecting the regularization parameter, , is to use a
fixed empirical number based on prior tissue-phantom studies, and to apply this value to
data acquired from patient exams. In this study, an L-curve approach was used to find the
optimal regularization parameter [91, 92]. The L-curve is a parametric plot of the L-2
norm of the data-model mismatch ( ) versus the difference between optical properties
of two iterations ( ) where:
( ) J (2.22)
2
01
( )NN
ji
(2.23)
In other words, ( ) and ( ) represent the model-data error and spatial prior
error, respectively, each of which is being minimized during the model inversion. With a
relatively small , ( ) dominates the objective function, and a lower model error is
expected at the cost of a larger prior error, and vice-versa for the case of large . Plotting
( ) vs. ( ) for a range of illustrates the trade-off between these two types of error,
which typically exhibits an L-shaped curve. The corner of the L-curve is commonly
regarded as the optimal regularization because it minimizes the two error terms. In this
study, the L-curve method was applied to determine the optimal regularization at each
iteration.
28
Figure 2.3. (a) Flowchart outlining the sequence for the optimization algorithm. In (b), the L2 norm of the prior property error vs. L2 norm of the model error creates the L-curve of values for each regularization parameter value from 0.001 to 100. The optimization algorithm is implemented at each iteration, and optimal regularization parameter from previous iteration is used once the L metric falls behind the threshold.
Figure 2.3(a) outlines the sequence for optimization of regularization based on L-
curve analysis at each iteration. To begin, prior error, which is the difference in optical
property solutions between two iterations, is plotted vs. model error over a range of
discrete regularization from 0.001 to 100. Fig. 2.3(b) shows a typical L-curve at the first
iteration of image reconstruction for a patient with a malignant breast abnormality. Here,
the prior error, ( ) , is defined as the difference of the sum of squared reconstructed
optical parameters between the first iteration and initial estimate. The model-data
mismatch, ( ) , depicts the model error in the first iteration. When regularization
29
increases, the model error increases and prior error decreases. Next, constrained regions
are selected. In the range of regularization values from 0.001 to 0.02, the behavior of the
prior error vs. model error does not show a linear relationship because a small
regularization results in unstable solutions in the ill-conditioned inverse problem. Once
the constrained regions are selected, the L-curve can be fitted through least squares to
slopes of vm and hm for vertical and horizontal regions, respectively, and a point on the
L-curve with maximal second derivative can be obtained. If this L-metric is larger than a
threshold, an optimal regularization is assigned as the point which is closest to the
intersection of the two fitting lines. Otherwise, the fitting at this iteration is discarded,
and the optimal regularization value from the previous iteration is retained. The algorithm
is repeated for each iteration until the stopping criterion for image reconstruction is
satisfied. The optimization algorithm has been applied in clinical data, and the detail will
be discussed in Chapter 3.
2.6.2 Soft prior reconstruction
Other than hard-prior reconstruction introduced in the last section, there exists
another strategy of encoding spatial information, called soft-prior reconstruction. In this
section, a novel regularization scheme is applied which directly encodes information
about the structural images, rather than enforcing uniformity within manually-segmented
regions. It is referred to as the DRI method and constrains FEM nodes according to their
corresponding grayscale value differences within the coregistered companion image
volume. In this case, the regularization matrix operator can be written as:
30
(2.24)
where γ is the anatomical image grayscale value which corresponds to a particular FEM
node (in this thesis, grayscale values were normalized to the maximum within the image),
σg is the characteristic grayscale difference over which to apply regularization, and Mi is
a normalization factor chosen for each row.
By performing a similar iterative Gauss-Newton reconstruction method through
Eq. (2.12), the general update equation for the underdetermined form can be expressed as
(2.25)
where is the update to the parameters; is the Jacobian matrix which is the
derivative of the measurements, f (x), with respect to the optical property parameters of
interest at the k-th iteration, and has the dimension of , M is the number of
measurements, and N is the number of parameters, x; the superscript T denotes the
transpose, is the forward solution using the estimated parameters form the
iteration. The hard-prior and DRI reconstruction method outlined in this chapter was
compared in chapter 3.
1
1 exp , 2i jij
i g
i j
Lotherwise
M
1
1T T T
k k k kx J J L L J d f x
kx kJ
M N
1kf x 1k
31
Chapter 3: Optimization of Image Reconstruction in MRI-guided
NIRST for Breast Cancer Diagnosis
3.1 Introduction
Magnetic resonance imaging (MRI) guided near infrared optical spectral
tomography (NIRST) has the potential to add molecular information to the spatial maps
of MR imaging of the breast, and thereby increase the specificity of contrast-enhanced
MRI exams [78, 93]. Niziachrisos et al. and then Brooksby et al. first developed
combined MRI-NIRS systems for concurrent MRI and optical imaging [46, 94, 95]. El-
Ghussein and Mastanduno et al. later developed a hybrid FD-CW MRI-guided NIRS
system, which was validated in a clinical trial in Xi’an, China [3, 45, 56, 96]. In this
chapter, studies on optimizing MRI-guided NIRS image reconstruction from multiple
perspectives were presented.
NIRST image recovery [97] is nonlinear and ill-posed and has been the subject of
many years of research [89, 98, 99]. The diffuse propagation of NIRS light in tissue
generates a poorly conditioned matrix that requires inversion. A widely used approach to
solve the inverse problem is the Newton-Raphson technique regularized by a modified
Levenberg-Marquardt algorithm [69, 100]. Implementation of a method to automate the
choice of regularization is critical for patient imaging [91, 92, 101, 102]. While methods
to choose this parameter automatically are well established in computational studies, little
investigation of how the selection influences the diagnostic performance of the imaging
method in actual practice has been reported. In section 3.2, optimization of the
regularization parameter based on L-curve analysis was pursued in clinical MRI-guided
NIRST imaging with the specific goal of retrospectively maximizing the discrimination
between known malignant and benign breast scan [103]. A classically defined L-curve
32
optimization algorithm was developed for regularization parameter selection during
image reconstruction of data from 25 patients collected in a clinical study of women with
breast abnormalities (BI-RADS category 4-5) of unknown diagnosis at the time of the
imaging exam. The methodology was also used to study NIRST reconstruction with
either amplitude data or both amplitude and phase data acquired with a multi-channel 100
MHz frequency domain NIR spectroscopy system. The performance of the optimal
regularization was compared to fixed regularization in this setting of differentiating
malignant from benign breast abnormalities. This study represents the first time the
effects of regularization have been investigated in MRI-guided NIRST based on a
relatively large data set of clinical exams with the goal of optimizing task-based
discrimination.
Other than the “hard-prior” image reconstruction discussed in section 3.2, there are
“soft-prior” reconstruction methods realizing spatially encoded regularization, where co-
registered image information is applied in a pre-defined way, such as through a Laplacian
filter or a depth dependent function [95, 104]. Both hard prior and soft prior
reconstruction methods require the segmentation of breast into different small regions. In
section 3.3, a new approach for incorporating image information directly into the
inversion matrix regularization was examined using Direct Regularization from Images
(DRI), which encodes the gray-scale data into the NIRST image reconstruction problem.
This process has the benefit of eliminating user intervention such as image segmentation
of distinct regions. Specifically, the DCE-MR image intensity value differences within
the anatomical image were used to implement an exponentially-weighted regularization
function between the image pixels.
33
3.2 Optimization of regularization parameter in MRI-guided NIRST for breast
cancer diagnosis
3.2.1 Reconstruction and visualization of optical images in MRI-guided NIRST
The combined FD-CW data acquisition provides amplitude and phase recordings
from FD measurements involving six wavelengths, and amplitude data from CW
measurements involving three wavelengths. Using the amplitude and phase data at these
six wavelengths, the absorption and scattering coefficients at each wavelength were
estimated from homogenous fitting to obtain the initial estimates of oxy-hemoglobin
(HbO), deoxy-hemoglobin (Hb), water, lipid, scattering amplitude (SA) and scattering
power (SP). Using the anatomical information provided by the MR images,
computational meshes were created for the whole breast consisting of three regions
composed of adipose, fibroglandular and suspected tumor tissue. Each region was
assumed to have uniform optical properties, and the absorption and scattering parameters
were estimated for all three regions. From the recovered chromophores concentrations,
physiologically relevant parameters were calculated, including total hemoglobin HbT =
HbO + Hb, oxygen saturation StO2=HbO/HbT, and tissue optical index TOI = HbT
Water/Lipid. Optical property contrast, defined as the ratio of the suspected tumor to
background (adipose) properties, was used to differentiate malignant from benign
abnormalities.
34
Figure 3.1. Images from a 33-year-old patient with a 11x21x14mm biopsy-confirmed Invasive Ductal Carcinoma (IDC) in her right breast. (a) non-contrast T1 MRI with tumor location is indicated (arrow); Reconstructed images of (b) HbT, (c) StO2, (d) water, (e) lipid, (f) scattering amplitude, and (g) scattering power are overlaid on the MR scan. The value of each parameter in the adipose region is suppressed for clarity of visualization.
Figure 3.1 shows recovered images of HbT, StO2, water, lipid, scattering amplitude (SA),
and scattering power (SP), of a patient with a biopsy-confirmed breast malignancy. The
optical images are overlaid on the MR scan, and the value of each parameter in the
adipose region is suppressed for clarity of visualization. The tumor-to-adipose contrasts
of HbT, StO2, water, lipid, SA and SP were found to be 1.2, 1.1, 1.3, 1.6, 1.4 and 1.02,
respectively. A fixed regularization parameter of 0.1 was used through the image
reconstruction procedure for this patient.
35
3.2.2 Fixed regularization parameter of 0.1 and 1 using only amplitude data
Figure 3.2. Tumor-to-adipose contrast in HbT vs. regularization for (a) benign, and (b) malignant cases. Circles have a regularization of 0.1, and asterisks have a regularization of 1. Box plots of contrast for the two pathologies are shown with fixed regularizations of (c) 0.1 and (d) 1 for all patients.
First, the tumor-to-adipose contrast in HbT vs. regularization was plotted (Fig. 3.2) for
benign and malignant patients when images were estimated from amplitude data only,
and box plots of HbT contrast in the two diagnostic groups for fixed regularization
parameters of either 0.1 or 1, respectively. Compared to the malignant group, the HbT
contrast was less sensitive to variation in regularization in the benign group, especially
over the range of values from 0.01 to 0.2. Larger separation or difference in mean HbT
contrast, between the malignant and benign groups resulted from the lower regularization
of 0.1, even though less variation occurred within the same groups with a higher
36
regularization of 1, although a statistically significant difference existed between the
means of the malignant and benign groups with p-values < 0.05 in either case.
3.2.3 Optimal regularization using only amplitude data
Figure 3.3. L-curves are shown for the first 3 iterations of a single case with a malignant tumor. The regularization was varied from 0.001 to 100 at each iteration, and the optimal regularization was (a) 0.18, (b) 0.22, and (c) undefined for the three iterations, respectively. Histograms of optimal regularization parameter for the 1st and 2nd iteration are inserted to Figs. 3-3(a) and (b), respectively.
Figure 3.3 shows L-curves for the first 3 iterations during optical image
reconstruction of a patient with a malignant breast abnormality. The same range of
regularization (0.001 to 100) was used to plot the L-curve at each iteration. Both the
range of model error and prior error decrease with increasing number of iterations.
Optimization of regularization was implemented using the algorithm outlined in Chapter
2, and the optimal point is marked by a red asterisk in the first 2 iterations, and had values
of 0.18 and 0.22, respectively. At the third iteration, the L-curve metric was below the
threshold, and an optimal regularization could not be found using the same process.
Instead, the regularization value for the third iteration was set to be the same as used in
the second iteration, namely 0.22. The L-curve of the optical data acquired from most
patient exams exhibited similar behavior. The number of patient data sets which had an
optimal regularization based on L-curve analysis was 22, 12 and 0 for the first three
iterations. The average optimal regularization for the 1st and 2nd iterations was 0.21, and
37
0.24, respectively. After three iterations, none of the exam data had an L-curve with a
metric higher than the threshold.
Figure 3.4. (a) Log scale of projection error vs. number of iterations. (b) Tumor/adipose contrast in HbT vs. number of iterations.
The number of iterations used in the reconstruction was also an important factor. In Fig.
3.4(a), the projection error, which represents the residual of the inversion equation, is
plotted on a log scale as a function of number of iterations and decreases with increasing
number of iterations. The largest decrease in the projection error occurred at the first
iteration, which is typical behavior of a Newton-type iterative method. The change in the
projection error by the 9th iteration was relatively small, resulting in convergence of the
tumor/adipose contrast as shown in Fig. 3.4(b). 9 iterations were chosen as the stopping
criteria for image reconstruction of all patients.
38
Table 3.1. Comparison of statistics using fixed regularization of 0.1 and 1, and optimal regularization. Only amplitude data (AMPL) was used for image reconstruction.
aSignificant difference defined by p-value<0.05.
Table 3.1 summarizes the statistical and diagnostic results in terms of HbT and
TOI contrast using fixed regularization of 0.1 and 1, and optimal regularization based on
L-curve analysis. Data from 22 patient exams which had an optimal regularization in the
first iteration were included in the analysis. A significant difference (p<0.05) was found
in HbT contrast for malignant vs. benign patients for all three regularization selections.
3.2.4 Fixed regularization parameter of 0.1 and 1 using both amplitude and phase data
Figure 3.5. Tumor-to-adipose contrast of HbT vs. regularization for (a) benign conditions and (b) malignant tumors. Circles have a regularization of 0.1, and asterisks have a regularization of 1. Box plots of contrast are shown for regularization of (c) 0.1, and (d) 1 for all patients. Both amplitude and phase data were used in the image reconstructions.
39
Both amplitude and phase data were used in the image reconstruction to see if the
involvement of phase data obtained at the six FD domain wavelengths further improved
the accuracy of the optical image reconstruction. Figure 3.5 is organized the same as
Figure 3.2, except that the reconstructions utilized both amplitude and phase data, and the
3 clinical exams that did not have an apparent optimal regularization value in the first
iteration were excluded. In Figs. 3.5(a) and 3.5(b), the tumor-to-adipose contrast in HbT
was plotted as a function of regularization for benign and malignant patients,
respectively. When regularization decreased, the contrast increased in most cases. As a
result, better separation occurred between the malignant and benign groups, although
with higher standard deviation at the lower regularization of 0.1 as shown in Figs. 3.5(c)
and 3.5(d). Here, the HbT contrast in the benign group was more sensitive to the change
in regularization for low values when compared with the results in Fig. 3.2(a).
Apparently, determining an appropriate regularization for image reconstruction using
both amplitude and phase data is even more important than when using only amplitude
measurements.
3.2.5 Optimal regularization parameter using both amplitude and phase data
We also applied the optimization algorithm during image reconstruction with both
amplitude and phase data; but, L-curve analysis failed to find an optimal regularization.
The compromise between model error and prior error was not significant on the L-curve
at any point; hence, no optimal regularization parameter could be found that satisfied the
criterion of the optimization algorithm. One approach to improve the L-metric might be
to use separate regularization parameters for amplitude and phase data. In practice, we
40
found that a fixed regularization of 2 for phase data, and an optimal regularization based
on L-curve analysis for amplitude data worked well.
Table 3.2. Comparison of statistics using fixed regularization of 0.1 and 1, optimal regularization, and fixed regularization of 0.1 for amplitude and 2 for phase. Both amplitude and phase data (AMPL/PH) were used for image reconstruction.
aSignificant difference as defined by p-value<0.05.
Table 3.2 summarizes the statistical results of 22 patient exams, in which the
optimal regularization (on amplitude only, phase fixed at 2) was compared with fixed
regularization of 0.1 and 1, when both amplitude and phase data were used in the image
reconstruction. Reconstruction using a fixed regularization of 0.1 for amplitude and 2 for
phase data was compared as well. Significant differences (p<0.05) exist between the
malignant and benign groups in terms of both HbT and TOI for either a fixed
regularization of 1 or optimal regularization. Optimal regularization provides highest
AUC for TOI (0.94) among all the regularization choices. Meanwhile, the optimization
method still gives a relatively high AUC for HbT, without significant difference
compared with the fourth approach. In sum, the optimization technique provides a
systematic and automated way to find the optimal regularization parameter in each
iteration, which gives the best separation between malignant and benign groups in terms
of recovered optical parameter TOI. The L-curve based optimization technique utilized in
this paper aims at finding the tradeoff between prior error and model error in terms of Hb
and deoxy-Hb, water, and lipids. As a result, the increase of AUC for TOI is more
obvious than HbT compared with fixed regularization, since TOI (HbT*Water/Lipid) is
41
defined such that it represents all the recovered chromophore concentrations.
Interestingly, the addition of phase information appears to degrade the statistical
performance of the image reconstructions in the fixed regularization cases.
3.2.6 Optimal regularization leads to better separation between malignant and benign lesions
Figure 3.6. ROC curve for fixed regularization of 1 (black) and 0.1 (blue), and optimal regularization (red), when HbT and TOI are combined. Both amplitude and phase data were used for image reconstruction.
Instead of using a single predictor, either HbT or TOI, we evaluated the
combination of HbT and TOI as an indicator of malignant versus benign contrast-
enhancing MRI regions of interest. Specifically, we applied both HbT and TOI as
predictors, and fit a logistic regression using pathology-confirmed malignancy to obtain
the outcome. The predicted score from the logistic regression model was used to
construct the ROC curve [105]. We repeated this procedure for 3 different regularization
parameters and compared their AUCs with a bootstrapping method [106]. As shown in
42
Fig. 3.6, under these conditions, the optimal regularization improved the AUC (94.4%),
relative to the fixed regularizations of 0.1 (88.2%) or 1 (84.4%).
Figure 3.7. Box plots of the contrast for (a) total hemoglobin (HbT), (b) oxygen saturation (StO2), (c) Tissue optical index (TOI), (d) scattering power (SA), and (e) scattering power (SP), as recovered using the optimal regularization and amplitude and phase data. aSignificant difference.
Figure 3.7 presents boxplots of tumor-to-adipose contrast for (a) HbT, (b) StO2,
(c) TOI, (d) scattering amplitude, and (e) scattering power by applying the optimization
algorithm during image reconstruction with amplitude and phase data. HbT was the most
significant indicator for differentiating the malignant and benign groups, and provided the
largest mean difference in tumor-to-adipose contrast in the malignant and benign groups
(1.55x vs. 0.89x). A significant difference in TOI and scattering power contrast was also
observed. The average contrast in both HbT and TOI was significantly higher in the
malignant group than in the benign group. No significant difference in the StO2 contrast
was found.
3.2.7 Discussions
43
The results presented in this section show that the contrast recovered in both HbT
and TOI was diagnostically significant, but also depended on the choice of regularization
parameter. Thus, an objective methodology to select/identify the regularization parameter
for reconstructing data from individual patient exams is critical for practical application
of the combined MRI/NIRST imaging approach in the clinical diagnostic setting. Instead
of applying an empirical value for every subject exam, the optimization algorithm sought
an exam-specific regularization parameter (or series of parameters) for image
reconstruction that was derived from the measured optical data. The optimization
algorithm developed and applied here was based on L-curve analysis, which is widely
used to stabilize ill-conditioned problems by balancing the model error with the prior
error as constrained by the prior information. However, previous approaches of finding
the “elbow” on the L-curve have been limited primarily to theoretical or simulation
studies with few efforts being based on actual patient data. To deal with an L-curve
generated from clinical exam data, we defined a metric describing the characteristics of
the resulting L-shape, and developed an optimization approach based on least square
fitting of the response. The L-metric determined whether the L-curve was sufficient for
selecting an optimal regularization parameter, and if so, the optimal value from the curve
was utilized. Otherwise, the value from the previous iteration remained. The optimization
algorithm appeared to be robust and effective in the clinical data set we applied. We
found that the average L-metric of all exams decreased with increasing number of
iterations (although, the number of iterations was set to 9 in all cases, i.e., we did not
apply the L-metric or L-curve as a stopping criterion). Within 4 iterations, the projection
error and measurement noise amplification were balanced, and the contrast converged.
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Table 3.1 shown comparisons of the diagnostic statistics resulting from three choices of
regularization parameters. Moreover, although oxygen saturation (StO2) or scattering
properties alone could not be significant indicators, their combined effects enable that
TOI becomes the best indicator for differentiating the malignant from benign lesions.
When the regularization parameter decreased, less dependence in the recovered
optical solution occurred with the prior information. The prior estimate was obtained
under the assumption that the whole breast had the same optical properties. As a result,
higher regularization will lower the contrast between tumor and background leading to
smaller separation in the mean HbT and TOI contrast between the malignant and benign
diagnostic groups at a regularization of 1. On the other hand, a high regularization also
suppresses the high spatial frequency variation in the recovered optical property
distribution resulting from measurement noise, and produced lower variation in HbT and
TOI within the malignant and benign groupings. A compromise between separation of
the mean value and noise driven variability was obtained by applying the optimal
regularization approach during image reconstruction.
The effects of optimal regularization on the recovered optical image when both
amplitude and phase data were used (relative to only amplitude measurements), was also
investigated. In this case, the regularization algorithm failed to find an optimal value
based on L-curve analysis, apparently because the relative contribution of phase data
noise was higher than amplitude data noise, requiring much higher regularization of the
phase data. Here, separate regularization values for phase and amplitude data improved
the resulting image outcomes. Specifically, improvement occurred when regularization of
the amplitude data was obtained through L-curve analysis, and regularization of the phase
45
data was fixed at 2 (~1 order of magnitude higher than the value used in the amplitude
regularization). With this approach, improvement in diagnostic performance in terms of
higher AUC for both HbT and TOI occurred relative to using fixed regularization. When
the phase data was added to the Jacobian matrix for image reconstruction, the optimal
regularization (with separate but fixed phase regularization) achieved maximal separation
of the malignant from benign diagnoses (highest AUC). Finally, the optimal
regularization generated the best AUC value (0.94) relative to the other regularization
choices considered when HbT and TOI were combined as the diagnostic indicator.
Although the best diagnostic performance occurred using both amplitude and
phase data, the three regularization approaches, either fixed or optimal, were still able to
separate the malignant and benign groups in terms of HbT (p<0.05), when applying only
amplitude data for image reconstruction. These results suggest the possibility of
simplifying the MRI/NIRS system into one with only CW channels, which would
significantly reduce cost while not sacrificing much in terms of diagnostic performance.
Table 3.3. Comparison of the three regularization approaches in all clinical exams, relative to selected exams when the optimal regularization parameter was found. The group of all exams included 16 malignant and 9 benign pathology-confirmed diagnoses whereas the selected exams included 15 malignant and 7 benign cases (3 exams from the former did not have optimal regularization at the 1st iteration). Both amplitude and phase data were used during image reconstruction.
aSignificant difference.
In the results presented here, data acquired in 22 out of 25 patient exams had an
optimal regularization parameter identified by L-curve analysis in the 1st iteration and
were included in the statistical analyses. For completeness, we compared outcomes of the
46
optimization algorithm in all 25 patients in Table III in terms of the p-value and AUC for
HbT and TOI contrast when applying the three regularization approaches. Both amplitude
and phase data were used for image reconstruction in these results. For the subset of
selected patients (22), optimal regularization provided the best AUC for both TOI and
HbT, relative to fixed regularization, but only for HbT when all exam data were
evaluated. Thus, the L-curve approach may be a pragmatic way to identify which
clinically acquired data sets are reliable. NIRS data can be compromised by a number of
factors during its acquisition, such as fiber-tissue coupling or reflections; hence, the
ability to objectively and conclusively determine which data sets will not lead to accurate
spectroscopic parameter recovery could be important in clinical practice.
3.2.8 Conclusion
In this section, a robust optimization algorithm for selection of the regularization
to be applied during image reconstruction based on exam-specific data acquired during
MRI/NIRST examination of the breast was developed. The optical contrast values for
HbT, StO2, TOI, and scattering parameters were estimated by applying the optimization
algorithm when amplitude only and both amplitude and phase data. A statistical
difference (p<0.05) occurred between malignant and benign groups for both absorption-
derived contrasts (HbT and TOI) as well as reduced-scattering derived contrast (SP).
To the best of our knowledge, these results represent the first time an extensive
study of regularization has been conducted on a relatively large amount of clinical breast
exam data with the MRI/NIRST multi-modality imaging approach. The optimization
algorithm better differentiated malignant from benign cases compared to a fixed
regularization parameter. The best diagnostic performance occurred with optimal
47
regularization values selected from the individual’s exam data, and when combining HbT
and TOI estimated from both amplitude and phase data as the diagnostic indicator.
3.3 Direct regularization from co-registered anatomical images for MRI-guided
NIRST image reconstruction
The image reconstruction approach presented in section 3.2 is an indirect two-step
procedure [107]. First, high resolution anatomical images provided by MRI were
segmented into a small number of sub-domains with assumed homogeneous or constant
optical properties representing the major tissues types. For breast imaging, tissues can be
segmented into adipose, fibroglandular and suspicious regions. Next, the prior structural
information from MRI imposed a hard constraint on the image reconstruction process.
Since the optical properties within an identified region were forced to be uniform, the
constraint was often named as a “hard prior”. The notable advantage of using a hard-prior
scheme is the dramatic reduction in the total number of unknowns alleviating the ill-
posedness of the inversion by reducing the number of unknowns to the few identified
homogenous volumes. This process has the peripheral benefit of significantly enhancing
NIRST accuracy within the localized regions. However, its stability and accuracy are
critically dependent on the accuracy of the structural priors derived from the co-registered
image, and the performance is degraded when incomplete or distorted structural priors
are employed. The choice of regularization parameter is still critical in the case of hard
prior reconstruction due to the diffusive nature of photons in turbid media, though the ill-
posedness is significantly improved. As discussed in section 3.2, an L-curve based
optimization algorithm was developed for hard-prior NIRST image reconstruction, which
improved the accuracy of the image reconstruction. However, the hard-prior
reconstruction requires manual segmentation from MRI images, which is time consuming
48
and requires expertise in breast radiology. To address this challenge, a soft-prior image
reconstruction method without the requirement of image segmentation has been
developed.
Schemes based on “soft priors” do not require optical-property boundaries to
coincide with the MR-defined boundaries; therefore, they allow changes across
boundaries, and reduce the likelihood that spatial biases will be introduced during the
inversion process. Other methods that encode some uniformity into the inversion are also
possible such as total variation minimization or Laplacian smoothing. However, the
traditional hard and soft prior approaches that have been tested require user input to guide
the image segmentation involved [53, 56]. Unfortunately, segmentation can be time
consuming for the user, especially when the tissues of interest are large, and is prone to
errors, for example, when identifying tumor boundaries if the radiological or anatomical
training of the user is not sufficient. Thus, a direct reconstruction method, which
implicitly incorporates the anatomical information into the inversion problem without
user intervention, would dramatically reduce processing time and expand the potential of
multimodal imaging such as MRI-NIRST by fully automating the image reconstruction
process. In this section, a Direct Regularization Imaging (DRI) method for MRI-guided
NIRST was developed. The performance of DRI method was compared with that of hard-
prior reconstruction using L-curve based optimization of regularization parameter
through patient data.
Figure 3.8 shows MRI images of this subject. The left image (a) displays the
Nirview, 3D surface rendering from the T1 image volume where the fiber locations are
evident from the tissue depressions of the breast surface and the fiducial markers. The
49
middle image (b) shows a representative MR image slice from the standard T1 sequence.
The tumor was not localized in this view but the fibroglandular (center, dark part) and
adipose (bright) tissue compartments are readily visible. The right image (c) is a DCE-
MR image. The lesion displayed wash in/wash out contrast enhancement kinetics and
was bright in this image data. The contrast of the grey scale value of the tumor to
surrounding normal tissues was approximately 1.4.
Figure 3.8. MR images from a patient with a malignant lesion (20mm27mm33mm) seen on DCE MRI. (a): Screenshot of the Nirview 3D surface rendering of the T1 MRI. Fiducial markers and fiber bundle positions are shown; (b): Standard T1 image; and (c): Dynamic contrast-enhanced MRI.
Hard prior reconstruction with L-curve based optimization of regularization
parameter was applied and the reconstructed images are shown in Fig. 3.9(b). Figure
3.9(c) shows reconstructed HbT images using DRI method, with λ=1 and σg=0.001.
Though the segmentation is not required for our proposed approach, to validate the
accuracy of the reconstructed tumor site, we compared to segmented images as references
which are shown in Figure 3-9(a) where the tumor was segmented from the T1 and DCE
images in these planes [55]. Visualization thresholds were chosen to suppress the optical
backgrounds. The optical images reconstructed with DRI method (c) exhibited good
agreement on tumor location relative to the segmented images. The estimated HbT in the
tumor region was higher than the surrounding normal tissue, and suggested that the tumor
was malignant. The recovered HbT contrast of the tumor to the normal surrounding tissue
50
reconstructed by hard prior and DRI was 2.6 and 3.6, respectively. The average optical
image size differences relative to the segmented tumor from DCE-MRI was 6% for the
DRI method.
Figure 3.9. The reconstructed HbT images overlaid in three planes with x=-100.0, y=-19.8 and z=-26.6 respectively. (a) Segmented images from corresponding T1 and DCE images. Optical images reconstructed by hard-prior reconstruction using L-curve based optimization of regularization parameter (b), and DRI with λ=1 and σg=0.001 (c), respectively. This figure has been modified from [79].
To conclude, a direct inversion matrix regularization approach from coregistered
anatomical images has been proposed and studied for MRI-guided NIRST. In this new
methodology, the gray-scale image information from coregistered DCE-MRI is encoded
directly into the inversion matrix regularization during NIRST image reconstruction
without any requirements for user intervention such as image segmentation. The method
51
was also tested on in vivo breast data acquired by our combined NIRST/MRI imaging
system. Compared to the hard-prior reconstruction using L-curve based optimization of
regularization parameter, the contrast of the tumor to the normal surrounding tissue
increased from 2.6 to 3.6.
3.4 Discussions
Compared with standard “no prior” reconstruction, both hard prior and soft prior
reconstruction encode the high-resolution spatial information into the inversion
procedure. However, due to the increasing complexity from data fusion of multi imaging
modalities, more attention need to be paid to the inversion, especially regularization. In
this chapter, the image reconstruction of MRI-guided NIRST was optimized in two ways,
with optimal regularization strategy using “hard prior” reconstruction and DRI
reconstruction, respectively. The “hard-prior” information allows the inversion problem
to be reduced in size by lumping regions together into just a few super pixels or regions.
However, the segmentation required by the “hard prior” reconstruction adds further
complexity to the processing and reduces objectivity when combining the image
information. Besides, an L-curve based algorithm has been developed for the choice of
regularization parameter, which yields better differentiation between malignant and
benign lesions, than using fixed regularization parameter. It has the benefit of robust
recovered contrast and stabilized matrix inversion.
In the case where accurate segmentation of MRI images was difficult to achieve,
the DRI method can be an ideal alternative for regularizing NIRST image reconstruction
using spatial priors of another imaging modality. The implicit assumption in DRI is that
the gray scale anatomical image contains structural information which should influence
52
the NIRST parameters, which is plausible for blood-based contrast such as hemoglobin.
However, recovery of water, lipid and scattering values may not directly correlate with
the types of gray scale structures evident on DCE-MRI. Utilizing a range of MRI scans
selected to better map these parameters may be possible. For example, diffusion MRI
could potentially best match with water images from NIRST, and T1 MRI might best
match with lipids and scattering parameters. Thus, multiple DRI regularizations could be
associated with multiple NIRST parameters and is an approach that requires further study
to determine its advantages.
53
Chapter 4: A Hybrid Frequency-Domain/Continuous-Wave NIRST
System with Simultaneous Measurements at Twelve Wavelengths
4.1 Introduction
Near infra-red spectral tomography (NIRST) systems typically have source-
detector schemes that include either frequency-domain (FD) [108], continuous-wave
(CW) [109], or time-domain (TD) [110, 111] data acquisition. FD measurements using
intensity-modulated sources are extremely stable and cost effective, but have limited
working range of wavelength since the response of the available photomultiplier (PMT)
detectors drops dramatically above 825nm. As a result, the ability to achieve accurate
recovery of water and lipid content by FD system alone is greatly weakened, since these
chromophores have characteristic absorption peaks at 975nm and 930nm respectively.
CW systems usually cover a much broader range of wavelength, but lack the ability to
provide patient specific scatter information. Scatter components, including scattering
amplitude and scattering power, have proven to be critical in accurate recovery of other
absorption derived optical parameters, especially in the case of NIRST imaging without
guidance about tumor positions from other imaging modalities [112]. Additionally,
scatter components themselves could be potential biomarkers for differentiating different
breast groups and predicting tumor responses to treatment [113]. Hybrid FD-CW NIRST
systems [3, 51], which take advantage of both FD and CW modules, have proven to
provide spatial reconstruction of both chromophore concentrations of oxy- and deoxy-
hemoglobin, water and lipid, and scatter components of scattering amplitude and
scattering power. Sequential measurement involving multiple wavelengths is time
consuming, and so one approach to speed up the acquisition is simultaneous acquisition
of multiple wavelengths [114], as developed here in tomographic mode.
54
In this Chapter, a portable 12-wavelength FD+CW system was developed.
Section 4.2 gives a brief introduction of the NIRST imaging system and patient exam
settings. The system was developed based on an existing MR-guided NIRST system [3],
but several unique features have been added to the new system for the purpose of
dynamic monitoring of responses to neoadjuvant chemotherapy within the infusion suite.
Compared to our previous stand-alone NIRST approach for monitoring patient responses
to neoadjuvant chemotherapy [2], components in the hybrid system were integrated into a
portable cart. No imaging bed was required, allowing us to acquire data in the clinical
infusion suite. This system provides tomographically reconstructed images of the breast
that can be used to monitor tumor response to neoadjuvant therapy dynamically.
A six-wavelength FD laser source sub-system and six-wavelength CW laser
source sub-system have been developed, as discussed in section 4.3. The combination of
both FD and CW laser sources provides wavelength coverage between 661nm and
1064nm, allowing more accurate recovery of water and lipid. Section 4.4 shows the
hybrid detection module of PMT and PD detectors. Dynamic calibration of PMT and PD
detectors are presented as well.
A major improvement in the hybrid system was the breast interface, designed to
fit different breast sizes and shapes easily. We have shown in chapter 3 that measurement
sensitivity, or tumor coverage, plays a critical role in accurate, spatial reconstruction of
optical properties [56]. The fiber-breast interface has been investigated extensively for
different diffuse optical tomography systems [96]. A common disadvantage of these
breast interface geometries was their lack of mobility and the requirement for the patient
to be positioned prone on a specific imaging bed during data acquisition. For the purpose
55
of monitoring patient response during neoadjuvant chemotherapy, where the intention is
to examine an individual frequently at different time points during treatment, the added
convenience by portable breast interface is significant. In some cases, continuous
measurements are desired for dynamic monitoring of response during the infusion
procedure. A portable NIRST system with corresponding fiber-breast interface is
required to satisfy these conditions. To overcome these challenges, section 4.5 discusses
a supine adjustable optical interface designed to accommodate different breast shapes and
sizes with easy setup. Its performance is validated on phantom, (see Chapter 5) normal
subjects (see Chapter 7) and cancer patient (see Chapter 8).
While several studies [2, 115-117] have focused on hemodynamic changes
throughout the treatment period, dynamic changes during a single infusion procedure are
also of interest. Thus, we adapted the system design to acquire twelve FD and CW
wavelengths simultaneously, which significantly reduced imaging time, as discussed in
section 4.6. Finally, the performance of the hybrid NIRST system was systematically
characterized in section 4.7.
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4.2 Imaging system and patient exam settings
Figure 4.1. The 12-wavelength FD-CW NIRST system. (a) A photo of the NIRST system. (b) and (c) Recline chair with two groups of fibers in the imaging suite. (d) Adjustable fiber-breast interface. (e) Subject being imaged with the system. (f) Surface image of breast-interface.
Figure 4.1(a) shows the system configuration with its main components housed in
a portable cart. The FD source module consists of six laser diodes (661nm, 730nm,
785nm, 808nm, 830nm and 852nm), modulated by high frequency (~100MHz) signals
generated from a multi-channel RF synthesizer (HS2004, Holzworth Instruments). The
CW source module consists of six laser diodes (850nm, 905nm, 915nm, 940nm, 975nm
and 1064nm), and it is modulated by low frequency sinusoidal signals generated directly
from the data acquisition board (USB 6255, National Instruments). A six-to-one fiber
combiner couples six light signals into a single source signal inside the FD/CW module.
57
As shown in Figs. 4.1(b) and 4.1(c), sixteen bifurcated fiber bundles are grouped
into two plastic tubes, which are attached to the recline chair. The chair was placed inside
the exam/infusion room where nurse helps on the setup of breast-fiber interface, and the
portable cart was placed outside, leaving enough privacy for the patient. The single end
of two groups of bifurcated fiber bundles are attached to the breast through an adjustable
interface designed to fit various breast shapes and sizes (Fig. 4.1(d)). During optical
measurement, the patient sits in a chair, with one side of the breast connected to the
imaging system through the fiber-breast interface (Fig. 4.1(e)). A black sheet covers the
patient to prevent room light from interfering with the data acquisition. The optical
measurements are easily completed in the infusion room given the positioning flexibility
of this portable NIRST system.
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4.3 Laser source sub-systems
4.3.1 6-wavelength FD source module
Figure 4.2. 6-wavelength FD source module. (a) System diagram; (b) Photo of the sub-system with major components labeled. (c) Photo of the customized 6-wavlength source module and multi-channel synthesizer. Raw data acquired from one PMT detector after heterodyned with reference signal, for f1=100.0004MHz (d), f2=100.0007MHz (e), f3=100.0011MHz, and mixed signal including frequency components at all three frequencies (g).
Figure 4.2(a) shows the system diagram of the house built 6-wavelength FD
source module. The data flow of radio frequency (~100 MHz) electrical signal and light
are represented by blue and red lines, respectively. A multi-channel synthesizer (HS2004,
Holzworth Instruments, USA) provides three RF channels of signals with the same power
of 13dB but at slightly different frequencies of F1=100.0004MHz, F2=100.0007MHz,
59
and F3=100.0011MHz, respectively. It also provides another reference channel at the
frequency of 100MHz for heterodyne detection. The three RF signals are used as inputs
of three single-pole-double-throw (SPDT) RF 1x2 switches, and the six outputs are
combined with six DC current lines through bias-tees to drive six laser diodes. A
customized six-to-one fiber combiner combines the light from six laser diodes into a
single source fiber as FD source output. The six FD diodes are divided evenly into two
sets, one represented by solid blue lines (661, 730nm and 785nm) and the other one by
dash lines (808nm, 830nm and 852nm). At one time, only three laser diodes
(661nm/808nm, 730nm/830nm, and 808nm/852nm) are turned on and modulated by RF
signals through the SPDT RF switches. Figures 4.2(b) and 4.2(c) show the actual layout
of the FD subsystem and multichannel synthesizer, respectively. As shown in Figs. 4.2(d)
to 4.2(g), the detected optical signal acquired from one PMT detector after heterodyned
with reference signal is displayed on an oscilloscope, for F1=100.0004MHz (Fig. 4.2(d)),
F2=100.0007MHz (Fig. 4.2(e)), F3=100.0011MHz (Fig. 4.2(f)), and mixed signal
including frequency components at all three frequencies (Fig. 4.2(g)).
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4.3.2 6-wavelength CW source module
Figure 4.3. 6-wavelength CW source module. (a) System diagram. (b) Photo of the sub-system with major components labeled. Raw signal acquired from a PD detector with source light modulated at 30Hz (c), 50Hz (d), 80Hz (e), and mixed signal including all three modulated laser sources (f).
A 6-wavelength CW source module is shown in Fig. 4.3. The data flow of low
frequency (<100Hz) electrical signal and light are represented by black and red lines,
respectively (Fig. 4.3(a)). A data acquisition board (USB 6255, National Instruments)
provides three AC voltage outputs at different frequencies of f1=30Hz, f2=50Hz, and
f3=80Hz, respectively. Six laser current drivers (Thorlabs, LD2000R) take the AC
voltage output from data acquisition board, and then provide combined AC+DC current
to drive six laser diodes. Each of the laser current drivers was individually controlled by
one channel from the relay board. The six CW diodes are divided evenly into two sets,
one represented by solid black lines (850, 915nm and 975nm) and the other one by dash
61
lines (905nm, 940nm and 1064nm). At one time, only three laser diodes (850nm/905nm,
915nm/940nm, and 975nm/1064nm) are turned on and modulated by the combined
AC+DC signals generated from the current driver. Figure 4.3(b) shows the layout of the
sub-system with major components labeled. As shown in Figs. 4-3(c) to (f), the detected
optical signal acquired from one PD detector is shown on an oscilloscope with source
light modulated at 30Hz (Fig. 4-3(c)), 50Hz (Fig. 4-3(d)), 80Hz (Fig. 4-3(e)), and mixed
signal including all three modulated source lasers (Fig. 4-3(f)).
4.4 Hybrid PMT-PD detector sub-system
4.4.1 Detector array of 15 PMT and PD detectors
Figure 4.4. Photos of the hybrid detector sub-system. The actual assembly of bottom plate and hybrid detector array are shown in (a) and (b), respectively [3].
A custom programmable mechanical rotary switch hosting PMT (H9305-3,
Hamamatsu, Japan) and PD detectors for FD and CW measurement, respectively, shown
in Fig. 4.4. Fifteen pairs of PMT and PD detectors and one pair of light source couplers
are mounted evenly on the top plate of the rotary stage. Two optical fibers with core
diameter of 800 µm deliver light from the FD and CW source modules into the pair of
light source coupler, respectively. The two ends of each of the sixteen bifurcated optical
fiber bundles are mounted on another plate, which is fixed separately on top of the
rotating circular plate. The circular plate housing the PMT/PD detectors is controlled by a
programmable motor to enable source-detector multiplexing. The two ends of one
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bifurcated fiber bundles are connected to each of the pair of source couplers, while the
other 15 bifurcated fiber bundles are connected each of 15 pairs of PMT and PD
detectors. The single ends of 16 bifurcated fiber bundles are attached to a breast interface,
to deliver source light to, and collect the diffuse light from the breast (Fig. 4.1(d)). The
rotary switch is incremented 15 times to complete the measurements, yielding a total of
240 (16×15) source-detector combinations. The bifurcated fiber bundles allow the
simultaneous acquisition of both FD and CW data.
4.4.2 Calibration of PMT/PD detectors
The AC amplitude and phase after lock-in detection can’t be directly fitted into
the diffusion model for estimating optical properties. A systematic calibration of the
PMT/PD detectors was completed to standardize the inter-detector data. In addition to
differences in detector responses, other factor, such as fiber loses and rotary switch
coupling errors are corrected during the calibration procedure. Using a central source
location relative to all detector fibers, the amplitude/phase response of each PMT detector
was characterized for every source position [118]. The same procedure was repeated for
all possible combinations of wavelengths, PMT gain settings, and modulation
frequencies. Similarly, the amplitude response of each PD detector was characterized for
every source position, for six wavelengths at corresponding modulation frequencies.
After calibration, changes in amplitude and phase shift for a given source-detector pair
arise largely from photon absorption and scattering in the breast tissue, which allows
accurate reconstruction of desired optical properties.
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Figure 4.5. PMT calibration. Input power (log10) (a) and phase (degree) (b) versus PMT AC amplitude for different gain settings from 0.5 to 1.1.
For different PMT detectors, an input light signal with the same power might
result in different AC amplitude after lock-in detection. Additionally, AC amplitude can
also vary for the same PMT detector at different gain settings. Figure 4.5 shows the input
power (a) and phase (b) versus PMT AC amplitude for different settings from 0.5 to 1.1.
The actual input power shows a linear relationship with measured AC amplitude, with
different slopes for corresponding gain settings. The phase shift offset was extracted for
each gain, by averaging all the phase data with AC amplitude (log10) in the range
between -2 and -0.5.
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Figure 4.6. Uncalibrated and calibrated amplitude/phase data. Uncalibrated amplitude (a1) and phase (a2) versus source-detector distance. Calibrated amplitude (b1) and phase (b2) versus source-distance distance. The uncalibrated amplitude and phase data are shown in Figs. 4.6(a1) and 4.6(a2),
respectively. By contrast, the calibrated amplitude and phase are shown in Figs. 4.6(b1)
and 4.6(b2). It’s clearly seen that the amplitude and phase data after calibration show
linear relationship with source-detector distance, as predicted by diffusion theory.
4.5 Adjustable parallel breast interface
4.5.1 Classical fiber-breast interfaces
Most fiber-based diffuse optical imaging systems require optical fibers have good
contact with breasts so as to satisfy assumptions made in the diffusion approximation to
the radiative transfer equation. As a result, breast interface is a critical component which
can significantly affect the quality of reconstructed optical images. Multiple fiber-breast
interfaces have been developed at Dartmouth, corresponding to different sampling
geometries and setups [52, 119, 120]. Three typical fiber-breast interfaces are shown in
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Fig. 4.7, including circular interface (a), dual-plate interface (b), and triangular interface
(c).
Figure 4.7. Three typical fiber-breast interfaces: (a) circular interface, (b) dual-plate interface and (c) triangular interface.
The circular interface is the most classical interface which is widely used by
various research groups [121]. During patient exam, the patient places her breast through
a hole in the exam bed. Three planes of fibers (16 3) array are controlled by a precision
positioning system underneath a custom-built patient bed in order to fit different breast
sizes. A circular mesh is made with given diameter of the breast, for image reconstruction
and display. The circular geometry makes it straightforward to compare the reconstructed
optical image with coronal view of breast MRI images. The circular interface proves to
work well through a series of clinical trials [2, 122, 123].
Besides circular interface which was designed for optical imaging alone, two
interfaces have been developed for MRI-guided diffuse optical tomography system [96,
119, 124]. The dual-plate interface holds one row of eight fibers on each side of the
breast. Two lift bags can be inflated remotely to raise and lower the imaging plane. Such
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design allows the imaging plane to be adjusted according to the position of tumor during
a MRI exam, which improves the sensitivity across the complete breast volume without
manually replacing the breast interface. However, this interface is hard to assess small
breasts and tumors close to the chest wall or the nipple area.
To overcome this problem and further improve the breast coverage in MRI-
guided diffuse optical tomography, a triangular breast interface was developed by
Mastanduno et al. [120], and then validated in the clinical trial in Xi’an, China. The
triangular interface divides the 16 fiber bundles into one set of eight, and two sets of four
fiber bundles. Each set is attached to the MRI breast coil, which can slide in both the
medial-lateral direction and the anterior-posterior direction. Before the combined
MRI/optical measurement, patient-specific adjustments were completed by the nurse.
Compared with previous design, the triangular interface provides more freedom on breast
size and tumor position.
4.5.2 Design of adjustable parallel breast interface
Although the interfaces discussed above prove to work well in both phantom and
patient imaging, they are not the ideal candidate for free space breast imaging of breast
cancer response to neoadjuvant chemotherapy. They either are fixed on the imaging bed,
or need to be attached to the MRI breast coil. None of them provide a feasible solution in
the case where patient is imaged while sitting in the chair in the infusion room. To
address this challenge, a parallel optical interface (shown in Fig. 4.8(a)) was developed to
provide robust optical measurements with a flexible patient setup [125]. The interface
consisted of opposing plates with a slight curvature, designed using Solidworks and
fabricated with a three-dimensional printer (Stratasys, Inc., Eden Prairie, MN). The
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interface was blackened on its exterior to dampen stray illumination from light
reflections. The sixteen optics fiber bundles were divided into two sets of eight, placed in
the same plane, and connected through two slim rods that allowed adjustments to fit
specific breast sizes. Each group of eight fibers was placed along an arc with radius of
101mm, and two adjacent fibers have an angular separation of 4.65 degrees. During a
breast exam, the interface was opened to its maximum extent and then closed, until all
(most) fibers achieved good contact with breast tissue by applying a modest amount of
pressure. The clinical exam attendant positioned the optical interface measurement plane
across the tumor based on prior information from mammography/MRI images. The
position and orientation of the breast interface was also adjusted to maximize intersection
with the tumor, by imaging in one of the mediolateral (ML), mediolateral oblique (MLO)
or craniocaudal (CC) geometries commonly used in mammography. Setup of the breast
interface required 2~3 mins and most participants did not indicate feelings of discomfort.
After the interface was setup, the fibers which did not have good contact with the breast
were noted. The corresponding boundary data was eliminated from the dataset for future
image reconstruction. Separation between the two breast interface sections was measured
and used to make patient specific 2D FEM models. Two breast interfaces with different
curvatures were designed to accommodate various breast sizes and tumor locations. Their
performances are compared in Chapter 6.
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Figure 4.8. Adjustable parallel fiber-breast interface. (a) Solidworks file showing dimensions of the interface. (b) A soft gelatin breast phantom being imaged with the interface. (c) Corresponding FEM mesh with fibers marked in red circles.
Figures 4.8(b) and 4.8(c) show a soft gelatin breast-mimicking phantom imaged
with the interface and its corresponding 2D FEM mesh, respectively. The interfaced was
placed such that there was modest pressure between the fibers and phantom, and each
fiber was in good contact with the phantom.
4.5.3 Phantom imaging with the parallel breast interface
Figure 4.9 shows phantom experiment setups and reconstructed images of HbT,
StO2, water, lipid, scattering amplitude (SA) and scattering power (SP) for two breast
interfaces with different curvatures. The recovered inclusion/background HbT contrasts
were 1.40 and 1.38, 6.7% and 8.0% different from the actual contrast of 1.5X,
respectively. Both interfaces were able to yield expected background values for StO2
(>95%), water (>90%) and lipid (<5%), from the spectral coverage provided by the
longer wavelengths in the six CW channels.
Surface artifact, or the unexpected enhancement along the mesh boundary, is a
well-known problem in diffuse optical tomography. The second interface generated more
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surface artifacts than the first one, which partly accounts for the fact that the second
interface recovered lower contrast. Additionally, the first interface produced less
heterogeneity in the recovered images of chromophore concentrations. In general, the
performance of the first interface with deeper curvature was superior in terms of both
reconstruction accuracy and noise level. However, the second interface was preferred
when imaging small breasts because it maintained better fiber-tissue contact under these
conditions.
Figure 4.9. Experimental setup and reconstructed optical images for two heterogeneous phantoms with 1-inch diameter inclusions. The corresponding interface had deep curvature (a) and flat curvature (b). For both phantoms, the blood concentrations inside and outside the inclusion were 1.5% and 1%, respectively.
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4.6 Simultaneous acquisition at twelve FD+CW wavelengths
4.6.1 Simultaneous acquisition at twelve wavelengths
Figure 4.10. System diagram for simultaneous acquisition. FD source module, CW source module, and data acquisition/processing module are highlighted in blue, green, and violet blocks, respectively. The flow of low frequency electrical signal, high frequency electrical signal, and light is shown by the black, blue and red solid lines, respectively.
Figure 4.10 shows a system diagram for simultaneous acquisition of both FD and
CW measurements [125]. Custom fiber combiners connect the 6 FD and 6 CW lasers.
Although the hardware is capable of delivering and collecting all twelve channels of light
at the same time, best practice divides the twelve channels into two sets, mixing signals
which are maximally separated but can be measured simultaneously. The total light
source power of any six sources used at one time is less than 120mW, and the cross-talk
is less than 0.8% between different channels.
The combined FD and CW light is coupled into two ends of one bifurcated fiber
bundle, whose distal end delivers the illumination consisting of six wavelengths
modulated at six different frequencies to the breast surface. The transmittance light is
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collected by the single end of the other fifteen fiber bundles. For each of the fifteen
bifurcated fiber bundles, the transmittance light is delivered to pairs of PMT and PD
detectors. To ensure the PDs do not saturate in the presence of high frequency modulated
signals at shorter wavelengths, thin film long pass filters (87C, Kodak) were installed on
the detector windows to block light shorter than 850nm. The RF output from the PMT
detectors is amplified by a 20dB low-noise preamplifier, which also filters out residual
DC components. The output of the preamplifier is heterodyned with a 100MHz reference
signal through a mixer, down-converting it to the lower frequencies 400Hz, 700Hz and
1100Hz. These low frequency signals are amplified (100X) and filtered again to reduce
high frequency noise. The resulting signal is read and processed by a DAQ board (USB
6255, National Instruments), where the phase shift and amplitude are extracted for the
three shorter wavelengths. Since the phase shift data also depends on the initial phase of
each RF output signal from the synthesizer, these signals are passed through RF splitters,
and heterodyned with the 100MHz reference signal, to get the initial phase shifts of three
components, respectively, which are subtracted in the 3 FD channels. Unlike the FD
module, the output of each PD detector is directly connected to the DAQ board. Only the
change in amplitude of light propagating through the scattering medium is extracted at
the three modulation frequencies. The complete measurement dataset consists of
amplitude and phase at six shorter wavelengths (661nm, 730nm, 785nm, 808nm, 830nm
and 852nm), and amplitude at six longer wavelengths (850nm, 905nm, 915nm, 940nm,
975nm and 1064nm), for 240 source-detector combinations.
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4.6.2 Hybrid gain setting of PMT detector
Figure 4.11. Hybrid gain adjustment of PMT detectors. (a) Flow chart illustrating the hybrid gain adjustment scheme. (b) A photo of the adjustable fiber-breast interface (c) Corresponding football shape mesh created with 16 fibers assigned along the surface. (d) Amplitude data acquired at source position #1 using automatic gain adjustment scheme. (e) Amplitude predicted for the other source-detector pairs, based on the parameters fitted from (d). The actual amplitude and phase data acquired using the gain from the lookup table for the rest of source-detector pairs, shown in (f) and (g) respectively.
In FD measurement, PMT detectors at different positions, relative to the source,
receive levels of light which could be different by orders of magnitude. To account for
the variability, various voltage gains need to be assigned for 15 individual PMT
detectors. Two gain acquisition schemes were used in previous studies [3, 51]. The fixed
gain scheme has been traditionally used for circular geometry, where source-detector
distance does not change for a given detector during the acquisition regardless of the
position of source fiber. However, it does not work for irregular geometries where
source-detector distance varies depending on the position of source fiber. In this case,
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another automatic gain adjustment scheme was introduced to make sure that the
corresponding gain is found and applied at each of 240 source-detector pairs, which
ensures that the detected light intensity falls in the optimal linear response range. Though
the latter adjustment scheme works for more complicated geometries, it suffers longer
acquisition time and larger signal variation because of the rapid change of gain. To
accelerate the data acquisition and thus reduce the signal noise due to patient movement,
a novel hybrid gain adjustment scheme has been developed, as shown in Fig. 4.11.
Figure 4.11(a) outlines the chart illustrating the hybrid gain adjustment scheme.
First, the separation between two half-moon plates housing fiber bundles was measured
before breast/phantom experiments (Fig. 4.11(b)), loaded into the data acquisition
program, and then a patient/phantom specific mesh was generated automatically (Fig.
4.11(c)). An automatic dynamic gain searching algorithm was applied at source position
#1, and the gain (control adjustable 0 – 1.1 V) of 15 PMTs increased from 0.4 V (in
increments of 0.1V) until the AC component of the amplified output signal reached at
least 0.1V, or the highest possible gain setting was reached. The acquired amplitude times
source-detector distance in log scale was plotted versus source-detector distance for 15
PMT detectors. A linear relationship was obtained (Fig. 4.11(d)), which can be predicted
by diffusion theory. Linear regression was applied for transmittance (black) and
reflectance (red) data, respectively, and regression coefficients were extracted
accordingly. Next the source-detector distance was calculated for the other source-
detector pairs for the given mesh, and amplitude was predicted using the fitted regression
coefficients for transmittance and reflectance data, respectively (Fig. 4.11(e)). Given the
predicted amplitude of each source-detector pair, the optimal PMT gain can be obtained,
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and a lookup table of optimal gain for all source-detector pairs was generated. Lastly, the
lookup table was applied during the data acquisition at source position #2 to #16, and
both amplitude (Fig. 4.11(g)) and phase data (Fig. 4.11(h)) were acquired.
Figure 4.12. Standard deviation of amplitude (a) and phase (b) of 30 measurements for two gain adjustment schemes.
Compared with dynamic gain adjustment scheme with automatic gain searching for
each source-detector pair, the hybrid gain adjustment scheme described in this paper
takes significantly shorter time, 55s vs. 90s, to complete acquisition for all 240 source-
detector pairs. A homogeneous gelatin phantom was measured 30 times using two gain
adjustment methods, and the variation (standard deviation) in amplitude and phase data
was shown in Figs. 4.12(a) and 4.12(b), respectively. It’s clearly seen that the hybrid
acquisition scheme has superior performance to the other one, in terms of signal stability
for both amplitude and phase, at all PMT gains from 0.5 to 1.1. The FD data acquired
using the hybrid gain adjustment scheme has an average standard deviation of 1.1% and
0.3 degree in amplitude and phase, respectively, compared with 2.1% and 0.7% using the
previous gain adjustment method.
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4.6.3 Data acquisition GUI
Figure 4.13. LabVIEW GUI for data acquisition, pre-processing and display.
All the data acquisition/calibration was automated through a LabVIEW program.
As shown in Figure 4.13, the GUI presents acquired real-time amplitude and phase data
at three FD wavelengths, and only amplitude data at three CW wavelengths. The
acquisition was repeated twice to get a complete dataset involving 12 wavelengths.
Besides the regular acquisition routine, the GUI also allows users to select acquisition
mode of sequential or simultaneous, to disable specific laser/detector, to select different
adjustment schemes of PMT’s and so on.
4.7 Systematic characterization of the system
4.7.1 Comparison between sequential and simultaneous acquisitions
PMT detectors at different positions, relative to the source, receive levels of light
which could be different by orders of magnitude. In order to account for the variability,
corresponding gains are set for 240 source-detector pairs via a dynamic automated gain
adjustment algorithm where the PMT gain (control adjustable 0 – 1.1 V) of each source-
detector pair increased from 0.4 V (in increments of 0.1V) until the AC component of the
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amplified output signal reached at least 0.01V, or the highest possible gain setting was
reached. The dynamic adjustment ensures that the input light intensity falls in the optimal
linear response range. During simultaneous measurements involving 6 frequency
(wavelength) signals, the dynamic gain adjustment algorithm applied gains based on the
AC signal of the 785nm (700Hz) component.
Compared to the sequential measurements recorded by previous system [3], the
new unit is much faster. Previously, a complete set of sequential measurements involving
six wavelengths required 12min, whereas the new simultaneous measurement scheme
only requires about 45 seconds, which is sufficient for monitoring of patient response
during neoadjuvant chemotherapy with adequate temporal resolution, since a typical
infusion procedure takes 2-3 hours. Moreover, reduced acquisition time encourages more
patients to participate in the clinical study.
A silicone phantom was used to compare amplitude/phase data obtained with
simultaneous versus sequential acquisition. The average relative differences between the
two measurement methods in amplitude and phase was 0.8% for intensity and 0.6 degrees
in phase, for the 661nm channel, 1.0% and 0.8 degree for the 785nm channel, and 1.1%
and 0.9 degree for 826nm channel, respectively. Relative differences were found to be
0.6%, 0.5%, 0.7%, 0.6%, 0.8%, and 1.0% for the CW wavelength channels of 850nm,
905nm, 915nm, 940nm, 975nm, and 1064nm, respectively. These results demonstrate the
data quality of simultaneous recording is essentially equivalent to sequential acquisition.
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Figure 4.14. Reconstructed optical images for the same heterogeneous phantom with a 1-inch diameter inclusion. The optical images were reconstructed using boundary data acquired from sequential measurement (a), and simultaneous measurement (b), respectively. The blood concentrations inside and outside the inclusion were 2% and 1%, respectively.
Figure 4.14 shows the reconstructed optical images using boundary data acquired
from sequential measurement (a) and simultaneous measurement (b). The average
difference between two measurements is less than 2.5% for all chromophores. There is no
significant difference between the reconstructed images using two groups of
measurements, as shown in Fig. 4.14.
4.7.2 Variation of phase and amplitude data
Figure 4.15. Standard deviation of phase (a) and AC amplitude (b) versus AC amplitude for different gain settings from 0.7 to 1.1.
We also studied in detail the stability of the data acquisition in terms of standard
deviation of phase and amplitude. As shown in Fig. 4.15(a), standard deviation of phase
is plotted versus measured AC amplitude with different gain settings from 0.7 to 1.1. At
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the same gain, standard deviation of phase increases as AC amplitude decreases, due to
the decrease of SNR. While at the same AC amplitude, standard deviation of phase data
increases as gain increases, since noise is amplified more while signal does not vary. A
typical plot of measured AC amplitude versus PMT number is shown in the middle upper
of Fig. 4.15(a). PMT #6 to #10 have high gain of 1.0 to 1.1, and the other PMT detectors
have relative gain from 0.7 to 0.9. High gain and low gain groups of detectors are
classified, showing that detectors with high gains yield in higher standard deviation in
phase data than the other group with low gains. The AC amplitude has similar noise
pattern (Fig. 4.15(b)), with standard deviation of phase as high as 3% for high gain
settings.
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Chapter 5: Tissue Simulating Phantoms for NIRST Imaging
5.1 Introduction
Tissue mimicking phantoms, which have similar spectral properties to soft tissues,
have been intensively investigated in the past decades and many useful models have been
created [126-131]. An excellent overview of tissue simulating phantoms for optical
spectroscopy, imaging and dosimetry can be referred to Pogue and Patterson [132].
Characteristic tissue phantoms have proven indispensable in various optical imaging
modalities such as near infrared spectral tomography, photodynamic therapy [133],
luminescence imaging [134], fluorescence molecular imaging [135] and optical
coherence tomography [131, 136]. In general, these tissue phantoms are used in several
ways. First, they can be used for the purpose of routine quality control, where an imaging
system is tested with the phantom on a regular basis. Second, phantom can be used to
validate the system performance, when the output data/image of a phantom is compared
with the true value associated with the given phantom. Besides, they are also used for
calibrating raw measurement data as the first step of data/image processing. In NIRST
image reconstruction, a homogeneous phantom is usually imaged before patient imaging,
to calibrate any unexpected measurement errors such as thermal shift in lasers and
coupling error between optical fiber and detectors.
Tissue phantoms are usually made of matrix/base, scattering and absorption
materials, the choices of which correspond to different benefits and weaknesses. Matrix
materials provide an overall base, which decides physical/chemistry properties of the
phantom. Common matrix materials include water, gelatin/agarose, room-temperature
vulcanizing (RTV) silicone, resin, and polyvinyl alcohol gels. To mimic the diffusive
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nature of biological tissues, a large scattering coefficient is desired for the tissue
phantom. There are three major types of scattering materials: lipid emulsion, polymer
micro particles, and white oxide powders. The choice of absorbers varies from ink,
molecular dyes and blood to provide relatively stable absorption at single wavelength, to
oxy- and deoxy-hemoglobin and cells to provide spectral features at multiple
wavelengths in the NIR range. Note for given matrix/base matrix materials, there is only
limited number of choices for both scattering and absorption materials. Over the past
decade, there have been several types of tissue phantoms developed and tested in the
optics lab at Dartmouth. The three most popular ones are gelatin phantom, resin phantom
and RTV silicone phantom, outlined in Table 5.1.
5.2 Comparison between major tissue mimicking phantoms
Table 5.1. Comparison of major tissue-mimicking optical phantoms.
Gelatin phantom uses water and agarose/gelatin powder as base materials. As one
of the main water based phantoms, it can use any of the three major scatters mentioned
above. Intralipid has become the most widely used lipid emulsion, which is well
calibrated and commercially available. The optical properties of Intralipid has been
systematically characterized by Staveren et al, who also proposed a simple power law for
the wavelength dependence of reduced scattering coefficient [86]. The absorption of such
phantom mainly comes from water in the NIR range, and is extremely low (<0.002mm-1).
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Different absorbers can be added to add spectral features and tailor the absorption
spectrum. In addition, fluorophores can be added into the gelatin phantom as well in the
case of fluorescence imaging.
A unique feature of gelatin phantom is its capability of including whole blood as
absorbers to mimic the spectrum of soft tissues in NIR range, where the dominant
absorbers are hemoglobin and water [137]. The addition of blood as absorbers in other
matrix materials is challenging since blood does not mix well with either resin or
silicone. Notice that it’s critical to use saline instead of distilled water to preserve the
oxygen-binding function of hemoglobin. Gelatin/Agarose powder does not dissolve in
water solution under room temperature, and therefore the water needs to be heated to
boil. The gelatin phantom will then take shape after the solution cools down. Despite the
advantages of gelatin phantom, it does not last for a long time, and should be imaged
within the same day that the phantom is made. Another drawback of the gelatin phantom
is the likely contamination when the fiber/detector is in contact with the phantom. The
leakage of water from the gelatin phantom can cause serious problems when the phantom
is directly in contact with various photon detectors.
Figure 5.1. Major tissue mimicking phantoms developed at Dartmouth. From left to right, gelatin phantom with ink (a), gelatin phantom with blood (b), resin phantom (c), RTV silicone phantom (d) and silicone soft gel phantom (e) are presented, respectively.
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Polyester resin phantoms were first introduced by Firbank, Delpy, and Oda, who
used both TiO2 [138] and polystyrene particle scatters [139]; Room-temperature-
volcanizing (RTV) silicone-based soft phantoms were introduced by Bayes et al [140]
and Beck et al [141]. The most common materials of constructing resin phantom are
resin, hardener, ink and TiO2. The addition of hardener into the resin helps create a
transparent solid resin, which usually takes at least 24 hours to cure. The mixed materials
need to be degassed in vacuum for 3-4 times to prevent any bubbles inside the eventual
phantom. A detailed outline of this procedure can be referred to [142]. The procedure of
making RTV silicone based phantom is similar to that of resin phantom, except that
RTV-based compounds are required instead of resin. The curing procedure typically
takes 5-6 days for RTV silicone phantom, depending on the volume of hardener, which
takes much longer than the 24-hour curing time of the resin based phantom. Both resin
and RTV-based silicone phantoms may last many years with relatively stable optical and
mechanical properties [143], and therefore they are the ideal candidates for repetitive
studies.
Compared with resin based phantoms, the major advantage of RTV-based
phantoms, is that the stiffness can be well controlled by lowering the hardener
concentration, which gives the RTV-based silicone phantom more flexibility than resin
based phantom. In practice, the stiffness of RTV-based silicone phantom was close to
that of soft tissue when the hardener concentration was around 3.4% [132]. Unlike resin
based phantoms where the interior cavities and exterior shape can be easily machined,
RTV-based phantom can’t be machined with complicated geometries.
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Gelatin phantom, resin phantom, and RTV-based silicone phantom have been
extensively studied and tested at Dartmouth, with corresponding strengths and
weaknesses. Based on previous experience, a new method of making tissue mimicking
phantom is proposed in this chapter. The characterization of the proposed phantom is
discussed in section 5.3.
5.3 Phantom Preparation
5.3.1 Preparation of homogenous silicone soft gel phantom
Figure 5.2. The preparation of silicone soft gel phantom. (a) Base materials of A-341: Silicone Soft Gel. (b) Silicone coloring materials which are used as absorber/scatter. (c) The mixing of base and silicone coloring materials.
This soft gel tissue-mimicking phantom has been inspired by the masks used in the
movie industry, which have similar optical/appearances to human soft tissue. As shown
in Fig. 5.2(a), A-341: Silicone soft gel (Factor2, USA), translucent and low-viscosity
RTV, was used as the base material. The phantom was made by mixing base A and
catalyst B at a ratio of 10:1. Under room temperature, it only takes less than 1 hour to
cure. This is much shorter than that required by the RTV based silicone phantom
discussed in section 5.2. Since neither typical scatters (TiO2/Intralipid) nor absorbers
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(ink/blood) dissolve in the mixed solution of base A and catalyst B, special silicone
coloring materials (FI-SK, functional intrinsic skin colors, Factor2, USA) were used as
scatters and absorbers. These coloring materials are blend of FD&C cosmetic pigments,
and can be crushed into a silicone crosslinking fluid (Fig. 5.2(b)). The white and pink
coloring materials were added into the mixed base materials, to get desired absorption
and scattering properties. Figure 5.2(c) shows the preparation of silicone soft gel
phantoms. The leftmost container shows the mixed solution after stirring, while the other
containers correspond to different steps of the preparation process. A recipe and mixing
procedure for making silicone soft gel phantom is listed below.
1. Add 50g base A of A-341 into the container.
2. Add white and pink coloring materials.
3. Stir for 3 minutes with an automated stir bar.
4. Add 5g catalyst B of A-341.
5. Stir for 3 minutes.
6. Pour the mixed solution into mold for setting.
7. Wait for approximately 45 minutes for the phantom to cure
The above procedure is significantly simplified compared with that of making
gelatin and traditional resin/RTV phantoms, since it does not require either vacuum or
microwave. Besides, the whole procedure can be completed within one hour.
5.3.2 Preparation of heterogeneous silicone soft gel phantom
The preparation of homogeneous silicone soft gel phantom was discussed in
section 5.3.1. In this section, heterogeneous phantoms with similar size, shape, optical
and mechanical properties to breast tissues with tumor-mimicking inclusions, were
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developed using the recipe introduced in the earlier section. Figure 5-3 shows the detailed
steps of making breast mimicking phantoms with sphere shape inclusions. First, 1000ml
base materials were mixed with coloring materials in a stand mixer for 10 minutes to
provide background in the eventual heterogeneous phantom, shown in Fig. 5.3(a).
Meanwhile, 3D printed molds (Fig. 5.3(b)) were filled with mixed solutions which have
different optical properties from the background to provide tumor-mimicking inclusions
in the eventual breast mimicking phantom. Figure 5.3(c) shows sphere-shape inclusions
after curing. The sphere-shape inclusions were fixed inside a large mold (Fig. 5.3(d)),
which matches the 2D cross-section of the fiber-breast interface. The relative position of
the sphere, in terms of height and distance to the boundaries, was recorded as the ground
true. The mold was then filled with mixed solution prepared in the first step (Fig. 5.3(a)).
Figure 5.3(e) shows the heterogeneous breast mimicking phantoms with heterogenous
inclusions. Besides sphere-shape inclusions, cylindrical cavity can also be created inside
the phantom. A 20ml syringe was placed vertically inside the large mold, and the
background was filled with mixed solution. The syringe was taken out after the mixed
solution cured, resulting in a cylindrical cavity, which can be later filled with liquid
solutions with various optical properties.
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Figure 5.3. Detailed steps of making breast mimicking phantoms. (a) The base material was mixed with coloring materials in a food mixer. (b) The mixed solution was poured into 3D printed molds. (c) Three sphere-shape inclusions were taken from the molds after curing, with radius of 6mm, 9mm and 12mm, respectively. (d) One sphere-shape inclusion was fixed inside a large mold, which was filled with mixed solution later. The optical properties of the inclusion are different from those of the background, in order to create inclusion/background contrast. (e) A group of heterogeneous breast mimicking phantoms, with either sphere shape inclusion inside, or cylindrical cavity.
5.4 Characterization of homogenous silicone soft gel phantom
A series of small homogenous phantoms (Fig. 5.2(c)) were made with varying
optical properties, for systematic characterization of the new silicone soft gel phantom.
The absorption and reduced scattering coefficients in the NIR range were measured using
a diffuse optical spectroscopic imaging (DOSI) system, developed at University of
California, Irvine [40]. The DOSI system performs high-resolution spectroscopy from
650 to 1000nm, which combines FD measurements at four wavelengths and broadband
spectroscopy. During measurement, a hand-held probe was placed on the upper surface of
the phantom with modest pressure, and there was a fixed separation of 22mm between
the source and detector fibers. FD measurements were performed sequentially at four
wavelengths. Both amplitude and phase responses were measured when the modulation
frequency of laser was scanned from 50 to 400MHz, as shown in Fig. 5.4(a). The reduced
scattering coefficients were fitted using Mie theory, as shown in Fig. 5.4(b). Meanwhile,
the high-resolution absorption spectrum was obtained with fitted scattering coefficients
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and measured broadband spectroscopy (Fig. 5.4(c)). There are no obvious spectral
features in the absorption spectrum, except the absorption peak centered around 905nm,
which mainly comes from the absorption of water in the base materials.
Figure 5.4. DOSI measurement of a silicone soft gel phantom. (a) Measured (blue points) and fitted (red line) amplitude and phase at four wavelengths, while the laser modulation frequency was scanned from 50 to 400MHz. (b) Measured s at four wavelengths (red points) and fitted scattering spectrum (blue line) using Mie theory. (c) Fitted broadband absorption spectrum.
To validate the reproducibility of the silicone soft gel phantoms, five silicone soft
gel phantoms were made using the same recipe, and each phantom was measured 10
times at randomly selected positions on the phantom. The fitted a and s at 661nm are
plotted with error bars representing the standard deviation among different
measurements, for five phantoms, as shown in Figs. 5.5(a) and 5.5(b), respectively. The
average variation within the same phantom is 2.4% and 0.8% for a and s , respectively.
The variation among different phantoms, defined as standard deviation versus mean
value, is 1.0% and 1.4% for a and s , respectively.
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Figure 5.5. The fitted a (a) and s (b) at 661nm are plotted with error bars representing the standard deviation among different measurements, for five phantoms. Five silicone soft gel phantoms were made using the same recipe, and each phantom was measured 10 times at randomly selected positions on the phantom, using the DOSI system.
Ideal tissue mimicking phantom should last for long time with relatively stable
optical properties. Figure 5.6 shows the a and s measured at 661nm of a silicone soft
gel phantom over 10 days. The phantom was measured at multiple time points during a
10-day period, and 10 measurements were performed at each time point. The fluctuation
(dev./average) is less than 1.5 % of both a and s , within the range of the imaging
accuracy of the DOSI system.
Figure 5.6. Measured a (a) and s (b) at 661nm of one phantom in 10 days. Each time the same silicone soft gel phantom was measured 10 times at randomly selected positions on the phantom, using the DOSI system.
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Another important metric evaluating tissue mimicking phantom is the precise
control of optical properties by adjusting the amount of absorbers and scatters. The
optical properties of the silicone soft gel phantom can be controlled through a
combination of white and pink coloring paints. Two groups of seven phantoms were
made, using 50g of base A and 5g of catalyst B as the base material for each phantom.
0.3ml and 0.8ml of white paint were added into each of the seven phantoms in the 1st and
2nd group, respectively. An increasing amount of pink paint, from 0.1ml to 0.7ml with an
increment of 0.1ml, was added into corresponding phantom in each group. Figure 5.7
shows the measured a (Fig. 5.7(a)) and s (Fig. 5.7(b)) versus pink paint concentration
for the group of 0.3ml (blue points) and 0.8ml (black points) white paint, respectively. A
linear regression was fitted for each group of measurement data, with R2 higher than
0.98. It can be clearly seen that the addition of pink paint into the base material affects
both a and s linearly. One interesting fact is that the concentration of white paint
affects the slope of the linear regression. With a higher concentration (0.8ml) of white
paint added into the based material, the fitted slope is lower (0.0072 vs. 0.0087) than that
of lower concentration (0.3ml) for a . Meanwhile, the fitted slope is higher (1.36 vs.
0.96) of the group with more white paint for s . Such behavior suggests that the mixing
of two types of coloring paint may cause the aggregation of absorbers/scatters existing in
the paint.
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Figure 5.7. Measured a (a)) and s (b) are plotted versus pink paint concentration for the group of 0.3ml (blue points) and 0.8ml (black points) white paint, respectively. Two groups of seven phantoms were made, using 50g of base A and 5g of catalyst B as base material for each phantom. 0.3ml and 0.8ml of white paint were added into each of the seven phantoms in the 1st and 2nd group, respectively. An increasing amount of pink paint, from 0.1ml to 0.7ml with an increment of 0.1ml, was added into corresponding phantom in each group. Each phantom was measured 5 times.
Similar procedure was repeated to investigate the effect of white paint
concentration on the optical properties of the silicone soft gel phantom. Two groups of
seven phantoms were made, using 50g of base A and 5g of catalyst B as base material for
each phantom. 0.3ml and 0.5ml of pink paint were added into each of the seven phantoms
in the 1st and 2nd group, respectively. An increasing amount of white paint, from 0.5ml to
1.1ml with an increment of 0.1ml, was added into corresponding phantom in each group.
Figure 5.8 shows the measured a (Fig. 5.8(a)) and s (Fig. 5.8(b)) versus white paint
concentration for the group of 0.3ml (blue points) and 0.5ml (black points) pink paint,
respectively. As we can see from Fig. 5.8(a), the variation of a among seven phantoms
with different concentrations of white paint is 1.1% and 1.4% for the group of 0.3ml
(blue points) and 0.5ml (black points) pink paint, respectively. This suggests that the
addition of white paint does not alter a of the silicone soft gel phantom. On the other
side, s presents linear dependence on the concentration of white paint, with R2 higher
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than 0.99 for both groups of phantoms. Similarly, the slope of linear regression does
depend on the amount of pink paint in the phantom. With higher concentration (0.5ml) of
pink paint added in the based material, the fitted slope is lower (0.38 vs. 0.54) than that of
lower concentration (0.3ml).
Figure 5.8. The measured a (Fig. 5-8(a)) and s (Fig. 5-8(b)) are plotted versus white paint concentration for the group of 0.3ml (blue points) and 0.5ml (black points) pink paint, respectively. Two groups of seven phantoms were made, using 50g of base A and 5g of catalyst B as base material for each phantom. 0.3ml and 0.5ml of pink paint were added into each of the seven phantoms in the 1st and 2nd group, respectively. An increasing amount of white paint, from 0.5ml to 1.1 ml with an increment of 0.1ml, was added into corresponding phantom in each group. Each phantom was measured 5 times.
To conclude, the optical properties of soft gel phantoms have been characterized
in this section. The absorption and scattering properties can be controlled through
adjusting the amount of white and pink paints added inside the base materials. More
specifically, the addition of white paint affects a linearly, while the addition of pink
paint affects bot a and s linearly.
5. 5 Validating the performance of the NIRST system using heterogeneous breast
mimicking phantoms
5.5.1 NIRST imaging of heterogeneous phantoms at different depths
The effects of choice of imaging plane on the NIRST image reconstruction have
been intensively investigated by Wang and Mastanduno et al. The position and
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orientation of fiber-breast interface is critical to get reliable measurement, since the 2D
fiber array has been used to sample the breast tissue in 3D space. Mastanduno [56]
showed that only the optical measurement with enough tumor sensitivity, can be used for
reliable reconstruction of optical images, based on the patient data acquired from the
Xi’an clinical trial. Wang showed that in NIRST the quantification of inclusion to
background contrast highly depends on the position of imaging plane, or fiber interface,
using experiments data of gelatin phantom with cylindrical inclusions.
Gelatin phantom with cylindrical inclusion provides a good solution to mimic
tumor/background contrast in breast. However, sphere-shape inclusion is naturally a
better estimate to the actual breast tumor than cylindrical inclusions. The new silicone
soft gel phantoms with sphere inclusion were imaged at different depths, to better
characterize the performance of the 12-wavelength NIRST system.
Figure 5.9. Phantom experiments using a silicone soft gel phantom with a sphere inclusion. (a) Reconstructed absorption images from the data acquired at different depths of 0mm, -3mm, -6mm, -9mm, and -12mm, respectively. The depth of 0mm corresponds to the case where imaging plane was placed across the center of the sphere inclusion. (b) Profiles of the
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reconstructed a along the X-axis, crossing the center of the inclusion projected on the surface, at 830nm. (c) The sphere has a diameter of 24mm. The actual inclusion/background contrast is 2.
Figure 5.9(a) shows the reconstructed absorption images using the data acquired at
different depths of 0mm, -3mm, -6mm, -9mm and -12mm, respectively. The 0mm plane
corresponds to the case where the fiber-breast interface was placed across the center of
the sphere inclusion, which has a diameter of 24mm. We can see that the reconstructed
image at 0mm plane has the highest inclusion/background contrast, which corresponds to
the highest maximum a (blue squares) as shown in Fig. 5.9(b). When the imaging plane
was placed 6mm away from the 0mm plane, the reconstructed absorption image gives
much lower inclusion/background contrast, and the maximum a (red crosses) is 16.6%
lower than that of 0mm plane. The a images acquired at 3mm and -3mm plane provide
similar inclusion/background contrast, together with similar maximum recovered a .
Table 5.2. Maximum recovered a (10-3/mm) at different depths for phantoms with sphere shape inclusions with diameter of 12mm, 18mm and 24mm.
Similar depth varying measurements were performed for breast mimicking
phantoms with sphere inclusions of a diameter of 12mm and 18mm as well. The
maximum revered a (10-3/mm) extracted from reconstructed optical images at different
depths are compared in Table 5.2. When a small sphere (d=12mm) was included in the
breast phantom, the maximum reconstructed a is limited, even at the center plane with
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depth of 0mm. The maximum recovered a , obtained from the reconstructed optical
image of the phantom with a sphere inclusion of a diameter of 24mm, is 16.7% higher
than that of a diameter of 18mm, at the same depth of 0mm. In addition, the group of
measurements (depth at 3mm, 6mm, and 9mm) with imaging plane placed higher than
the baseline (depth at 0mm), has reasonable symmetry with those with imaging plane
placed lower than the baseline (depth at -3mm, -6mm, and -9mm).
The phantoms experiments presented in this section show that the quantification
of inclusion/background contrast in NIRST highly depends on the position of imaging
plane/fiber interface, especially in the case of imaging small inclusions. Furthermore, the
proposed heterogeneous silicone soft gel phantom with sphere shape inclusion is an ideal
candidate for characterizing the performance of NIRST imaging systems.
5.5.2 NIRST imaging using partial transmission/reflectance data
Various sampling geometries have been used in different NIRS/NIRST imaging
systems [40, 144]. In general, there are two types of boundary data: reflectance and
transmission. When the sources were placed along the same side as detectors, reflectance
data were acquired. On the contrary, transmission data was acquired when sources were
placed on the opposite side of detectors. The first sampling geometry simplifies the
design of fiber interface and has been widely used in hand held devices [43, 44].
However, a large source-detector separation is desired when a deep tumor/inclusion
needs to be imaged.
The adjustable fiber-breast interface introduced in Chapter 3 acquires both
reflectance and transmission boundary data. However, in this section, NIRST image
reconstruction using partial reflectance/transmission dataset is investigated.
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Figure 5.10. Phantom experiments using a silicone soft gel phantom with a sphere inclusion, which has a diameter of 24mm. Reconstructed absorption images from the data acquired at different depths of -9mm, -6mm, -3mm, 0mm, 3mm, 6mm, 9mm, and 12mm respectively, using (a) both reflection and transmission data; (b) both sides of reflectance data; (c) one side (upper side) of reflectance data; and (d) two sides of transmission data. The plane at 0mm corresponds to the case where imaging plane was placed across the center of the sphere inclusion. The actual inclusion/background contrast is 2.5.
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Figure 5.10 shows the reconstructed a images from data acquired at depth of -
9mm, -6mm, -3mm, 0mm, 3mm, 6mm, 9mm, respectively using both reflection and
transmission data (a), both sides of reflectance data (b), one side (upper side) of
reflectance data (c), and two sides of transmission data (d). The plane at 0mm
corresponds to the case where imaging plane was placed across the center of the sphere
inclusion. In the case of using complete datasets, including both reflectance and
transmission data for NIRST image reconstruction, a circular-shape inclusion can be
found from the recovered a image (Fig. 5.10(a)). The inclusion/background contrast is
most obvious on the a image at depth of 0mm, and there is almost no contrast in the
image at depth of 12mm.
In contrast, when only two sides of reflectance data were used for NIRST image
reconstruction, the recovered inclusion on the a image presents a football shape rather
than circular shape, as shown in Fig. 5.10(b). Such difference suggests that the
reconstructed tumor/inclusion may have distortion along X-axis, the same as the
sampling direction, if only reflectance data was used for NIRST image reconstruction.
Similar distortion along the X-axis can be found in the a images when only the upper
side of reflectance data was used for NIRST image reconstruction, shown in Fig. 5.10(c).
Meanwhile, in the case of using only transmission data for NIRST image reconstruction,
distortion along the Y-axis can be found in the a images, shown in Fig. 5.10(d).
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Table 5.3. Comparison of the reconstructed inclusion/background contrast using different subsets of measurement data acquired at different depths. Four subsets of measurement data are compared: (A) full dataset, i.e., both transmission and reflectance; (B) two sides of reflectance data; (C) upper side of reflectance data and (D) only transmission data.
Table 5.3 compares the reconstructed inclusion/background contrast using
different subsets of measurement data at various depths. Reconstruction using full dataset
provides the highest contrast at each depth. Comparing the reconstructed contrast using
different subsets, we can find that the reconstruction with only one side of reflectance
data provides slightly higher contrast than that using both sides of reflectance data. It
suggests that reasonable contrast can be recovered given only one side of reflectance
data, if the inclusion/tumor has modest distance from the boundary.
Table 5.4 provides the comparison of the sensitivity of inclusion for four subsets
of measurement data. Subset A has the highest inclusion sensitivity at all depths, while
subset D (transmission data only) has the lowest inclusion sensitivity. Also, measurement
data using two sides of reflectance data (B) has similar sensitivity to that using one side
of reflectance data, since the inclusion is far away from the lower side of the mesh
boundary. As we can see from Table 5-3, reconstruction using only transmission data
gives the lowest contrast among all groups. This can be explained by the fact that
transmission data only (D) has the lowest inclusion sensitivity among all groups, as
shown in Table 5.4.
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Table 5.4. Comparison of the sensitivity of the inclusion (%) using different subsets of measurement data acquired at different depths. Four subsets of measurement data are compared: (A) full dataset, i.e., both transmission and reflectance; (B) two sides of reflectance data; (C) upper side of reflectance data and (D) only transmission data.
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Chapter 6: In vivo Collagen Quantification in Breast Tissue
6.1 Introduction
Separation of multiple chromophores in breast tissue has been one of the major
challenges in NIRS and NIRST. Limited by the lack of data at wavelengths longer than
850nm, most near infrared imaging/spectroscopy systems target on the quantification of
up to four major absorbers of oxy- and deoxy-hemoglobin, water and lipid in breast. The
existence of other chromophores/absorbers is usually neglected during the imaging
reconstruction/fitting procedure. This is mainly due to the most higher gain PMTs suffer
from photocathodes that have poor performance above this wavelength range, and may
result in overestimation/underestimation in the above four major absorbers. Among all
the possible chromophores, collagen is one main constituent of soft tissues and its
quantification in breast tissue will be of interest to many researchers. Collagen seems to
be involved in the development of breast cancer [145], and expected to be related to
breast density [146]. Taroni et al has recently shown collagen as one important biomarker
in lesion classification, as concluded from a clinical study using an optical
mammography system operating with 7 wavelengths (635nm to 1060nm) [147]. Taroni
and coworkers did an extensive set of studies on collagen quantification in human breast,
who first measured collagen absorption spectrum, quantified collagen in vivo [148] and
showed collagen images [149]. However, there are only few publications regarding non-
invasive quantification of collagen content in breast tissue [111, 147, 150, 151], and there
are no publications testing the quantification of collagen in breast tissues with
tomographic capabilities.
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Using the hybrid 12-wavlength FD-CW NIRST system, with wavelength
coverage from 661nm to 1064nm, tomographic images of collagen content in breast
tissue have been extracted for the first time. Section 6.2 shows the simulation studies
using both homogeneous and heterogeneous phantom, and section 6.3 discusses collagen
quantification in normal subjects and cancer patient, and further investigates the effect of
presence of collagen on the reconstruction of other chromophores.
6.2 Simulation
The absorption spectrum of collagen content was encoded into the reconstruction
procedure as well, which enabled tomographic recovery of collagen content, in addition
to the other four chromophores.
6.2.1 Homogeneous phantom simulation
First a homogeneous mesh with HbT of 20M, StO2 of 70%, Water of 50%,
Lipid of 50%, SA of 0.8 and SP of 0.3 was assigned for each node. A certain amount of
collagen (0-10%) was added into the mesh as well. The total amount of water, lipid and
collagen is kept as 100%. Then the forward dataset was generated, based on which the
images were recovered for all chromophores except for collagen. As a result, some levels
of overestimate or underestimate were expected in the recovered images, because of the
contribution of collagen in the forward data.
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Figure 6.1. Recovered HbT (a), StO2 (b), water (c) and lipid (d) in a simulated homogeneous phantom, with collagen content increased from 0 to 10%.
Background collagen concentration with a range of 0 to 8.5% was reported in
human subjects by Taroni et al in Reference [111]. A slightly higher value of 10% has
been used in the simulation studies. Figure 6.1 shows the recovered chromophore
concentration of four major absorbers in a simulated homogeneous phantom when
background collagen content increased from 0 to 10%. When collagen content increased
from 0% to 10%, overestimation in HbT, water and lipid, as well as underestimation in
StO2 can be observed from the recovered values. The overestimate increases linearly with
the concentration of collagen added. With 10% collagen, there was an overestimate of
8.9uM, 1.8% and 15.8% in the recovered HbT, water and lipid, respectively. These
overestimates stayed the same, regardless of the actual assigned HbT, water and lipid
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changed in the range of 10uM - 30uM, 30% - 70% and 30% - 70% respectively.
Meanwhile, the underestimate in StO2 decreased nonlinearly versus increasing collagen
concentration. With 10% collagen, there was an underestimate of 12.6%, 9.5% and 6.5%
in the recovered StO2 for actual StO2 assigned at 80%, 70% and 60%, respectively.
The absorption due to collagen content would contribute to the recovery of other
chromophores, and results in an overestimation in all other four chromophores, when
collagen was not included in the reconstruction procedure, as shown in Fig. 6.1.
Depending on the absorption spectrum of each chromophore and the wavelengths utilized
to generate forward data, the amount of overestimate varied between chromophores. As
shown in Fig. 6.1(b), deoxy-hemoglobin is more sensitive to the presence of collagen
than oxy-hemoglobin, which leads to the underestimate in oxygen saturation. In addition,
with the same amount of added collagen content, recovered StO2 suffers more
underestimate in the case of higher StO2. Comparing Fig. 6.1(c) and Fig. 6.1(d), it was
found that given the same amount of 10% collagen added into the background, the
overestimate in water (1.8%) is much less than that in lipid (15.8%). This may be because
one absorption peak of lipid (at 926nm) is very close to one peak of collagen (at 913nm)
and the ignored absorption from collagen was accounted in lipid.
6.2.2 Heterogeneous phantom simulation
Insufficient recovery of tumor to normal-surrounding-tissue contrast is always a
challenge in diffuse tomographical image reconstruction. In addition to the diffusive
nature of photon transport in biological tissue, the existence of chromophores other than
four major absorbers may also make a contribution. To further investigate the effect of
existence of collagen on the recovered contrast in HbT and other chromophores, a two-
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region heterogeneous mesh was generated with a circular inclusion centered at (70mm,
40mm) with a diameter of 20mm. An inclusion/background contrast of 2 in HbT was
assigned to the mesh, with the same concentration of other chromophores as the
homogeneous phantom described above. Similarly, a certain amount (0-10%) of collagen
was uniformly added to the heterogeneous mesh. The images were reconstructed using
the simulated forward data for all chromophores without collagen included. The
recovered inclusion/background contrast was investigated versus different amounts of
collagen.
Figure 6.2. Reconstructed images of a simulated heterogeneous phantom. (a) Images with true values. The diameter of the circular inclusion is 20 mm. An inclusion/background contrast of 2 is assigned to HbT, with homogeneous background value of 75%, 45%, 45%, 10%, 0.8 and 0.3 assigned for StO2, water, lipids, collagen, SA and SP, respectively. Reconstructed images without collagen (b) and with collagen included (c).
Figure 6.2 showed the reconstructed images without collagen (Fig. 6.2(b)) and
with collagen (Fig. 6.2(c)). An inclusion/background contrast of 2 was assigned to HbT,
with true values of 40M and 20M assigned in the inclusion and background,
respectively. Homogeneous background values of 75%, 45%, 45%, 10%, 0.8 and 0.3
were assigned for StO2, water, lipids, collagen, SA and SP, respectively. As shown in
Fig. 6.2(b), the recovered HbT of 31.0M in the background was significantly
overestimated, resulting in an underestimated recovered contrast of 1.3. In contrast, with
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collagen included in the reconstruction, the recovered contrast of HbT increased to 1.6.
Considering that the assigned collagen content was homogenous, the
inclusion/background contrast in reconstructed collagen image of 1.2 indicates possible
crosstalk between HbT and collagen.
The same procedure was repeated for StO2, water, and lipid, respectively, in the
presence of 10% of background collagen. When collagen content was not included in the
reconstruction process, the recovered contrast of StO2, water and lipid decreased by
8.3%, 4.5% and 14.3%, respectively, compared with the case where collagen was
included in the reconstruction, given true contrast of 1.3, 1.5 and 1.5.
Figure 6.3. Image reconstruction in the presence of collagen, which was not included in the reconstruction. A similar simulation setup was used as Figure 2, except that the contrast was assigned in HbT (a), StO2 (b), water (c), and lipid (d), respectively. The extracted inclusion/background contrast was plotted versus collagen concentration accordingly.
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Figure 6.3 shows the extracted inclusion/background contrast plotted versus collagen
concentration, with contrast assigned in HbT (a), StO2(b), water (c) and lipid (d),
respectively. For all chromophores, the recovered contrast decreases versus increasing
amount of collagen concentration. The recovered contrast clearly presents different
collagen dependences between collagen less than 2% and collagen higher than 2%. The
recovered HbT contrast drops dramatically from 0% to 2% collagen, after which it keeps
dropping at a lower rate. With 10% collagen added into the background, the recovered
inclusion/background contrast in HbT drops by 28.2% from 1.9, 27.4% from 1.7, and
15.4% from 1.3, respectively for assigned contrast of 2.5, 2.0 and 1.5. For StO2 and
Lipid, the dependence of recovered contrast on collagen concentration is not obvious
with collagen less than 2%. While there is a sharp drop in the recovered contrast around
2% collagen, after which the recovered contrast tends to be stable. In the presence of
collagen, the recovered water contrast was not significantly affected, despite some
amounts of suppression.
6.3 In vivo collagen quantification in breast tissue
6.3.1 In vivo collagen quantification in normal subject
Figure 6.4. Contents of breast tissue recovered for HbT (a), StO2 (b), Water (c) and Lipids (d), with and without collagen included in reconstruction. The radiographic density type of subject #1 and #2 is heterogeneously dense (HD) and scattered fibroglandular dense (Scattered), respectively.
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Figure 6.4 shows the comparison of estimated chromophore concentrations of two
normal subjects with and without collagen included in the reconstruction process. The
radiographic density type of subject #1 and #2 is heterogeneously dense (HD) and
scattered fibroglandular dense (Scattered), respectively. The recovered background
collagen content of subject #1 and #2 is 4.8% and 1.3%, respectively. Compared with
reconstruction without collagen included, reconstruction with collagen yields in 6.7uM &
1.6uM lower HbT, 17.8% & 4.7% higher StO2, 2.8% & 0.5% lower water, and 6.7% &
3.8% lower lipids, for subject #1 & #2, respectively.
A total number of nine normal subjects were imaged, and similar comparison was
performed for each subject. Tomographic images were reconstructed of collagen and the
other four chromophores using spectral dataset acquired from the FD-CW NIRST system.
Average background collagen concentration was then calculated for each subject using
the reconstructed collagen image. Average collagen content with a range of 0 to 4.8%
was found in breast tissue among all the subjects, together with an increase in HbT from
0 to 6.7 M, 0 to 2.8% in water, and 0 to 6.7% in lipid, and decrease in StO2 from 0 to
17.8%, when collagen was included in the reconstruction.
Comparing the reconstructions with and without collagen, there was lower HbT,
water and lipid, and higher StO2 observed when collagen was included into the
reconstruction estimation process. This result is consistent with the simulation results,
and the patient data reported by Taroni et al [148, 152]. However, the change in lipid
between with and without collagen in the image reconstruction in this study is
significantly higher than that reported by Taroni. This may be explained by the fact that
different wavelengths have been used to extract chromophore contents in our NIRST
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system, and there are major differences in the nature of the signal acquisition (time
domain system vs FD + CW system here), as well as large potential differences in the
fitting algorithms.
6.3.2 In vivo collagen quantification in cancer patient
Figure 6.5. MRI T2 images of a patient with invasive cancer in the left breast: (a) Axial view, (b) sagittal view and (c) coronal view. Reconstructed optical images without (d) and with (e) collagen included. Recovered optical images are displayed in the same orientation in (d) and (e) as in (c).
Figure 6.5 shows the MRI and reconstructed optical images of a breast cancer
patient imaged with the NIRST system. This 63-year old woman has a 2-cm invasive
ductal carcinoma (IDC) tumor in her left breast. The position of the tumor was marked in
advance on the breast surface according to her prior MRI to guide placement of the fiber-
breast interface during optical imaging. The optical images were reconstructed without
(Fig. 6.5(d)) and with (Fig. 6.5(e)) collagen included. Orientation of the breast mesh (2-8
o’clock) was adjusted to coincide with the coordinate system in MRI, as shown in Figs.
6.5(d)-6.5(e). The recovered inclusion/background contrast in HbT increased from 1.5 to
1.7 when collagen was included in the reconstruction.
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HbT contrast has been always presented as the most important indicator in breast
cancer diagnosis and tumor response monitoring to neoadjuvant chemotherapy (NAC). A
more accurate quantification of the HbT contrast is important to provide better separation
between malignant and benign lesions, and this can also provide more robust prediction
of tumor response to NAC at the early stages. Furthermore, a contrast of 1.2 was found in
the recovered collagen image, with an average background collagen concentration of
2.3%. This contrast may bring in a new biomarker for breast cancer detection and/or
treatment monitoring, though more patient data is needed to validate that this collagen
contrast does not come from the crosstalk between collagen and other chromophores.
6.4 Discussions
In this chapter, tomographic images of breast collagen content have been
recovered for the first time, and image reconstruction approaches with and without
collagen content included have been validated in simulation studies and normal subject
exams. Simulations indicate that including collagen content into the reconstruction
procedure can significantly reduce the overestimation in total hemoglobin, water and
lipid by 8.9 M, 1.8% and 15.8%, respectively, and underestimates in oxygen saturation
by 9.5%, given an average 10% background collagen content. A breast cancer patient
with invasive ductal carcinoma was imaged and the reconstructed images show that the
recovered tumor/background contrast in total hemoglobin increased from 1.5 to 1.7 when
collagen was included in reconstruction.
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Chapter 7: Imaging Normal Subjects
7.1 Introduction
The breast is a turbid, light scattering medium with a complex combination of
layers, bands, sheets and nodules of absorbers, scatters and fluorophores [153]. In
general, the breast is richly supplied with blood but more vessels are found in the
fibroglandular than in the adipose tissues. Postmenopausal breast shows a reduction in
the fibroglandular volume and increase in the adipose volume [154]. Breast density
reflects variations in breast tissue composition and is strongly associated with breast
cancer risk [155]. Currently the gold standard in telling the breast density is x-ray
mammography. NIRS/NIRST methods have shown success in providing optical
biomarkers which are correlated with breast density [156-158].
The performance of the hybrid NIRST system has been validated on phantom
experiments as discussed in Chapter 5. However, the complexities of the composition,
size and shape, of the human breast with irregular-shape tumors cannot be completely
mimicked by tissue phantoms. To better evaluate the accessibility of the breast-fiber
interface to human breasts, a normal subject imaging study has been carried and the
results are presented in this section, before imaging cancer patients with the NIRST
system. The performance of the imaging system and reconstruction algorithm were
validated through comparison of reconstructed chromophore concentrations of normal
subjects with those reported in literature.
All human subjects imaging was carried out under a protocol approved by the
Committee for the Protection of Human Subjects (CPHS) at Dartmouth-Hitchcock
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Medical Center. Written consent was obtained for each subject and the nature of
procedure was fully explained.
7.2 Imaging setup
A major improvement in the current system was the breast interface, designed to
fit different breast sizes and shapes easily. We have shown that measurement sensitivity,
or tumor coverage, plays a critical role in accurate, spatial reconstruction of optical
properties [56]. In previous studies, the fiber-breast interface has been investigated
extensively for different diffuse optical tomography systems [51, 56]. A common
disadvantage of these breast interface geometries was their lack of mobility and the
requirement for the patient to be positioned prone on a specific imaging bed during data
acquisition. For the purpose of monitoring patient response during neoadjuvant
chemotherapy, where the intention is to examine an individual frequently at different
time points during treatment, the added convenience is significant. As shown in Fig. 1,
the subjects were seated in an adjustable chair in an examination room, and the NIRST
system was placed outside.
Figure 7.1. The setup of human subject imaging. (a) The NIRST system placed outside the exam/infusion room. (b) Exam room. (c) A female subject being imaged on the left breast.
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7.3 Imaging normal subjects with various breast sizes
Figure 7.2 shows recovered images of HbT, StO2, water, lipid, scattering
amplitude and scattering power for three normal subjects. Two interfaces were evaluated
- one with a deeper and one with a flatter surface curvature. The first (C cup, Fig. 7.2(a))
and second (D cup, Fig. 7.2(b)) subject were imaged using the breast interface with a
deeper curvature, and had separations between the two fiber holders of 63mm and 85mm,
respectively. The third subject had smaller breasts (A cup) and was imaged using the
interface with flat curvature and a maximum separation of 40mm. All sixteen fibers
contacted the breast well in the three cases. Heterogeneity in the recovered images
appears to arise from differences in fibroglandular and adipose content in the breast,
which varied case by case. No common pattern was found in the recovered images, which
suggests that the NIRST system does not introduce systematic bias. The interface with
flatter curvature was used for smaller breasts, and enabled better coupling at all fibers.
The other interface with deeper curvature yielded slightly better performance when all
fibers were in contact, which mostly the case for the larger breasts was considered here.
As shown in Fig. 7.2, we were able to image both small and large breasts. The
recovered lipid content for the participant imaged with a 63mm interface separation was
35.8%, which was lower than the other two participants.
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Figure 7.2. Reconstructed optical images of three normal subjects. Maximum separation between the two fiber holders in the interface was 63mm (a), 85mm (b), and 40mm (c), respectively.
7.4 Continuous imaging of normal subjects
One major goal of this thesis is to dynamically monitor tumor response to NAC
during one single infusion and the whole course of treatment. Figure 7.3 shows the
recovered optical parameters of two normal subjects measured continuously for 30
minutes. The standard deviation of 8 continuous acquisitions was calculated as 4.5%,
3.8%, 4.2%, 2.3%, 3.8% and 4.1% for HbT, StO2, water, lipid, water and lipid,
respectively. Several factors contribute to the temporal variation including breathing
pattern and patient movement, and the effects of these variations on the recovery of
tumor/background optical contrast needs further investigation.
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Figure 7.3. Continuous measurements of (a) HbT; (b) StO2; (c) water; (d) lipid; (e) SA and (f) SP for two normal subjects.
7. 5 Intra-subject and inter-subject variations
A group of ten normal subjects were imaged on both sides of the breast.
Corresponding ages, breast sizes, mammographic breast densities were recorded.
Subjects were divided into high and low radiographic density groups based on their
recent mammograms. Specifically, fatty and scattered breasts were categorized as low
density, and heterogeneously dense (HD) and extremely dense (ED) breasts were
considered as high density. Inter-subject and intra-subject variations were compared with
previous studies to validate the performance of the current NIRST system as well.
Normalized standard deviation was calculated to evaluate tissue heterogeneity in
healthy breast tissue. Table 7.1 shows the mean value and standard deviation of both
sides of the breast. The average spatial variance in HbT and StO2 across the recovered
tomographic images of each subject was 13.4% and 7.1%, respectively. Larger variance
of 14.6% and 13.7% was found for water and lipid, respectively. The larger variation in
the latter two chromophore concentrations arises mainly from the non-uniform
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distribution of glandular structures in the breast. The low variation in StO2 and HbT
agrees with our previous study reported by Wang et al [51], and results presented by
Shan et al [159]. Compared with Wang’s data, the results here indicated less variation in
water and lipid which may be a consequence of having more CW channels in the longer
wavelength range that lead to more accurate reconstruction of water and lipid.
Table 7.1. Mean and standard deviation of optical parameters of both sides of the breast.
Inter-subject variation for age, body mass index (BMI), HbT, StO2, water and
lipid appears in Table 7.2. Mean HbT values within the breast ranged from 10.0 to
26.8μM with an overall subject mean of 18.1μM. Mean StO2 within the breast varied
from 58.5% to 88.0% with an overall group mean value of 70.5%. The results reported
here are consistent with several other studies of asymptomatic breast tissue [45, 121,
160], which also indicate that optical and physiological parameters are significantly
affected by biological factors such as age, menopausal status, hormone use, and BMI.
A substantial amount of variation was found in the recovered lipid among these
10 normal subjects, with total range from 17.2% to 62.2%, and mean value of 40.2% with
standard deviation of 15.1%. The recovered physiologically relevant values of HbT,
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StO2, water and lipid all fell into reasonable ranges, and were comparable to previous
studies [152, 161, 162].
In addition to the inter- and intra-subject variation, differences between left and
right sides of the breast were assessed by calculating |left-right|/average*100% for all
optical parameters, which were 13.2% for HbT, 5.4% for StO2, 8.3% for water, and
12.9% for lipid. No statistically significant differences were found between the left and
right values for breasts imaged.
Table 7.2. Mean, standard deviation and total range of physiological and optical parameters of 10 normal subjects
A Student’s t-test determined whether different breast density groups could be
separated given the recovered optical parameters. Significance was achieved at the 95%
confidence interval using a two-tailed distribution. Figure 7.4 shows a comparison, in
which significantly (p<0.05) higher HbT and water contents (<0.03) were found in the
high-density group relative to the low-density group. No trends in oxygen saturation were
identified between the two groups, each being near 70% oxygenated. A strong correlation
between NIRST recovered properties and radiographic breast density was observed, as
shown previously [161, 162].
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Figure 7.4. Comparison between radiographic dense and non-dense groups in terms of HbT (a), StO2 (b), Water (c), Lipid (d), SA (e) and SP (e).
7.6 Discussions
HbT has been shown to be an indicator of tissue malignancy, widely used to
assess changes in tumor physiology during neoadjuvant chemotherapy [2, 117, 163]. StO2
may also be an important index for predicting tumor responses, since hypoxic tumors
have been found to be more resistant to chemotherapy [164]. Water, lipid and scatter
components have also shown potential to correlate with patient response [165]. Intra- and
inter-subject variations were calculated and compared to our previous work. Temporal
variations were also investigated through continuous measurements of 30 minutes. Less
than 5% standard deviation in 8 continuous measurements was observed, suggesting the
data are stable. No significant difference was found between the two sides of the breast
for all optical parameters, which supports use of the average of the contralateral breast
imaged before treatment to highlight the tumor/background contrast relative to the
surrounding tissue over the course of therapy. Furthermore, these physiological
parameters were compared between high and low density groups, and significantly higher
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HbT and water were found in the high density breasts, consistent with earlier studies [51].
No significant differences were found in the other optical indicators, partly because of the
modest sample size in the normal subject groups.
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Chapter 8: Towards monitoring breast cancer response to neoadjuvant
chemotherapy 8.1 Introduction
The optimal management of locally advanced breast cancer (LABC) has been a
challenging problem [166]. Preoperative systemic (neoadjuvant) chemotherapy (NAC)
has become an important option in the treatment of LABC, which sometimes allow for
breast-conserving surgery [167]. Studies indicate that tumor response to NAC correlate
with clinical outcomes, and patients with pathologic complete response (pCR) have
higher long-term survival rate than those with pathologic incomplete response (pIR)
[168]. However, due to the difficulty in evaluating patient response, there are still
controversies in optimizing the choice of intensity and duration of NAC for LABC [169].
In the case where a patient can demonstrate pCR/pIR at early stages during the treatment,
a more customized treatment plan can be expected to maximize clinical outcome.
NIRS/NIRST has also been used in predicating and monitoring breast tumor
response to NAC, besides its application in breast cancer diagnosis. Several research
groups have published promising results [43, 93, 116, 165, 170]. The independently-
executed, prospective multicenter ACRIN 6691 trial led by Tromberg was carried out
several years ago [43]. In this study, the diffuse optical spectroscopic imaging (DOSI)
systems were developed at University of California, Irvine, and delivered to six
institutions. A total of 60 patients undergoing NAC were enrolled over a 2-year period.
The results suggest that subjects who presented a greater drop in %TOITN from baseline
measurement to mid-therapy measurement, were more likely to have a pCR to NAC.
More specifically, in the subgroup of 17 patients who have baseline tumor StO2 greater
than the median population, a AUC value of 0.83 was obtained in %TOITN.
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In another recent study led by Zhu [171], the Hb content of 32 subjects, with 20
Miller-Payne grade 1-3 tumors and 14 grade 4 to 5 tumors, were assessed at different
stages during the NAC treatment, using a near-infrared imager coupled with an
ultrasonography (US) system. There were significantly higher HbT, oxy- and deoxy-
hemoglobin in the grade 4 and grade 5 tumors than in grade 1-3 tumors, with p-values of
0.005, 0.008 and 0.017 respectively. There was also significantly higher mean percent
HbT in grade 4-5 tumors at the end of treatment cycles 1-3, with p-values of 0.009, 0.004
and <0.001, respectively.
In the optics in medicine group at Dartmouth College, a series of clinical trials
have been conducted by Jiang et al, using the standard-alone NIRST system for
monitoring treatment response to NAC [30, 57, 114]. In an earlier study published in
2009 [30], average normalized change in HbT showed to be the only significant DOS
parameter in separating pCR from pIR group in seven patients. In a more recent study [2]
which involved 19 patients with locally advanced breast cancer undergoing NAC, it has
shown that pretreatment HbT inside the tumor ROI relative to that in the contralateral
breast, and the change in HbT after the first cycle of NAC were significant predictors of a
pCR. Therefore, HbT of both cancerous and contralateral breasts before the start of first
cycle of NAC, and change in HbT in the involved breast within the first cycle of
treatment could be used as prognostic indicators of NAC response.
The requirement of breast imaging several times with additional contrast MRI
scan may inhibit enrollment of women, based on our previous experience. A portable
NIRST system with easy setup of the fiber-breast interface could ease the burden of
participation, which allows for patient imaging during the process of chemotherapy
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infusion within the infusion suite. In addition, a larger clinical trial involving more
patients is needed for further validation and identification of the best potential imaging
biomarkers. We expect that the combination of optical recovered biomarkers and other
clinical parameters, will add more predictive power of tumor response to NAC, with
more patients enrolled. This will help individualize patient treatment and hopefully
reduce treatment length for some patients by early identification of pIR group, which are
of great importance considering the cost and complexity of NAC procedures. In this
chapter, the hybrid FD-CW NIRST system will be used to monitor tumor response to
NAC, and case studies are discussed.
8.2 Optimal workflow of NIRST breast imaging
Figure 8.1. Optimal workflow for NIRST patient imaging.
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Figure 8.1 outlines the workflow for NIRST patient breast imaging, which has
been optimized to get reliable optical recovery in the clinical conditions. First, the
LABVIEW acquisition program was customized for completely automatic NIRST data
acquisition, and a user friendly custom LABVIEW GUI was developed as well. Each
time a patient was imaged, the patient relevant information such as patient ID, age, body
mass index (BMI), mammographic breast density, visit number, interface orientation and
size were input directly through the acquisition GUI. Measurement data of multiple visits
were grouped together with easy lookup. Before each patient imaging session, a reference
homogeneous silicone phantom was imaged for calibration. This calibration was used to
calibrate system variation and to reconstruct patient images.
During the patient imaging session, measurement plane/orientation was picked up
and recorded according to the tumor location and size obtained from the pre-
treatment/diagnostic MRI/mammography images. After data acquisition at all
wavelengths, a 2D mesh was chosen from the mesh library based on given separation
between two plates of the breast interface.
With both homogeneous phantom and heterogeneous patient data, the nonlinear
FEM based reconstruction strategy and parameters were optimized to get accurate optical
properties from patient imaging. Individual regularization parameter and stopping
criterion, which decides number of iterations before the reconstruction terminates, were
chosen to maximize the ratio of the tumor to contralateral breast.
Displaying the optical images in a way that the tumor location can be easily
recognized and correlated to MR images is a challenging and critical task due to different
imaging geometries and breast deformation during the optical data acquisition. Such
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visualization is even more important in monitoring tumor response to NAC since the
breast/tumor size may change significantly during the long course of NAC. A user-
friendly GUI was developed to display optical images for easy interpretation by clinical
personnel.
For the purpose of ROI analysis, the tumor ROI was defined with the help of
contrast MRI images of coronal plane acquired before the initiation of therapy. The entire
tumor area, the entire area outside of the tumor, and the entire area of the contralateral
breast, were defined as tumor, nontumor, and contralateral ROIs, respectively. The mean
values of HbT, StO2, water, lipid, SA and SP of each ROI were extracted from the
reconstructed images.
8.3 Case studies of imaging breast cancer patients
8.3.1 Imaging breast cancer patients
To validate the performance of the NIRST system, espacially the flexibility of the
fiber-breast interface in the case of various breast sizes, shapes and tumor locations, a
group of 6 breast cancer patients have been imaged by the NIRST system.
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Figure 8.2. Case study #1. Dynamic Contrast Enhanced MR Images (DCE-MRI) of a patient with invasive cancer in the right breast: (a) Axial view, (b) sagittal view and (c) coronal view. Recovered optical images of HbT, StO2, water, lipid, SA, and SP for right breast (d) and contralateral breast (e).
Case study #1: This case was a 67-year old woman with a 1.1×0.9×1.2 cm3
invasive ductal carcinoma in her right breast. The position of the tumor was marked in
advance on the breast surface to guide placement of the fiber interface during optical data
acquisition. Dynamic Contrast Enhanced MR Images (DCE-MRI) of the patient were
also displayed in Figs. 8.2(a)-8.2(c). Registration between MRI and reconstructed optical
images was accomplished, with the prior knowledge of the fiber bundle positions relative
to the tumor location (marked on the breast surface), using MR images acquired prior to
the optical imaging session. Orientation of the breast mesh (1-7 o’clock) was adjusted to
coincide with the coordinate system in MRI, as shown in Fig. 8.2(d). The tumor region
was segmented from the recovered HbT image. The average tumor to background
contrast was calculated to be 1.41X for HbT, 0.92X for StO2, 1.23X for water, 0.78X for
lipid, 0.95X for scattering amplitude, and 1.08X for scattering power, respectively.
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Figure 8.3. Case study #2. DCE-MRI of a patient with invasive cancer in the right breast: (a) Axial view, (b) sagittal view and (c) coronal view. Recovered optical images of HbT, StO2, water, lipid, SA, and SP for cancerous breast (d) and contralateral breast (e).
Case study #2: This case was a 39-year-old female subject with a 2.1x1.5x1.9cm
invasive ductal carcinoma and ductal carcinoma in situ in her right breast. MRI images of
the patient were also acquired and displayed in Figs. 8.3(a)-8.3(c). Orientation of the
breast mesh (2-8 o’clock) was adjusted to coincide with the coordinate system in MRI, as
shown in Fig. 8.3(d). The HbT concentration at the tumor region was 27.8μM on average,
while the background value was about 17.7μM. The average tumor to background
contrast was calculated to be 1.57X for HbT, 1.08X for StO2, 0.92X for water, 1.07X for
lipid, 1.50X for SA, and 1.16X for SP, respectively.
Figure 8.4. Case study #3. DCE-MRI images of a patient with invasive cancer in the left breast: (a) Axial view, (b) sagittal view and (c) coronal view. Recovered optical images of HbT, StO2, water, lipid, SA, and SP for cancerous breast (d) and contralateral breast (e).
Case study #3: This case was a 47-year-old female subject with a 12x8x5 cm
invasive ductal carcinoma in her left breast. MRI images of the patient were also
displayed in Figs. 8.4(a)-8.4(c). Orientation of the breast mesh (2-8 o’clock) was adjusted
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to coincide with the coordinate system in MRI, as shown in Fig. 8.4(d). It’s clearly seen
from the MRI images that the tumor region occupies almost the whole breast. The HbT
concentration at the tumor region was 37.1μM on average, while the background value
was about 23.9μM. The average tumor to background contrast was calculated to be 1.55X
for HbT, 1.02X for StO2, 1.39X for water, 0.77X for lipid, 1.37X for SA, and 0.49X for
SP, respectively.
Figure 8.5. Case study #4. DCE-MRI images of a patient with invasive cancer in the left breast: (a) Axial view, (b) sagittal view and (c) coronal view. Recovered optical images of HbT, StO2, water, lipid, SA, and SP for cancerous breast (d) and contralateral breast (e).
Case study #4: This case was a 49-year-old female subject with 5.3x3.2x1.4 cm
invasive ductal carcinoma in her right breast. MRI images of the patient were also
acquired and displayed in Figs. 8.5(a)-8.5(c). Orientation of the breast mesh (2-8 o’clock)
was adjusted to coincide with the coordinate system in MRI, as shown in Fig. 8.5(d). A
modest amount of pressure was added onto the breast, which ensured that all the fibers
were in good contact with the breast tissue. The tumor was pushed towards the center of
the breast, as shown in the reconstructed HbT image in Fig. 8.5. The HbT concentration
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at the tumor region was 32.4μM on average, while the background value was about
24.1μM. The average tumor to background contrast was calculated to be 1.46X for HbT,
1.03X for StO2, 1.52X for water, 0.82X for lipid, 1.24X for SA, and 1.27X for SP,
respectively.
8.3.2 Monitoring breast response to NAC
Figure 8.6. Case study #5. Clinical images of a 63-year-old patient with pathological confirmed pIR. Postcontrast T2-weighted MRI images prior to initiation: (a) axial view, (b) sagittal view and (c) coronal view. Pathological findings showed pIR to neoadjuvant chemotherapy. Reconstructed optical images of HbT (uM), StO2 (%), water (%), lipid (%), scattering amplitude (SA) and scattering power (SP) before treatment (d), on day 9 of cycle 1 (e) and after therapy (29 days prior to surgery, (f)) are shown for abnormal breast. The recovered images of contralateral breast are shown in (g).
Case study #5: This case was a 63-year-old woman who had a 2.0x2.2x2.2cm
tumor in her left breast which was proved to be DC, ER-positive, PR-positive and HER2-
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negative. The chemotherapy regime consisted of dose dense AC (DD-AC, Doxorubicin +
Cyclophosphamide) and weekly trastuzumab. Figure 8.6 shows postcontrast MRI images
obtained prior to initiation, as well as reconstructed optical images of HbT, StO2, water,
lipid, SA and SP 14 days before the first treatment, on day 9 of cycle 1, and 6 days after
the last cycle (29 days before surgery) for the left breast.
Table 8.1. Reconstructed optical contrast in terms of HbT, StO2, water, lipid, SA and SP for three visits of before treatment, on day 9 of cycle 1, and after therapy, respectively.
As shown in Table 8.1, the contrast was defined as the ratio between tumor ROI
and pretreatment contralateral breast in HbT, StO2, water, lipid, SA and SP, respectively
for three measurements. There has been an increase in the contrast of HbT between pre-
treatment measurement and (Day 9, cycle 1), from 1.4 to 1.6. Such increase in HbT
contrast has also been found in previous studies as a strong indicator to pIR [30].
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Figure 8.7. Case study #6. Clinical images of a 54-year-old patient with radiologic findings and pIR. Postcontrast MRI images prior to initiation: (a) axial view, (b) sagittal view and (c) coronal view. Pathological findings showed pIR to neoadjuvant chemotherapy. Reconstructed optical images of HbT (uM), StO2 (%), water (%), lipid (%), scattering amplitude (SA) and scattering power (SP) before treatment (d), on day 19 of cycle 1 (f), and after therapy (24 days prior to surgery, (g)) are shown for abnormal breast. The recovered images of contralateral breast are shown in (g).
Case study #6: This case was a 54-year-old woman who had a 3.8x2.4x3.4cm
tumor in her left breast which was proved to be IDC, DCIS, ER-negative, PR-negative
and HER2-negative. The chemotherapy regime consisted of four cycles of Carboplatin
and Taxol. Figure 8.7 shows postcontrast MRI images obtained prior to initiation, as well
as reconstructed optical images of HbT, StO2, water, lipid, SA and SP 2 days before the
first treatment, on day 19 of cycle 1, and 19 days after the last cycle (24 days before
surgery) for the left breast.
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Table 8.2. Reconstructed optical contrast in terms of HbT, StO2, water, lipid, SA and SP for three visits of before treatment, on day 19 of cycle 1, and after therapy, respectively.
Table 8.2 shows the extracted contrasts in HbT, StO2, water, lipid, SA and SP,
respectively for three visits. There is an early percentage change of -16.7% in HbT
between (Day 19, cycle 1) and pretreatment measurement. The pathology results on the
surgical specimen revealed that the residual IDC and DCIS were present in the lesions,
which confirmed the case as pIR, although with some treatment effects. This patient has
Triple-negative breast cancer (TNBC), by the lack of estrogen receptor (ER),
progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2)
expression. TNBC are biologically aggressive and studies have suggested that they
respond to NAC better than other types of breast cancer [172]. However, this patient still
had partial response to NAC, which might be related to the negative early percentage
change in HbT contrast.
8.4 Discussions
HbT has shown to be the most important optical biomarker in breast diagnosis
and treatment monitoring [2, 45]. The contrast in HbT indicates an overall increase in
tissue vascular density in the tumor region. The formation of new blood vessels is
stimulated by the production of various growth factors caused by the carcinoma cells
[173]. Hemoglobin saturation is also reflective of their physiologic composition in the
breast. Oxy hemoglobin is more related to global vascular structures, while deoxy-Hb is
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more sensitive to cellular oxygen consumption and local metabolism. Low oxygen
tension or hypoxia is capable of facilitating tumor growth [174], and it has been related to
decreased survival rate and increased risk of local recurrence in soft tissue sarcomas
[175]. Therefore, the measurement of pretreatment/baseline StO2 in breast can be
attractive due to its potential predictive value. However, there is no obvious change in
StO2 in the tumor, as shown in the case studies, which is consistent with other studies
[27].
Besides oxy- and deoxy-Hb, water and lipids are another two major absorbers in
the breast tissue, which have critical biochemical significances [165]. MRI studies of the
apparent diffusion coefficient of water (ADCw) provide an insight into the distribution of
water, which relates to the cellular pathology and cellular density in several tissues. The
increase in water of the tumor might indicate variations in tumor cell density and edema.
Meanwhile, the reductions in tumor water might be caused by cell deaths. Cerussi
reported that there is a significantly higher pretreatment tumor to normal water ratio in
the pCR group [165]. The quantification of lipid can also be helpful indicating lesions in
the breast. Several studies on normal breast tissues have shown the lipid properties in
breast are strongly affected by age and menopausal status [159, 176].
Besides the absorption-derived chromophore concentrations, scattering-derived
parameters such as scattering amplitude (SA) and scattering power (SP) provide
additional information about tissue cellularity [177], composition [162], and disease state
[178]. SA and SP are related to the number density and size of scatters, respectively. A
large variation in SA and SP was reported among subjects with different radiographic
densities [179], which was most likely related to the dominant compositional changes.
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Further microscopic study of the scattering properties of tumors has the potential to
explain the exact features that contribute to the scattering properties.
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Chapter 9: Conclusions and Future Directions
9.1 Completed work
This thesis focused on improving the performance of near infrared spectral
tomography (NIRST) for diagnostic imaging and treatment monitoring of breast cancer,
from both the developments of imaging system and the nonlinear image reconstruction.
Some major results and conclusions are recapped here.
9.1.1 Optimization of MRI-guided NIRST image reconstruction
Chapter 3 introduced a robust optimization algorithm for optimal choice of the
regularization parameters for “hard prior” image reconstruction based on exam-specific
data acquired during MRI/NIRST examination of the breast. The optical contrast values
for HbT, StO2, TOI, and scattering parameters were estimated by applying the
optimization algorithm to amplitude data only, and both amplitude and phase data. A
statistical difference with p<0.05 indicates that absorption-derived contrasts in HbT and
TOI as well as reduced-scattering derived contrast in SP can be used to differentiate
malignant from benign lesions.
To the best of our knowledge, these results represent the first time an extensive
study of regularization has been conducted on a relatively large amount of clinical breast
exam data with the MRI-NIRST multi-modality imaging approach. The optimization
algorithm has better performance in differentiating malignant from benign cases,
compared to a fixed regularization parameter. The best diagnostic performance occurred
with optimal regularization values selected from individual’s exam data, and when
combining HbT and TOI estimated using both amplitude and phase data as the diagnostic
indicator. These results were published by Zhao et al [113].
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9.1.2 A hybrid FD/CW system with simultaneous measurements at twelve wavelengths
Chapter 4 summarized the unique features of the 12-wavelength hybrid FD-CW
NIRST system developed in this thesis. The system was developed for quantifying
changes in HbT, StO2, water, lipid, scattering amplitude and scattering power in the
breast during neoadjuvant chemotherapy. One six-wavelength FD source module and one
six-wavelength CW source module were built to provide the wavelength coverage in the
range of 661–1064nm. A full data acquisition was completed by sequentially acquiring
two sets of data, each of which consisting of simultaneous acquisition of three FD and
three CW wavelengths. Using a novel gain adjustment scheme in the PMTs, the data
acquisition time for each simultaneous acquisition has been reduced from 90 to 55
seconds, while signal variation was also reduced from 2.1% to 1.1%. An adjustable
interface was designed to fit different breast shapes and sizes. All components were
integrated into a portable system, which allows robust measurements in the infusion unit.
The performance of the hybrid NIRST system was validated in phantom experiments
(Chapter 5), normal subject exams (Chapter 7) and breast cancer exams (Chapter 8). The
design and characterization of the 12-wavelength system was published by Zhao et al
[180].
9.1.3 Silicone soft-gel based tissue mimicking phantom for NIRST imaging
In Chapter 5, several major tissue-mimicking phantoms were compared, and a
novel silicone soft-gel based tissue mimicking phantom was developed and characterized.
The proposed silicone soft gel phantom used A-341 silicone as base material. Different
coloring paints were used to provide scattering and absorption, respectively. The
preparation procedure is significantly simplified compared with that of making gelatin
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and traditional resin/RTV phantoms, since it does not require either vacuum or
microwave. The whole procedure can be completed within one hour. The phantom
presents stable optical properties over several months, which makes it an ideal candidate
in repetitive measurement for quality assurance and routine calibration.
The concentration of pink paint in the phantom affects both a and s in a linear
way, while that of white paint only affects s linearly. By altering the concentration of
white and pink paints, specific a and s can be obtained. A series of large breast tissue
mimicking phantoms were made as well, with sphere-shape inclusions of different sizes.
Such phantom was imaged at different depths, and the reconstructed images presented a
strong depth dependence of recovered contrast. Eventually, the heterogenous phantom
was used to validate the feasibility of NIRST imaging with partial
transmission/reflectance data. The experiments results suggest that reasonable image
reconstruction can be achieved using only one side of reflectance data.
9.1.4 Collagen quantification using the NIRST system
In Chapter 6, tomographic images of breast collagen content have been recovered
for the first time, and image reconstruction approaches with and without collagen content
included have been validated in simulation studies and normal subject exams. When
collagen was not included into the reconstruction, overestimate in recovered HbT, water
and lipid, as well as underestimate in StO2, have been observed. Both simulation and
patient studies showed that the recovered inclusion/background contrast in HbT increased
when collagen was included into the reconstruction, in the presence of background
collagen.
9.1.5 Imaging normal subjects
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Chapter 7 discussed imaging of normal subjects using the hybrid NIRST system.
A group of ten normal subjects were imaged on both sides of the breast. The adjustable
breast-fiber interface proved to work well for various breast sizes and breast densities. A
strong correlation between NIRST recovered properties and radiographic breast density
was observed, where significantly (p<0.05) higher HbT and water contents were found in
the high-density group relative to the low-density group. Two normal subjects were
measured sequentially for 30 minutes and the standard deviation of 8 continuous
acquisitions was found to be less than 5%.
9.1.6 Imaging breast cancer patients
Chapter 8 presented case studies of monitoring tumor response to NAC using the
NIRST system. An optimized workflow of data acquisition and image reconstruction was
developed to get reliable optical recovery in clinical conditions.
9.2 Future Directions
The work presented in this thesis improved NIRST breast imaging from different
perspectives. In this section, three potential directions are proposed based on preliminary
results, to further improve the performance of NIRST in breast cancer diagnosis and
treatment monitoring.
9.2.1 Optimization of NIRST system for monitoring patient response to NAC
The design of a multi-channel hybrid FD-CW NIRST system with simultaneous
acquisition has been introduced in this thesis. The performance of the NIRST system may
be further improved with the addition of more FD channels (phase data). Optical image
reconstruction using FD measurement with both amplitude and phase data has shown
superior performance to that using CW measurement with only amplitude data, in the
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case of single wavelength NISRT image reconstruction. However, as shown in Chapter 3,
the improvement in the spectral image reconstruction with the addition of phase data was
not significant in MRI-guided NIRST using hard-prior image reconstruction, since the
effect of phase data can be partially replaced by the spatial prior information obtained
from MRI images. As a result, better localization of tumor in the reconstructed images
can be expected with the addition of more phase data, when no spatial prior information
is available.
As shown in Chapter 8, the shape of the breast was approximated by a 2D
football-shape FEM mesh, which was created using the separation between two sides of
the fiber-breast interface. Such approximation is valid only when certain boundary
conditions were satisfied. In the case of small breast imaging, the simplified 2D
approximation might be insufficient for accurate modeling of the breast tissue.
Multimodality NIRST imaging with MRI/Ultrasound can be used to provide 3D patient
specific mesh for NIRST image reconstruction, which is not feasible in the current setup.
Instead, thanks to the rapid progress in consumer-grade electronics, range sensing devices
such as time-of-flight (ToF) camera and structured light imaging system, have the
potential to provide high-resolution 3D point cloud map of the surrounding object, from
which 2D/3D breast FEM mesh can be created.
9.2.2 Optimization of sampling geometry in MRI-guided NIRST
PMT detectors are usually expensive and bulky in size. In a typical fiber based
FD NIRST system, a large array of PMT detectors are needed to sample enough source-
detector pairs in terms of both amplitude and phase data. The reduction of phase data
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without significantly sacrificing system performance can help simply the design of next-
generation FD NIRST systems.
It has been found in section 3.2 that we can still differentiate the malignant vs.
benign lesions based on recovered optical contrast, using phase data for only
homogeneous fitting of initial guess, but not necessary in the reconstruction. This can be
explained by the assumption that in MRI guided NIRS tomography, hard priors
segmented from MRI images can provide similar spatial information phase data used to
provide. As a result, phase data is not necessary during the reconstruction procedure in
the case of hard-prior reconstruction. However, phase data is still needed to get initial
guess of SA and SP. In this section, we systematically investigated the NIRST
reconstruction with limited phase data.
Figure 9.1. Flowchart outlining the sequence for two reconstruction methods.
As shown in Fig. 9.1, when both amplitude and phase data are used to construct
the Jacobian matrix, noted as AMPL/PH, both absorption derived parameters such as
chromophore concentrations of HbT, StO2, water and lipid, and also scattering derived
parameters including SA and SP, can be recovered; when only amplitude data is used to
construct the Jacobian matrix, only absorption derived parameters will be recovered.
Note, even for the AMPL reconstruction method, scattering parameters are still needed to
be assigned in the initial guess to initialize the reconstruction procedure. For either
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AMPL/PH or AMPL method, both amplitude and phase data are fitted into the model to
get a complete set of initial guesses by assuming the tissue is homogeneous. The process
has historically provided a reasonable starting point for the image reconstruction
algorithm [45].
(a) (b) (c) Figure 9.2. Triangular interface with different sampling geometries. Three strategies of choosing phase measurements, (a) with full transmission across many sources and detectors, (b) with just partial reflectance data, and (c) with just partial transmittance data. Fiber locations are shown as blue dots.
The utilization of triangular patient interface has been discussed previously by
Mastanduno et al [96]. The adjustable triangular interface is designed to place 16 fiber
optical bundles via position 1 through 16, as shown in Fig. 9.2. There are three strategies
of taking phase measurements, (a) with full transmission across many sources and
detectors, (b) with just partial reflectance data, and (c) with just partial transmittance data.
To better understand how the number of detectors and sources affects the accuracy of
initial guess of scattering properties, initial guesses with a series of detector and source
combinations are obtained and further compared.
139
Figure 9.3. (a) Illustration of triangular patient interface for optical fiber placement. (b) Relative difference versus detector number.
Both the triangular interface and patient breast do not have geometrical symmetry
at different optical bundles. As a result, different fiber bundles and thus corresponding
PMT/PD detectors can have various importance from the image reconstruction
perspective. To evaluate the importance of a single detector, the relative error in terms of
SA and SP between homogeneous fitting with phase data from all 15 PMT detectors and
that using only one single PMT detector are plotted in Fig. 9.3(b).
Taking SA for instance, local minimum of fitting difference occurs at detector #
3 and #10, which indicates that they are the most critical positions for detector placement.
Similarly, the other positions can be sorted form high to low priority based on the fitting
difference. Moreover, we also can notice that SA (blue) and SP (red) shows similar
pattern with respect to detector position. Eventually, all the positions are sorted with
corresponding priority.
140
Figure 9.4. (a) Relative difference of optical contrast versus number of FD detectors used for homogeneous fitting of initial guess. ROC curves with 4 detectors (b), 6 detectors (c) and 15 detectors (d) for estimating initial guess.
Next, we tried to use phase data from limited number of PMT detectors for
estimating of initial guess of scattering properties, and compare with that using complete
phase data in terms of tum/ad contrast. As shown in Fig. 9.4(a), the relative difference of
tumor/adipose contrast is plotted versus number of PMT detectors. From 0 to 14, phase
data of a given number of sorted PMT detectors with highest priority are used for
estimating initial guess. Taking the number of 6 FD detectors for instance, PMT detectors
of position #2, #3, #4, #9, #10, and #11 were included. With increasing number of FD
detectors included, the relative difference in terms of contrast decreases due to the
141
addition of more phase data. Note that there exists a significant drop between 4 and 6 FD
detectors. 4 detectors are still not enough to get accurate initial guess, with a difference of
more than 10%, while 6 detectors yield in a relatively small difference of less than 3%.
As a result, we are able to get small enough variation in tumor contrast to the surrounding
normal tissue, by reducing the number of FD detectors from 16 to 6, showing the
potential of reducing the FD detectors. By comparing Fig. 9.4(b) through Fig. 9.4(d), we
can also find that 4 detectors and 15 detectors show similar statistical performance, p-
value=0.005 & AUC=0.82, versus p-value=0.004 & AUC=0.84, respectively in terms of
HbT. Meanwhile, 4 detectors correspond to a much worse AUC of 0.71.
Table 9.1. Initial guess of SA and SP for four categories grouped by MRI-identified breast density.
As we have discussed, the number of FD detectors could be as low as 6 without
sacrificing the reconstruction accuracy. This could be further improved with a lookup
table based approach. Mean values of four groups with different breast densities, and all
patients are listed in terms of SA and SP, as shown in Table 9.1.
Reconstruction of tumor to adipose contrast with initial guess from two lookup
tables, and that fitted with phase data (AMPL), are compared in Fig. 9.5 in terms of p
value and AUC for HbT and TOI. For HbT, both Table I and Table II have similar
performances (p value below 1% and close AUC), and are better than the first method of
AMPL (AUC of 0.88). Similar pattern exists for TOI among three methods. Therefore,
we can differentiate between malignant and benign patients without using any phase data
142
for either estimating initial guess or reconstruction. Such conclusion shows the potential
of hardware simplification by removing FD modules in MRI guided NIRS tomography,
while keeping a reasonable statistical performance.
Figure 9.5. ROC curves of AMPL reconstruction (a), Lookup table I (b), and lookup table II (c) for HbT, and AMPL reconstruction (d), lookup table I (e), and lookup table II (f) for TOI.
In this section, MRI guided NIRS optical tomography with limited phase data was
investigated. The optical contrast values for HbT, StO2, TOI, and scattering parameters
were estimated using amplitude only and both amplitude and phase data. A systematic
optimization of the system hardware design has been conducted as well. A statistical
difference (p<0.05) occurred between malignant and benign groups in terms of HbT and
TOI. We are able to get less than 3% variation in tumor contrast to the surrounding
normal tissue, by reducing the number of FD detectors from 16 to 6, showing the
potential of reducing the FD detectors. Furthermore, a lookup table of the scattering
143
properties has been made to replace that fitted from measured phase data. To the best of
our knowledge, these results represent the first time an extensive study of reconstruction
with limited phase data has been conducted on a relatively large amount of clinical breast
exam data with the MRI/NIRST multi-modality imaging approach. Such result would
potentially help in the design of next generation MRI coupled NIRS system and
optimized reconstruction method.
9.2.3 Imaging small tumors using MRI-guided NIRST
It’s always challenging to diagnose small size breast tumor with MRI-guided
NIRST. In this section, the limitation of the detectible size of tumor in MRI-guided
NIRST was investigated through simulation studies. The tumor size was defined as the
equivalent tumor diameter. The tumor region was first segmented from the MRI T1
images, and the whole tumor volume was calculated by adding all the tumor elements
with corresponding sub volume. Then equivalent tumor diameter was calculated as the
diameter of an imaginary sphere with the same volume of the tumor. The tumor region
can be dilated/eroded in the MRI mesh to increase/decrease the equivalent tumor
diameter. 2D mesh of equivalent tumor diameter of 12.2mm, 37.7mm and 46.8mm are
shown in Fig. 7(a)-(c), respectively.
Figure 9.6. Equivalent tumor diameter of 12.2mm (a), 37.7mm(b) and 46.8mm (c). Three regions of tumor, fibroglandular and adipose are represented in white (region 3), yellow (region 2) and red (region 1), respectively.
144
Tumor sensitivity is another critical indicator characterizing the relative coverage
of optical measurement of the tumor region, which was defined by Mastanduno et al. [56]
and suggests that no diagnostic significance was found when measured amplitude and
phase data with very low region of interest sensitivity were included in the analysis. In
practice, a minimum relative tumor sensitivity of 1% was required for filtering patient
data.
As shown in Fig. 9.7, the recovered HbT of tumor (black asteroid) is plotted
versus equivalent tumor diameter, with corresponding tumor sensitivity represented by
red square. The tumor sensitivity increases when equivalent tumor diameter increases for
both patients, since larger tumor tends to be covered more given the fixed sampling
geometry. The recovered HbT of tumor is close to that of background with small
equivalent tumor diameter (<9mm), shown in Fig. 9.7(a). For the other patient, the
minimum equivalent tumor diameter becomes 5mm (Fig. 9.7(b)).
Figure 9.7. Recovered HbT (black asteroid) of tumor and adipose (black circle) are plotted versus equivalent tumor contrast, for patient #51 (a) and #30 (b), respectively. The corresponding tumor sensitivity is represented by red square.
Figure 9.8 shows the recovered tumor/adipose contrast versus actual
tumor/adipose contrast for the tumors with equivalent tumor diameter of 8mm, 10mm,
12mm, 20mm and 40mm, respectively. The recovered tumor/adipose contrast presents
145
similar pattern when the tumor size is relatively large (20 and 40 mm), while the
recovered contrast increases much slower versus actual contrast for small tumor with
equivalent tumor diameter of 8-12mm. Aggressive regularization techniques can be used
to recover objective/maximum contrast in the case of small tumor.
Figure 9.8. Recovered tumor/adipose contrast versus actual tumor/adipose contrast for the tumor with equivalent tumor diameter of 8mm, 10mm, 12mm, 20mm and 40mm, respectively. A fixed regularization parameter of 1 was used in the image reconstruction.
Finally, the role of phase data in the recovery of HbT of tumor was investigated
for tumor with small size. The recovered HbT of tumor and adipose was plotted versus
equivalent tumor diameter, using FD/CW reconstruction (Fig. 9.9(a)) and CW
reconstruction (Fig. 9.9(b)), respectively for the same patient #30. Using FD/CW
reconstruction with both amplitude and phase data, the minimum equivalent tumor
diameter with recoverable contrast is 5mm, and the recovered HbT of tumor stays
relatively stable when tumor diameter increases higher than 5mm (Fig. 9.9(a)). By
contrast, the minimum equivalent tumor diameter increases to 8mm using CW
146
reconstruction, and the recovered HbT of tumor has much larger variation as well (Fig.
9.9(b)).
Figure 9.9. Comparison between FD/CW reconstruction (a) and CW reconstruction (b), with increasing equivalent tumor diameter. The recovered HbT of tumor and adipose are represented by blue asteroid and red square, respectively.
To conclude, the limit on the size of tumor with recoverable optical contrast in
MRI-guided NIRST was investigated through simulation studies, using the patient MRI
mesh with actual optical sampling geometry acquired from our clinical dataset. The
tumor was dilated from the original tumor size, corresponding to increasing equivalent
tumor diameter and increasing tumor sensitivity. Minimum equivalent tumor diameter of
5mm was found. Moreover, FD/CW reconstruction shows better performance than CW
reconstruction in the case of small tumor.
147
Appendix A: LabVIEW Acquisition Program
A.1 Front panel: Program initialization
(1) Program initialization
(2) Patient/phantom measurement mode (default)
(3) Calibration of PMT/PD detectors
148
A.2 Front panel: Data acquisition
(1) Switch to the “Acquisition” tab
(2) Choose the path of the folder where patient data will be saved
(3) Type patient ID
(4) Choose one from the measurement categories: “homo”, “hetero”, “left”, “right”
(5) Set visit number of a specific patient
(6) Choose one acquisition number: 13 and 14 for a complete measurement
(7) Click “Start Acquisition” once (1) to (6) have been set
149
Appendix B: Matlab Codes
Function Name Description
calibration_FD_wavelength Calibrate the AC amplitude and phase
using the PMT calibration file
calibration_CW_wavelength Calibrate the AC amplitude using the PD
calibration file
calibration_all_wavelength Calibrate amplitude and phase of all the
wavelengths
plot_lnri_fd Plot lnri and phase vs. s-d distance for FD
measurement.
plot_lnri_cw Plot lnri vs. s-d distance for CW
measurement.
reconstruction_spectral_fdcw Reconstruction of patient/phantom optical
images using hybrid FD-CW data.
reconstruction_single_wavelength Reconstruction of absorption/scattering
images using single wavelength data.
visualization_reconstructed_images Visualization and image processing of the
reconstructed optical images
para_mesh_creation Make 2D football shape mesh with various
separations between two plates
reconstruct_spectral_fdcw_n
Reconstruction of optical images using
hybrid data set. The choice of
regularization, stopping criterion, and
other constraints can be adjusted.
recon_GUI GUI for data calibration, image
reconstruction and visualization.
tumor_dilation_MRI Varying the size of tumor in MRI breast
images.
Optimal_regualrization_L_curve Get optimal regularization parameter using
L-curve analysis
150
Appendix C: Itemized Components List Itemized Component Quantity
Multi-channel RF synthesizer (HS2004, Holzworth Instruments) 1
6x1 fiber optic combiner (Fiberguide, Striling, New Jersey) 2
Photomultiplier tube (PMT, H9305-3, Hamamatsu, Japan) 15
Photodiode module (PD, C10439-03, Hamamatsu, Japan) 15
Bidirectional RF switch (401-220802A-R0HS, Dow-Key Microwave) 3
Coaxial frequency mixer (ZP-1-S+, Mini-Circuits) 3
Coaxial Bias-Tee (ZFBT-282-1.5A+, Mini-Circuits) 3
Power Splitter (ZFSC-2-1-S+, Mini-Circuits) 3
DC power supply (LS35-5, TDK-Lambda) 4
4-Channel SPDT Relay Board (RLY104, Winford, USA) 4
Laser Diode Driver (IP250-BV, Thorlabs) 6
Quadruple 2-Input Positive-AND Gates (SN74LS08N, Texas Instruments)
6
Laser Diode Driver (LD-1255, Thorlabs) 6
Collimation lenses (F220SMA-B, Thorlabs) 12
Microwave cable (Thorlabs) 30
Laser Diodes at 661nm, 730nm, 785nm, 808nm, 830nm, 850nm, 852nm, 905nm, 915nm, 940nm, 975nm and 1064nm
12
Computer System (SL-2U-AH110M-WD, SuperLogics) 1
16 Ch Voltage Output Module for USB (NI 9264, National Instruments) 1
Multifunction I/O Device (USB-6255, National Instruments) 1
Multifunction I/O Device (USB-6259, National Instruments) 1
3-meter long bifurcated fiber bundles (Customized) 16
151
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