MONITORING TUMOR THERAPEUTIC RESPONSE WITH … · MONITORING TUMOR THERAPEUTIC RESPONSE WITH...

MONITORING TUMOR THERAPEUTICRESPONSE WITH DIFFUSE OPTICAL

SPECTROSCOPIES

Ulas Sunar

A Dissertation

in

Physics and Astronomy

Presented to the Faculties of the University of Pennsylvania in Partial

Fulfillment of the Requirements for the Degree of Doctor of Philosophy

2006

Arjun G. Yodh

Supervisor of Dissertation

Randall Kamien

Graduate Group Chairperson

c© Copyright 2006

by

Ulas Sunar

Dedication

To Semra and My Family

In Memory of My Father ...

iii

AcknowledgementsSeveral years ago before coming to Penn I decided to study applied physics. To me

medical physics research area seemed to be best candidate. The concepts were new and

one directly works for improving human health. Dr. Yodh lab served for fulfilling my

aims for this respect. I would like to thank Dr. Yodh for helping me stay focused, for

teaching me to focus on what needs to be done first, and for creating a dynamic interactive

laboratory so that people improve themselves.

Dr. Chance was an interesting personality to interact with during my Ph.D. years.

I would like to thank him for discussing with me on any subjects whenever I needed. I

always appreciated the way he interacts: he would be always ready for discussions, critical

thinking, and brainstorming. Even if he could be busy, he would not turn me down but

say “just a minute” and he would finish what he was busy on then discussion would start.

8-am-in-the-lab discussions were the most fruitful to me in understanding the subjects as

well as learning how to approach to a problem critically. I also appreciated when he was

doing the “dirty work”, repairing an instrument. Shoko (Dr. Nioka) was also very effective

for my Ph.D. She provided me the clinical project for my thesis and always was ready to

help whenever I needed. I also enjoyed her “motherhood” she provided while she was my

landlord for three years (and later). She would invite me when she cooked nice Japanese

food and a nice chat naturally would follow up on the dinner table.

I was lucky to have Xavier (Dr. Intes) as my roommate in Shoko’s house. Although we

could not work on the same project together for unrelated reasons we had the opportunity

to develop friendship while he was hanging with our Turkish gang. We went out, enjoyed

parties, and played soccer together, leaving memorable moments (I will never forget when

he scored to our gollies!).

I would like to thank the people in Yodh’s lab: Turgut was always ready to discuss,

and his practical mind was very helpful when I needed in the lab. Regine was also very

iv

helpful, especially her recent help on measurements greatly reduced the pressure on me

during my thesis and paper writing period. Chao was always available whenever I needed

urgent help and support. Guoqiang’s electronic knowledge reduced my time waste in

instrumentation. I enjoyed Alper’s basic knowledge and chat in our office. From Soren

and Kijoon, I enjoyed their numerical and optical knowledge, and Jonathan introduced us

basic neuroscience concepts. I did several experiments with Hsing-Wen and I learnt a lot

from her about the whitelight system. With David I had the opportunity to discuss on TRS

system, at the same time I enjoyed his good company (maybe because our families are

farmers!). Leonid introduced me basic experimental and instrumental skills. I enjoyed his

Russian chocolates and St. Petersburg stories. I had interacted with many people in Dr.

Chance’s lab. Gunay was a good friend, Zhao was always ready to help me. Jun and Juan

helped me in clinical experiments. I also would like to thank Lanlan, Masha and Yu Chen.

I appreciate very much the help from Dr. Intae Lee. We together did experiments

at nights and at weekends. I also enjoyed collaborating with Dr. Gang Zheng and Dr.

William Lee. I thank them all for their trust and for providing their lab facilities to pursue

preclinical research. I did further preclinical experiments with Peter, Hui, Sosina, Lisa

and Diane.

I would like to thank Dr. Kamien, Dr. Soven, Dr. Drndic and Dr. Bernstein for being

in the committee. Dr. Kamien was always helpful during my Ph.D. years. I should also

add Pat, Dot and Tom, for their help related to paper drafting, administration, and business

issues. Mike and Bill were great help in machine shop.

Lastly I wish to thank my family. They showed me what’s important in life. I also

thank Semra, my wife and best friend, for her continuous support, love and for always

being with me. We share our future together.

v

Abstract

Monitoring tumor therapeutic response with diffuse opticalspectroscopies

Ulas Sunar

Arjun G. Yodh

The diffuse optical technique using Near-Infrared (NIR) light provides a promising

means for non-invasive imaging and clinical diagnosis of deep tissues. During the last few

years, we have developed a multi-modal diffuse optical technique combining two qual-

itatively different methodologies: Diffuse Reflectance Spectroscopy (DRS) and Diffuse

Correlation Spectroscopy (DCS). This approach permits real-time, non-invasive and si-

multaneous quantification of tissue hemoglobin concentration, blood oxygen saturation

and blood flow. The instrumentation is portable and rapid, and it has enabled us to study

tissue responses in a variety of physiological contexts from cancer treatment monitoring

to functional imaging of brain.

In this thesis I focus on monitoring of tumor responses to therapies in preclinical and

clinical contexts. In preclinical applications, I investigate an antivascular therapy in ani-

mal models. The effects of an antivascular drug, Combretastatin, were monitored continu-

ously and were found to induce substantial reduction of blood flow and tissue oxygen. The

observations of blood flow and oxygenation were then correlated with power Doppler Ul-

trasound and EF5 (hypoxia biomarker) techniques, respectively. In another animal model

application, the chemotherapy drug, Onconase (Onc), was tested. Onc enhances the ther-

apeutic effects of the drug Cisplatin, which is currently used as a chemotherapeutic agent

for head and neck patients during chemoradiation therapy. Our observations demonstrated

that Onc increased both tissue blood flow and tissue blood oxygenation; we also compared

vi

our results with those from MRI/MRS measurements.

The diffuse optical technique was then translated to the clinic, i.e. head and neck

patients during chemo-radiation therapy. Our pilot study with eight patients revealed sig-

nificant early changes in hemodynamic parameters suggesting that daily optics-based ther-

apy monitoring during the first two weeks of chemo-radiation therapy may have clinical

promise. Total hemoglobin concentration, blood oxygen saturation and blood flow during

treatment showed variable sensitivity to the therapy for different individuals, thus empha-

sizing the need for simultaneous monitoring of multiple tissue parameters and the potential

for individualized treatment planning.

vii

Contents

Dedication iii

Acknowledgements iv

Abstract vi

List of Tables xiii

List of Figures xvii

1 Introduction 1

1.1 Tumor Functional Parameters and Therapeutic Response . . . . . . . . . 2

1.2 Thesis Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Diffuse Optical Spectroscopies . . . . . . . . . . . . . . . . . . . . . . . 7

1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 Theoretical Background 11

2.1 The Photon Diffusion Approximation . . . . . . . . . . . . . . . . . . . 12

2.2 The Semi-infinite Medium Approximation . . . . . . . . . . . . . . . . . 15

2.3 General Fitting Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.3.1 Evaluating Goodness of Fit . . . . . . . . . . . . . . . . . . . . . 19

2.4 Extraction of Optical Properties Using Frequency Domain Measurements 20

viii

2.4.1 Extraction of µa, µ′s by the Multi-distance Method . . . . . . . . 20

2.4.2 Extraction of µa, µ′s with Intralipid Calibration . . . . . . . . . . 22

2.5 Extraction of Optical Properties Using Continuous Wave Measurements . 23

2.6 Physiological Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.6.1 Traditional Fitting for Physiological Parameters . . . . . . . . . . 27

2.6.2 Direct Multi-spectral Fitting for Physiological Parameters . . . . 28

2.7 Diffuse Photon Correlation Spectroscopy . . . . . . . . . . . . . . . . . 29

3 Experimental Methods 33

3.1 Instrument Design Requirements . . . . . . . . . . . . . . . . . . . . . . 33

3.2 Diffuse Reflectance Spectroscopy Instruments . . . . . . . . . . . . . . . 35

3.2.1 The Frequency Domain Instrument . . . . . . . . . . . . . . . . 36

3.2.1.1 RF Generation Module . . . . . . . . . . . . . . . . . 37

3.2.1.2 Laser Module . . . . . . . . . . . . . . . . . . . . . . 38

3.2.1.3 Detection Module . . . . . . . . . . . . . . . . . . . . 39

3.2.1.4 Dynamic Range and Linearity Tests . . . . . . . . . . . 41

3.2.1.5 Validation In Vitro: Intralipid Titration Tests . . . . . . 42

3.2.2 The Broadband CW Spectroscopy Instrument . . . . . . . . . . . 45

3.2.2.1 Validation In Vitro . . . . . . . . . . . . . . . . . . . . 46

3.3 The Diffuse Correlation Spectroscopy Instrument . . . . . . . . . . . . . 49

3.3.1 Validation In Vivo: Arm Cuff Ischemia . . . . . . . . . . . . . . 50

3.4 Optical Probe Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4 Preclinical Applications 53

4.1 Non-invasive, Continuous Monitoring of Antivascular Tumor Therapy . . 53

4.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.1.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . 55

ix

4.1.2.1 Animal and Tumor Models . . . . . . . . . . . . . . . 55

4.1.2.2 Contrast-enhanced Ultrasound Imaging . . . . . . . . . 55

4.1.2.3 Tumor Histology and Immunohistochemistry . . . . . . 56

4.1.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . 56

4.1.3.1 Combretastatin Induces Significant Blood Flow Reduction 56

4.1.3.2 Combretastatin Induces Significant Blood Oxygen Sat-

uration Reduction . . . . . . . . . . . . . . . . . . . . 59

4.1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4.2 Monitoring a New Chemotherapy Drug (Onconase) . . . . . . . . . . . . 61

4.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.2.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . 62

4.2.2.1 Animal and Tumor Models . . . . . . . . . . . . . . . 62

4.2.2.2 Magnetic Resonance Spectroscopy . . . . . . . . . . . 63

4.2.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . 63

4.2.3.1 Onconase Enhances Radiation Response . . . . . . . . 63

4.2.3.2 Onconase Induces Significant Blood Flow and Oxygen

Saturation Increase . . . . . . . . . . . . . . . . . . . 64

4.2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5 Clinical Applications 68

5.1 Head and Neck Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

5.2 Molecular and Cellular Basis of Radiation Therapy . . . . . . . . . . . . 70

5.2.1 Physical Interactions . . . . . . . . . . . . . . . . . . . . . . . . 70

5.2.2 Biological Effects of Radiation . . . . . . . . . . . . . . . . . . . 72

5.2.2.1 Free Radicals . . . . . . . . . . . . . . . . . . . . . . 72

5.2.2.2 Oxygen Effect in Radiation Therapy . . . . . . . . . . 72

x

5.2.3 Tumor Hypoxia and Therapy . . . . . . . . . . . . . . . . . . . . 74

5.3 Clinical Radiation Therapy . . . . . . . . . . . . . . . . . . . . . . . . . 75

5.3.1 Importance of Fractionation and Re-oxygenation . . . . . . . . . 76

5.4 Non-invasive Diffuse Optical Measurement of Blood Flow and Blood Oxy-

genation for Monitoring Radiation Therapy in Patients with Head and

Neck Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.4.2 Clinical Instrumentation . . . . . . . . . . . . . . . . . . . . . . 83

5.4.2.1 The Clinical Diffuse Correlation Spectroscopy (DCS)

Instrument . . . . . . . . . . . . . . . . . . . . . . . . 84

5.4.2.2 The Clinical Diffuse Reflectance Spectroscopy (DRS)

Instrument . . . . . . . . . . . . . . . . . . . . . . . . 85

5.4.3 Measurement Protocol . . . . . . . . . . . . . . . . . . . . . . . 87

5.4.4 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 88

5.4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

5.4.5.1 Average rBF Response . . . . . . . . . . . . . . . . . 95

5.4.5.2 Average StO2 Response . . . . . . . . . . . . . . . . 97

5.4.5.3 Average THC Response . . . . . . . . . . . . . . . . 97

5.4.5.4 Average µ′s Changes . . . . . . . . . . . . . . . . . . 98

5.4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

5.4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.5 Future Work: Detection and Monitoring of Primary Head and Neck Tumors 103

5.5.1 Early Detection (Autofluorescence Spectroscopy) . . . . . . . . . 103

5.5.2 Early Therapy Monitoring (DRS and DCS) . . . . . . . . . . . . 104

5.5.3 The Hybrid Instrument . . . . . . . . . . . . . . . . . . . . . . . 104

xi

6 Summary and Future Prospects 107

Bibliography 108

xii

List of Tables

2.1 Reff values for different interfaces. . . . . . . . . . . . . . . . . . . . . . 17

3.1 Titration test results for λ = 780 nm. . . . . . . . . . . . . . . . . . . . . 48

5.1 Characteristics of patients with head and neck cancer. . . . . . . . . . . . 89

5.2 Individual tumor (t) and arm muscle (m) relative blood flow changes (rBF (%))

at the end of week-1, week-2, week-3 and week-4 of chemo-radiation ther-

apy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

5.3 Weekly blood oxygen saturation (StO2 (%)) changes during chemo-radiation

therapy for tumor (t) and arm muscle (m). . . . . . . . . . . . . . . . . . 91

5.4 Weekly total hemoglobin concentration (THC (µM )) changes during chemo-

radiation therapy for tumor (t) and arm muscle (m). . . . . . . . . . . . . 92

xiii

List of Figures

1.1 Schematic model of the tumor functional parameters (vascular end-points)

accessible to diffuse optical spectroscopies. . . . . . . . . . . . . . . . . 2

1.2 Diagram showing some differences between normal and tumor blood ves-

sels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 (a) Tissue absorption over wide spectrum. (b) NIR spectral window. . . . 8

2.1 (a) Spherical diffuse photon density waves (DPDWs). (b) Amplitude de-

cay and phase shift of DPDWs. . . . . . . . . . . . . . . . . . . . . . . . 14

2.2 Semi-infinite medium model. . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3 Fitting scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4 Reflectance data fit with residuals. . . . . . . . . . . . . . . . . . . . . . 20

2.5 Logarithm of the product of squared source-detector separation (ρ) and

RF amplitude (Amp) versus ρ, and Phase shift versus ρ. . . . . . . . . . 21

2.6 Sensitivity (S) as a function of source-detector distance (ρ) with µa = 0.10

cm−1, and µ′s = 8 cm−1. . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.7 (a) Photons scattering from static scatterers and from blood cells. (b) De-

cay rate of autocorrelation intensity fluctuations related to blood flow. . . 29

3.1 RF instrument. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.2 RF generation module. . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.3 RF modulation of laser diodes . . . . . . . . . . . . . . . . . . . . . . . 39

xiv

3.4 Detector block. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.5 I/Q homodyne detection demodulator. . . . . . . . . . . . . . . . . . . . 40

3.6 Linearity and dynamic tests. . . . . . . . . . . . . . . . . . . . . . . . . 42

3.7 Linearity test. (a) Output power vs. input power. (b) Linear fit error. . . . 43

3.8 Absorbance and scattering spectra of Intralipid solution. . . . . . . . . . 44

3.9 Titration experiment results with RF instrument for (a) µa and (b) µ′s. . . . 44

3.10 Broadband CW spectroscopy instrument. . . . . . . . . . . . . . . . . . 45

3.11 (a) Image on CCD detector. (b) Small probe for pre-clinical applications. 46

3.12 StO2 and BV (blood volume) measured in hemoglobin phantoms using

broadband reflectance spectroscopy vs. oxygen partial pressure measure-

ments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.13 (a) Measured and calculated reflectance spectra. (b) µ′s titration test for

large source-detector separations. . . . . . . . . . . . . . . . . . . . . . . 48

3.14 Blood flow instrument. . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.15 Arm cuff ischemia experiment. . . . . . . . . . . . . . . . . . . . . . . . 51

3.16 Optical probe used in clinical setup. . . . . . . . . . . . . . . . . . . . . 52

4.1 (A) DCS recordings show the acute effects of CA4P. (B) Power Doppler

ultrasound perfusion image. (C) Vasculature destroyed. (D) Perfusion

reduced. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.2 Histology showing untreated (control) and CA4P-treated tumor sections. . 58

4.3 Mean percentage change in StO2 (A). EF5 immunofluorescence for con-

trol (B) and treated (C). . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.4 Blood volume before and after CA4P injection. . . . . . . . . . . . . . . 60

4.5 Growth delay assay after treatment with Onc and X-radiation. . . . . . . 64

4.6 Mean percentage change in relative flow (A). Mean percentage change in

blood oxygen saturation for (B). . . . . . . . . . . . . . . . . . . . . . . 65

xv

4.7 Histogram of StO2 distribution from all mice. . . . . . . . . . . . . . . . 66

4.8 Time dependence of lactate and ATP levels after i.p. administration of 10

mg/kg of Onc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.1 Head and neck nodes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

5.2 (a) Photon and electron depth-dose curves. (b) Schematic illustration of

the energy deposited by a charged particle along its track in tissue. . . . . 71

5.3 Effect of oxygen as a radiosensitizer. . . . . . . . . . . . . . . . . . . . . 73

5.4 Part of the tumor surrounding capillary. (a) Oxygen concentration de-

creases with increasing distance from the capillary. (b) Surviving fraction

of cells increases with distance from the capillary. . . . . . . . . . . . . . 75

5.5 Radiation repair responses of normal and tumor tissues. . . . . . . . . . . 76

5.6 Tumor shrinkage with radiation. . . . . . . . . . . . . . . . . . . . . . . 77

5.7 Clinical instrument in radiation treatment room. . . . . . . . . . . . . . . 83

5.8 (a) Schematic diagram of the flow instrument. (b) Hand-held optical probe. 84

5.9 (a) Schematic diagram of frequency-domain instrument. (b) Optical probe. 86

5.10 Treatment and measurement schedule. . . . . . . . . . . . . . . . . . . . 87

5.11 Tumor relative blood flow changes (rBF (%)), blood oxygen saturation

(StO2) and total hemoglobin concentration (THC) during chemo-radiation

therapy for one of the responding patients (P-1). . . . . . . . . . . . . . . 93



therapy for partial responder (P-8). . . . . . . . . . . . . . . . . . . . . . 94



therapy averaged over all patients excluding P-8. . . . . . . . . . . . . . 95

xvi

5.14 Tumor scattering coefficient changes (µ′s(cm−1)) during chemo-radiation

therapy for an average of patients P1 to P7. . . . . . . . . . . . . . . . . 98

5.15 Proposed hybrid instrument for autofluorescence, blood flow and blood

oxygenation measurements. . . . . . . . . . . . . . . . . . . . . . . . . . 105

xvii

Chapter 1

Introduction

Cancer treatment depends on factors such as tumor type, tumor stage and patient health.

For advanced-stage tumors, neoadjuvant therapies such as chemotherapy, radiation ther-

apy or combination therapy (i.e. chemo-radiation therapy) are often used to decrease

tumor size before surgery; in this case tumor resection and organ preservation is feasi-

ble. Sometimes patients gain complete recovery from neoadjuvant therapies. On the other

hand, some patients do not exhibit favorable response to such treatment. These varied

responses are due to differences in tumor functional parameters across the patient pool.

The effectiveness of cancer treatment is judged mainly by tumor size reduction. How-

ever, tumor functional changes (e.g. changes in tumor metabolism) due to treatment often

precede gross structural changes. Thus methods sensitive to tumor functional changes

are of interest for assessing treatment efficacy early and for adjusting the treatment plan

accordingly.

Near-infrared diffuse optical spectroscopy is a noninvasive and portable technique

suited for detection and monitoring of functional parameters in deep tissues [27,169,183].

In this thesis I will describe pilot studies establishing the utility of diffuse optical tech-

niques in pre-clinical (therapy monitoring, drug testing in small animals) and clinical (head

1

and neck cancer in human subjects) contexts.

1.1 Tumor Functional Parameters and Therapeutic Re-

sponse

Number ofcells

(~Blood volume)(~Blood volume)ood volume

Figure 1.1: Schematic model of the tumor functional parameters (vascular end-points) ac-cessible to diffuse optical spectroscopies [166]. Blood flows through the tumor from thearterial side to venous side carrying many blood cells (number of blood cells ∼ blood vol-ume). Tumor cells near the microvessels are well oxygenated compared to those far fromthe microvessels. Tumor vessels with high permeability/surface-area exchange nutrientsand therapeutic drugs more efficiently with tumor tissues.

Tumor functional parameters (also called vascular end-points) inform tumor therapeu-

tic response [80,81,173]. Information about these quantities permits optimization of indi-

vidual treatment protocols that can result in an improved response to treatment. The tumor

functional parameters accessible to diffuse optical spectroscopies are blood oxygen satura-

tion, blood flow, blood volume1 and blood vessel permeability, as presented schematically

in Figure 1.1 [166]. As a result of the hyper-metabolic activities of tumor cells com-

pared to normal tissues, the oxygen supply will often be in deficit compared to oxygen

1In this thesis, blood volume and total hemoglobin concentration (THC) are used interchangeably.

2

demand; a state of hypoxia is thus created, with low tumor blood oxygen saturation, and

low oxygen partial pressure. Tumor oxygen status is known to affect radiation therapy

outcome [22,119]. In general, approximately two to three times higher radiation doses are

needed to kill hypoxic tumor cells compared to well-oxygenated cells [21, 69, 87, 173].

Normal

Blind end

Breakin vesselwall

Tumor Temporary occlusion

Arteriovenous shunt

Figure 1.2: Diagram showing some differences between normal and tumor blood ves-sels. Normal tissue has uniform and well-ordered blood vessels that are sufficiently closetogether to oxygenate all of the tissue. By contrast, blood vessels in tumors have slug-gish flow and often have regions of hypoxia between the vessels arterio-venous (AV)shunts [23].

Microvessels in tumors exhibit a series of severe structural and functional abnormali-

ties. Thus blood flow in tumors is generally quite different from normal tissues. Normal

tissues have a well organized and regular vasculature. On the other hand, blood vessels

in tumors are irregular, having arterio-venous (AV) shunts, blind ends, and leaky walls

(Fig. 1.2). As a result, blood flow is sluggish in tumors. This “sluggishness” can lead

to a poor oxygen supply and inefficient chemotherapeutic drug delivery. Tumor hypoxia

can also occur when too many tumor cells are too far from the blood vessels. According

to metabolic demand, tumor blood flow is typically higher than surrounding tissue [173].

As was first pointed out by Folkman [62], tumors also grow new blood vessels (angiogen-

esis) to supply nutrition and remove waste products. This condition can produce higher

3

blood volume in the tumor compared to surrounding normal tissues. The responsiveness

of tumors to therapies depends greatly on blood flow. In radiation therapy, adequate blood

flow is important for the tissue oxygen supply. In chemotherapy, adequate blood flow is

important for delivery of anti-angiogenic and antivascular drugs to tumor cells.

Tumor vasculature also has a tendency to be leaky (i.e. more permeable) [111]; con-

trast agents or drugs for example, can leak out of the blood vessels into the tumor tissue,

enhancing its effects in tumors compared to normal tissues. If tumor vessels are more per-

meable (and/or have more surface area), more contrast agent or drug uptake is observed.

Tumor permeability-surface area is therefore an important vascular end-point in antivascu-

lar, antiangiogenic therapies, since tumor uptake of injected drugs during these therapies

strongly depends on the tumor vessel-permeability/surface-area product [161, 162].

1.2 Thesis Motivation

This thesis focuses on noninvasive monitoring of the therapeutic responses of tumors via

assessment of tumor vascular parameters. Several recent studies have shown that therapy

monitoring on a daily basis is important [82]. Current traditional methods for monitoring

therapies are positron emission tomography (PET), computer tomography (CT), magnetic

resonance imaging (MRI) and ultrasound. All of these techniques rely on injection of a

contrast agent to assess vascular end-points. They use pharmacokinetics of the contrast

agent to extract tumor vascular end-points such as blood flow and permeability. How-

ever, quantification is not easy [1, 166]. PET has molecular imaging advantages, but the

contrast agents are expensive to produce and not widely available [1]. Dynamic contrast

enhanced magnetic resonance imaging (DCE-MRI) is more readily available in the hospi-

tals, but it is expensive, immobile and gadolinium-based contrast agent pharmacokinetics

depends on both blood flow and the vascular-permeability/surface-area product, making

4

results difficult to interpret [109, 166]. In dynamic CT, ionizing radiation is required [75].

Ultrasound and laser Doppler flow (LDF) techniques are also valuable for monitoring

tumor vascular end points. Laser Doppler flow is primarily surface sensitive [76] and

ultrasound Doppler is generally more sensitive to larger blood vessels [1], although sev-

eral recent reports suggest higher sensitivity to smaller vessels [67]. The oxygen-sensitive

micro-electrode needle method provides a “reference standard” for measurement of tumor

oxygenation [21, 154, 173], but it is highly invasive [96] and is not widely used in the

United States.

The near-infrared (NIR) diffuse optical spectroscopies presented in this thesis are ideal

for noninvasive and repetitive monitoring. They offer a minimally-invasive, rapid, portable

and low-cost alternative for continuous monitoring of tumor responses with quantification

of therapeutically important functional vascular parameters. Apart from blood vessel per-

meability, all vascular parameters can be extracted non-invasively.

The concept of noninvasive repetitive measurements fits nicely with recent research

on vascular targeting agents that further modulate the response and sensitivity of tu-

mors to therapies such as antivascular therapy, radiation therapy and combinations thereof

[160, 166]. Investigators have demonstrated potential therapeutic benefits of targeting tu-

mor vasculature, and preclinical models of an antivascular drug Combretastatin A-4 3-O-

Phosphate (CA4P) have confirmed that CA4P enhances the effects of radiation [115,116].

To facilitate clinical translation of agents that target tumor vasculature, an ability to assess

tumor vessel blood flow and oxygen saturation with repetitive measurements is desirable,

and an ability to perform frequent measurements is particularly advantageous.

CA4P is currently in clinical trials, and very recent studies have showed that this drug

enhances radiation therapy response. It was used in a clinical study at the Hospital of

University of Pennsylvania for radiation therapy response monitoring of rectal carcinomas

[142]. It is possible, in the future, that head and neck cancer patients may benefit from

5

CA4P during their radiation therapy treatment. Furthermore, many new contrast agents

(molecular beacons) are being developed to increase specificity and sensitivity in tumor

detection and therapeutic delivery. New molecular beacons might have direct impact on

therapy monitoring: with higher sensitivity and specificity, one can monitor tumors in

their early stages, which should lead to better survival rates. These drugs should be tested

first in preclinical settings, and diffuse optical methods can be used to monitor these drugs

continuously in this context (see Chapter 4).

Our ultimate goal is to apply diffuse optical methods routinely in the clinic. This the-

sis reports initial results from head and neck cancer patients who were monitored during

their chemo-radiation treatments. Head and neck cancers have a worldwide incidence of

197,000 deaths per year [135]. During last two years, approximately 300 patients enrolled

for radiation treatment at the Hospital of University of Pennsylvania, and 140 patients

were diagnosed with head and neck cancer, the second most common tumor type after

breast cancer. Despite aggressive surgery and radiation therapy, which may result in ma-

jor functional loss, the survival rate of patients with head and neck cancer has remained

relatively unchanged over the past 3 decades. In an effort to improve survival, chemo-

radiation therapy has been incorporated into the initial nonsurgical management of newly

diagnosed cases. Predictive indices based on tumor morphologic features or clinical char-

acteristics are not very accurate. Hence, there is potential for instruments that reliably

monitor and predict the early responses of these tumors.

This thesis examines early monitoring and the predictive power of diffuse optical spec-

troscopies by identifying specific metabolic changes as markers. The early blood flow and

oxygenation changes presented herein suggest the potential utility of daily basis mea-

surements in the early stages of therapy. In fact it has been suggested very recently that

the greatest tumor physiological (hemoglobin concentrations, water content, lipid con-

tent) changes may occur within the first week and that optical methods can pick up these

6

changes [82, 156]. Due to very low accessibility of the other techniques described ear-

lier, the diffuse optical methods have advantages for daily based therapy monitoring. As

Vaupel [173] noted, most clinical studies are anecdotal case studies, and there is an urgent

need for clinical data which involve statistically significant large populations. Noninvasive

diffuse optical methods have potential to assist in this endeavor.

1.3 Diffuse Optical Spectroscopies

Photons in the Near Infrared (NIR) spectral window penetrate deep (several centime-

ters) into living tissues [183, 184]. This property was successfully applied by Cutler in

1929 to examine the breast lesions by transillumination [43]. In 1977, Jobsis used a two-

wavelength spectroscopic approach to extract blood oxygenation in vivo and established

the field of oximetry [84].

Figure 1.3(a) shows the optical absorption properties of major chromophores for a

wide range of spectra. It is seen that most of the spectral region is dominated by high

absorption. However, there is a specific spectral region, the NIR therapeutic window be-

tween 650 nm and 950 nm, wherein absorption is low so that light can penetrate deep

in tissue. In the NIR “therapeutic window”, the main absorbing chromophores are oxy-

genated hemoglobin and deoxygenated hemoglobin (Fig.1.3(b)), whose net absorption

characteristics depend on oxygenation state, which in turn is affected by metabolic pro-

cesses [27, 28].

Near Infrared (NIR) spectroscopy has therefore emerged as a new, noninvasive tech-

nique to probe living tissue. With NIR spectroscopy one extracts the optical properties

(absorption and scattering coefficients) of living tissue. Absorption information is used to

characterize the concentration of biological chromophores, such as hemoglobin, which in

turn indicates physiological responses. Scattering measurements give information about

7

200 400 600 800 10000

10

20

30

40

50

Wavelength (nm)A

bsor

ptio

n C

oeffi

cien

t (cm

−1 )

HbO2

HbH

2O (x100)

(a)

650 700 750 800 850 900 9500

0.1

0.2

0.3

0.4

Wavelength (nm)

Abs

orpt

ion

Coe

ffici

ent (

cm−

1 ) HbO2

HbH

2O

"Therapeutic Window"

(b)

Figure 1.3: Absorption spectra in tissue. (a) Tissue absorption is dominant in most of thespectral region. (b) NIR “therapeutic” window. Absorption is relatively much lower andoxy-, and deoxy-hemoglobin are the main absorbers.

the composition, density, and organization of tissue structures, such as cells and subcel-

lular organelles [27, 169]. An increase in organelle population, particularly mitochondria,

sometimes accompanies the higher metabolic activity of the rapidly growing tumor and

leads to an increasing scattering coefficient for the tumor. Therefore NIR techniques can

provide information about disease-related functional and structural changes. It has been

shown recently that physiological changes such as ischemia, necrosis and malignant trans-

formation can perturb tissue optical properties [169].

8

Blood flow is another interesting intrinsic contrast measurable by the NIRS tech-

nique [18, 34, 183]. Diffusing photons sometimes scatter from moving blood cells which

cause the intensity of the diffusing light to fluctuate in time. The fluctuations are more

rapid for faster moving blood cells. Therefore, one can derive information about tissue

blood flow far below the tissue surface from measurements of temporal fluctuations im-

pressed upon light diffusing through tissue [19]. Information about tissue blood flow is ob-

tained using diffuse correlation spectroscopy (DCS) [18–20,34,73,108,133]. DCS probes

dynamical fluctuations in the turbid media, and transport of the field correlation function

resembles, formally, the photon diffusion modeling. Tumor blood flow measurements are

particularly attractive for assessing tumor response to therapies, since blood flow has been

correlated with tumor oxygenation [24,60,173]. Furthermore, in some treatments vascular

modulating or anti-angiogenic agents are used concurrently to increase the sensitivity of

tumors. Noninvasive methods for repetitive blood flow measurements which can moni-

tor responses to vascular modulating and anti-angiogenic agents should therefore enable

better planning of individualized therapies.

1.4 Thesis Outline

In this thesis, I quantified tumor responses to therapies with both preclinical and clinical

applications. Chapter 2 introduces basic theoretical concepts of photon diffusion. Analyt-

ical solutions for semi-infinite media are used to extract blood oxygen saturation, blood

volume and blood flow from noninvasive measurements.

Chapter 3 describes the experimental techniques that I used in the laboratory and in the

clinic. The instruments and their validation experiments in vitro and in vivo are described.

Section 3.2.1 describes the frequency domain instrument that was used in clinical mea-

surements. A CCD-based white light spectrometer, which was mainly used in preclinical

9

studies, is discussed in Section 3.2.2. The blood flow instrument is described in Section

3.3. The optical probe design requirements are discussed in Section 3.4.

In Chapter 4, preclinical applications are discussed. This chapter mainly gives proof-

of-concept, and it is also a supplement to the clinical studies. In Section 4.1, the results

from a study of a vascular targeting drug, CA4P, is presented. It is shown that CA4P

induced drastic blood flow reduction. One important feature of this study is that the non-

invasive blood flow and oxygenation results were compared with power ultrasound and

EF5 binding techniques and good correlations were found with these other modalities. In

Section 4.2, a chemotherapy drug, Onconase (Onc), was tested. This drug is an enhancer

of Cisplatin, which is currently being used as a chemotherapeutic agent for head and neck

patients at the Hospital of University of Pennsylvania and at other institutions. The results

were compared with MRI and Magnetic Resonance Spectroscopy (MRS).

Chapter 5 introduces basic principles of radiation therapy at the cellular level and

translates the concepts to clinical studies. Then I present clinical applications of noninva-

sive diffuse optical spectroscopies on head and neck tumors to monitor radiation therapy

response. The results revealed significant blood flow and oxygenation changes within two

weeks of the therapy, and suggest that the diffuse optical methods could be used in daily

based therapy monitoring.

Chapter 6 summarizes the thesis and discusses future prospects.

10

Chapter 2

Theoretical Background

In this chapter, I will describe the basic principles of photon migration in highly scattering

media such as tissue, and I will describe the current techniques and theories of diffuse

optical methods used in tissue characterization. The review is mainly based on the pub-

lications of Patterson et al. [88], Haskell et al. [72], and the dissertations from our group

by David Boas [18], Xingde Li [98] and Turgut Durduran [52]. This chapter is organized

as follows: Section 2.1 describes the diffusion approximation, which enables fast analytic

solutions in both preclinical and clinical settings. Section 2.2 derives the analytical solu-

tion for diffuse photon density waves (DPDWs) in semi-infinite homogeneous media. The

semi-infinite approximation is the primary analysis approach used in spectroscopic clini-

cal applications. In section 2.3 data analysis fitting schemes are briefly described. Section

2.4 describes the important physical parameters in tissues, and the extraction of absorp-

tion and scattering coefficients. Section 2.5 makes a transition from physical parameters

(i.e. optical properties) to physiologic parameters. Finally, Section 2.6 introduces diffuse

correlation spectroscopy (DCS) for calculating blood flow parameters.

11

2.1 The Photon Diffusion Approximation

In the near-infrared spectral window (650-950 nm) the absorption of light by tissues is

low, and light scattering is high. After injection of light into tissue, photons travel in the

form of random walk due to the multiple scattering of the medium. Photon migration in

tissue is most generally described by the transport equation [25, 48] or the diffusion ap-

proximation to transport equation. The diffusion approximation is more manageable from

a mathematical point of view and applies quite well under the assumptions of high scat-

tering and low absorption [78, 130, 170]. In a homogeneous medium, the photon fluence

rate (which is proportional to the photon number density), Φ(r, t) [Watt cm−2s−1], obeys

the time-dependent diffusion equation [18, 130]:

D∇2Φ(r, t)− vµaΦ(r, t) + vS(r, t) =∂Φ(r, t)

∂t. (2.1)

The diffusion equation accounts for the principles of conservation (balance) of photons

in the turbid medium. On the one hand, photons are produced from the sources. On the

other hand, they are lost as a result of escape from the system as well by absorption. In the

above equation, v is the speed of light in the medium, µa is the absorption coefficient, D

is the diffusion coefficient, and S(r, t) is the photon source term which gives the number

of photons emitted at position r and time t per unit volume per unit time [Watt cm−3].

D is related to the reduced scattering coefficient µ′s, i.e., D ∼= v/(3µ′s) [50, 53]. The

reduced scattering coefficient is the reciprocal of the photon random walk step, l? = 1/µ′s.

The photon random walk step is essentially the average distance traveled by the photon

before the photon’s propagation direction becomes randomized. The scattering coefficient

µs is the reciprocal of the scattering length. The scattering coefficient and the reduced

scattering coefficient are related by the single-scattering anisotropy factor, g, a measure of

how much of the incident light is scattered in the forward direction in a typical scattering

12

event. Specifically, g is the average of the cosine of the “single-scattering” scattering angle

(θ):

µ′s = µs(1− g) = µs(1− < cos(θ) >). (2.2)

There are three kinds of photon diffusion techniques commonly employed: continuous-

wave (CW), frequency-domain (FD) and time-resolved (TR). In the CW method, the light

intensity at the source and the detector is constant. In the FD technique, the light inten-

sity is sinusoidally modulated; thus both amplitude and phase of the sinusoidal diffusive

wave are measured by the detector. The time-resolved system (TRS) uses short input laser

pulses, and the broadening of these pulses due to multiply scattering is measured.

These techniques are related to one another; the time-resolved scheme is equivalent to

a frequency domain measurement over a wide range of modulation frequencies, and the

frequency domain technique reduces to the CW technique when the source modulation

frequency, ω, is zero. CW is the simplest method, and uses relatively cheap instrumen-

tation with fast data acquisition. However, because it only measures amplitude, it suffers

in quantification of both absorption and scattering parameters. TRS has the most infor-

mation content, but it requires relatively complex and expensive instrumentation which

limits clinical applications. Frequency domain systems are generally compact, mobile,

cost-efficient instruments that also permit extraction of both absorption and scattering pa-

rameters. In this thesis we will focus on frequency domain approach; we have found it to

be the easiest for use in the clinic. In the frequency domain approach, the source intensity

is modulated sinusoidally, i.e., S(r, t) = Soδ(r) exp(iωt). Here So is the source strength.

In this case one assumes Φ(r, t) = Φω(r) exp(iωt), and the diffusion equation simplifies:

D∇2Φω(r) + (−vµa + iω)Φω(r) = vSoδ(r). (2.3)

13

source

fiber

detector

fiber

Turbid medium

(a)

0 2 4 6 8 100.5

1

1.5

2

2.5

3

Time (ns)

Inte

nsity

(a.

u.)

Source

Detected DPDW

Phase shift

DPDWAmplitude

(b)

Figure 2.1: (a) Intensity modulated light produces spherical outgoing photon diffusedwaves. (b) Source and detected DPDWs showing attenuated amplitude and phase delay.

For simple geometries, analytical solutions to diffusion equation can be obtained. In an in-

finite homogeneous medium, for example, the only boundary restriction is that the fluence

rate vanishes at large distances from the source. The solution for a sinusoidally modulated

source at the origin is then [18, 72]:

Φ(r, t) =vSo

4πD

exp(−ikr)

rexp(iωt), (2.4)

where k is the wavenumber, k2 = (−vµa + iω)/D, ω is the angular modulation frequency,

and r = |r| is the distance between the source and point r. The solution implies that

14

when the source is amplitude modulated with angular modulation frequency ω, a macro-

scopic diffuse photon density wave (DPDW) is generated in the tissue and propagates

as an overdamped spherical wave outwards from the source. A detector at r measures

an amplitude-decayed and phase-shifted signal relative to the intensity modulated source

(Fig. 2.1). The detected diffusive wave has amplitude and phase that depend on the opti-

cal properties of the intervening media, e.g. tissue [18]. These diffusive waves interfere,

refract, scatter and diffract from optical heterogeneities [18].

2.2 The Semi-infinite Medium Approximation

In most noninvasive medical applications of in vivo diffuse optical spectroscopy, one

places both the light source and the detector on a tissue surface. The infinite-medium

scheme is not appropriate for such a geometry. A better approach is to consider a uniform

semi-infinite medium and to solve the diffusion equation with the appropriate boundary

conditions.

The semi-infinite approximation is used to extract physical and physiologic quantities

in all of the preclinical and clinical applications in this thesis. In the semi-infinite ge-

ometry, the source and detector fibers are arranged on the same surface plane (Fig. 2.2).

The solution for the semi-infinite geometry can be obtained by using extrapolated zero

boundary conditions [72, 130]. For a semi-infinite medium, the solution of the diffusion

equation is easily found by using image sources. An image of the real source is formed

by reflection of the real source about the plane of the “extrapolated boundary” (Fig. 2.2).

Furthermore, it has been shown that a light beam incident upon the source is well rep-

resented by a single point source at a depth zo equal to one effective photon mean free

path, i.e., zo = 1/µ′s. The parameter zo has a value of ∼ 1 mm in tissues. We observe

that this feature accounts for an effective isotropic photon source even if the photons are

15

Negative image

Source Detector fiber

Positive source

z +zob

b

o

z

z

nnout

in

z -z =

z -z = b

p

z 0=

Extrapolatedboundaryρ

fiber r

r

2

1

Turbid medium

Figure 2.2: Semi-infinite model: zb is the distance between the extrapolated boundary andthe surface of the medium and zo is the depth of the effective single scatter source insidethe scattering medium. The distance between the effective (image) source and the detectoris r1(r2). The projection of the source-detector distance onto the plane z = 0 is z.

actually injected in a single direction. Finally, we use image sources and the superposition

principle to obtain the solution at the detector on the surface of the semi-infinite medium:

Φ(r) =So

4πD

(exp(ikr1)

r1

− exp(ikr2)

r2

), (2.5)

with r1 = [ρ2+(zb+zo−z)2]1/2 and r2 = [ρ2+(zb+zo+z)2]1/2. Here zb = (1+Reff )/(1−Reff ) × (2/3µ′s) is the distance of the extrapolated boundary to the real boundary and k

is the complex wave-number defined as before. Reff is the effective reflection coefficient

16

on the interface [36, 52, 137]:

Reff = −1.440n−2 + 0.71n−1 + 0.668 + 0.00636n, (2.6)

where n = nin/nout, the ratio of the index of refraction of the “inside” to “outside” me-

dia. The values of Reff for different interfaces are listed in Table 2.1 [72]. The diffuse

Interface Type nin nout Reff zb

Air-Air 1.00 1.00 0 0.667Water-Air 1.33 1.00 0.431 1.667Tissue-Air 1.40 1.00 0.493 1.963

Table 2.1: Reff values for different interfaces.

reflectance, R, i.e the flux out from the tissue surface, is derived using Fick’s law [136]:

R(ρ, ω, µa, µ′s) = −k∇Φ(ρ, ω, µa, µ

′s)|z=0

=So

4πD

(zo(1 + kr1)

r31

exp(−kr1) +zb(1 + kr2)

r32

exp(−kr2)

)(2.7)

= A(ρ, ω, µa, µ′s) exp(iφ(ρ, ω, µa, µ

′s)), (2.8)

where A(ρ, ω, µa, µ′s) and φ(ρ, ω, µa, µ

′s) are the amplitude and phase, respectively, of the

detected DPDW.

2.3 General Fitting Schemes

In this section, our data analysis scheme is briefly described (see the book by Bevington for

a more detailed discussion [14]). The clinical frequency domain (FD) spectrometer that

will be described in detail in the Experimental Methods Chapter provides data at three

discrete optical wavelengths with four different source-detector separations. Continuous

17

NIR spectra are also available from a broadband CW spectrometer. In the latter case statis-

tically significant data with many (∼200) wavelengths and 5 source-detector separations

are used.

Nonlinear least squares fitting is used to fit the nonlinear diffusion model to data

and thus to compute an estimate of parameters pi, where pi denotes deoxy- and oxy-

hemoglobin concentrations and the scattering coefficient, i.e. pi = (CiHb, C

iHbO2

, µ′is ).

Fitting parameters are derived using an iterative approach with the following main steps

(Fig.2.3):

• Start with an initial estimate. Hemoglobin concentrations in tissue are in the range

of [0-200] µM and µ′s varies typically in the range of [3-20] cm−1. Thus an initial

parameter p0 = [20 µM, 20 µM, 8 cm−1] might be a reasonable starting guess.

• Compute a theoretical reflectance curve, Rth, for p0 and compare to reflectance data,

Rdata.

• Minimize the merit function χ2, the sum of the squares of the residual (χ2 =∑

(Rdata − Rth)2) until the convergence criteria met. The convergence criteria can

be defined as |χ2i+1−χ2

i |χ2

i< Conv, where χ2

i denotes χ2 at ith iteration and Conv is a

user defined convergence criteria (e.g. 1.0× 10−6).

• Update the parameter set pi (pi → pi+1). Parameter update depends on a minimiza-

tion algorithm. In each iterative algorithm selecting step size strategies are different.

For example, in gradient-search based minimization algorithms, if pi is the current

parameter, the next parameter is pi+1 = pi − C · ∇χ2(pi), where C > 0 is the step

size. Very small step sizes result in slow convergence, whereas step sizes that are too

large may overshoot the local minima. There are sophisticated iterative algorithms

(e.g. Levenberg-Marquardt Method) that optimize step size at each iteration.

All the steps are shown graphically below.

18

Update p p ---> p

i i+1

Introduce initial parameters (p , i=0)

Calculate Rth

χ2

reached minimum?NO YES

Obtain R data

p = pi

i

Figure 2.3: Fitting scheme.

2.3.1 Evaluating Goodness of Fit

After fitting the data, it remains to evaluate a goodness of the fit. Residuals and fit statistics

give information about the goodness of fit.

• Residuals: By definition residuals are differences between data and fit (r = Rdata−Rth). In the case of good fitting results, residuals should show a random behavior

with respect to independent variables (e.g. wavelength). However, if the residuals

display a systematic pattern with respect to independent variables, the model fits the

data poorly.

• χ2: χ2 measures the total deviation of the data from the fit. It is called the summed

square of residuals. A value closer to 0 indicates a better fit.

19

680 700 720 740 760 7800.360.38

0.40.420.440.46

R

680 700 720 740 760 780-0.01

0

0.01

resi

dual

λ (nm)

Figure 2.4: The fit of reflectance data obtained from broadband whitelight spectrometerand residual of the fit.

2.4 Extraction of Optical Properties Using Frequency Do-

main Measurements

Attenuation of diffuse light in living tissue is governed by both absorption and scattering

parameters with an effective attenuation coefficient, µeff =√

3µaµ′s. The Frequency Do-

main (FD) method relies on measurement of amplitude and phase information, which in

turn allow us to extract both the absolute absorption coefficient and the scattering coef-

ficient separately (i.e. it decouples absorption from scattering). We use two methods to

extract absolute absorption coefficients by using both amplitude and phase information:

the multi-distance method and Intralipid calibration.

2.4.1 Extraction of µa, µ′s by the Multi-distance Method

Gratton et al. [56, 61, 68] pointed out many years ago that analytic solutions obtained for

infinite and semi-infinite tissue allow us to recover optical parameters by a multi-distance

slope method. For the semi-infinite geometry, when the source-detector separation ρ À

20

1/µ′s, Eq. (2.5) can be simplified to:

Φsemi(ρ) =vS0

4πD

exp(ikρ)

ρ2[−2ik(z0zb + z2

b )], (2.9)

from which we can get the following linear relations:

ln(ρ2 A(ρ)) = −kiρ + ln(A0)

θ = krρ + θ0, (2.10)

Here A0 and θ0 are the instrument initial amplitude and phase, and ki and (kr) are the

imaginary and real parts of the DPDW wavevector. From the above equation one can infer

that logarithm of the product of squared source-detector separation (ρ2) and amplitude

(A), and the phase shift, θ, are both linear functions of ρ and the slopes of these curves

give the imaginary (ki) and real (kr) parts of the wavevector, respectively (Fig. 2.5).

1.5 2 2.5 32.5

2

1.5

1

0.5

ρ�� (cm)

Log(

ρ2 *Am

p)

(a)

1.5 2 2.5 32.5

2.6

2.7

2.8

2.9

ρ (cm)

Pha

se (

rad)

(b)

Figure 2.5: (a) Logarithm of squared source-detector separation (ρ) multiplied by RFamplitude versus ρ, and (b) Phase shift versus ρ shows a linear relationship for semi-infinite geometry.

It can be seen that the optical parameters can be recovered with this multi-distance

method; with a little bit algebra, it can be shown that absorption and scattering coefficients

21

can be recovered by:

µa =ω

2v(ki

kr

− kr

ki

)

µ′s =2v

3ωkikr. (2.11)

It should be noted that multi-distance method at large separations only works to sepa-

rate µa, µ′s in the frequency domain because of the additional phase information. For

continuous wave (CW) measurements (ω = 0), there is only amplitude information, and

amplitude attenuation governed by a decay rate ki = (µaµ′s)

1/2, where µa and µ′s are being

coupled. Therefore it may seem that it is not possible to independently recover µa and µ′s.

In the next section, however, we will see a possibility of recovering both optical properties

by the CW method with acquisition of information at many wavelengths (or over a broader

range of source-detector separations).

2.4.2 Extraction of µa, µ′s with Intralipid Calibration

This method utilizes the a-priori optical property information of a calibration phantom,

Intralipid. In the frequency domain (FD), the analytical form for the reflectance (R) for

the semi-infinite geometry is a complex number as described previously in Section 2.2

(Eq. 2.7). In this approach the amplitude (A) and the phase (θ) signals from tissue are

measured along with that of the reference Intralipid solution. Assuming the instrument re-

sponse functions are the same for the Intralipid and the tissue measurement, the instrument

responses cancel out when we normalize the measured tissue signals with the reference

signals. We define

An =Atissue

AIntralipid

, θn = θtissue − θIntralipid (2.12)

22

as the normalized amplitude and phase, respectively. Calibration corrected data is then fit

with a normalized analytical solution to diffusion equation in order to extract the optical

absorption and scattering properties of the unknown tissue. Matlab fitting algorithm (e.g.

Matlab lsqcurvefit fitting function utilizes Levenberg-Marquardt algorithm) can be used

to minimize the merit function:

χ2 =∑

i

(Amn,i − Aa

n,i)2 + (θm

n,i − θan,i)

2 (2.13)

Here i stands for data points and the superscripts ‘m’ and ‘a’ denote the ‘measured’ and

‘analytical’ values, respectively. Iteration is done until χ2 reaches it’s minimum. See Sec-

tion 2.3 for fitting scheme details. It should be noted that the fitting results in an estimate

of µa, µ′s since the optical properties of calibrated phantom are assumed to be well-known

(for example, they can be extracted by multi-distance method). This approach has the

effect of normalizing out the instrumental systematic uncertainties (i.e. instrumental am-

plitude and phase offsets/shifts) with fewer measurements.

2.5 Extraction of Optical Properties Using Continuous Wave

Measurements

Although the Continuous Wave (CW) method has less information content than the fre-

quency domain (FD) method per source-detector-wavelength pair, the instrumentation

simplicity, low-cost, size and speed make it very attractive for both preclinical and clinical

applications. Especially when employing broadband white light measurements, the CW

method provides “real” spectroscopic power, since it offers information at many wave-

lengths. Moreover, it has been shown very recently that, with optimal choice of wave-

lengths it is possible to separate absorption and scattering coefficients [40]. With these

23

advantages in mind, Farrell et al. [57], Nichols et al. [117], and Solonenko et al. [150]

showed it is possible to recover absorption and scattering coefficients of living tissue by

spectroscopic measurements at many wavelengths and with multiple source-detector sep-

arations. The details of the fitting algorithm are described elsewhere [176]. The method

requires the measurements of amplitude of diffuse photon density waves both at short and

long separations. Both short and long separations are preferred since reflectance mea-

surements are more sensitive to the scattering coefficient at short separations and to the

absorption coefficient at long separations [77, 117, 180]. Indeed, if we plot the sensitivity,

S, of the reflectance equation (Eq. (2.7)) with respect to separation (ρ) for µa = 0.10

cm−1, and µ′s = 8 cm−1, it is seen clearly that absorption sensitivity increases and scat-

tering sensitivity decreases with increasing ρ. Here we defined sensitivity S(µa) as the

ratio between a relative variation of the measured quantity, i.e., the slope of ln[R], and

the relative variation of µa when µ′s is kept constant [8]. The same definition is used for

S(µ′s). In other words, if m is the slope of ln[R] (i.e. m = ddr

ln[R]), then S(µa), S(µ′s)

are defined by

S(µa) =∣∣∣ ∆m/m

∆µa/µa

∣∣∣µ′s=const

∼=∣∣∣µa

m

∂m

∂µa

∣∣∣µ′s=const

,

S(µ′s) =∣∣∣ ∆m/m

∆µ′s/µ′s

∣∣∣µa=const

∼=∣∣∣µ

′s

m

∂m

∂µ′s

∣∣∣µa=const

. (2.14)

During the measurements, the whitelight CW signal may be affected by background

light (B(λ, ρ)) and therefore background light should be subtracted from the real mea-

sured tissue reflectance signal (Rtissue(λ, ρ)). Furthermore to calibrate the instrument at

each measurement, an integrating sphere is used, so that wavelength dependent fiber-to-

fiber variations and the white light source strength variations can be subtracted out. We

24

10−1

100

0

0.2

0.4

0.6

0.8

1

ρ (cm)

Sen

sitiv

ity

S(µa)

S(µs′ )

Figure 2.6: Sensitivity (S) as a function of source-detector distance (ρ) with µa = 0.10cm−1, and µ′s = 8 cm−1.

obtain calibrated measured reflectance data as:

Rm =Rtissue(λ, ρ)−B(λ, ρ)

Rsphere(λ, ρ)−Bsphere(λ, ρ), (2.15)

where Rsphere(λ, ρ) and Bsphere(λ, ρ) are reflectance spectra obtained from an integrating

sphere when the whitelight source is on and off, respectively. While Equation (2.15) can

be used to analyze the data, it is convenient to normalize the data to the diffuse reflectance

measured at a known distance, ρo [117]. This eliminates the need for a scaling factor that

would be dependent on experimental conditions. Therefore, to get the absolute values of

µa and µ′s, we minimize the merit function:

χ2 =∑

λ

∑ρ

∣∣∣∣Rm(ρ, λ, µa, µ

′s)

Rm(ρ = ρo, λ, µa, µ′s)− Rc(ρ, λ, µa, µ

′s)

Rc(ρ = ρo, λ, µa, µ′s)

∣∣∣∣2

. (2.16)

Simultaneous measurement of both very small and large separations is not always

practical when we are dealing with large tissue volumes (due to the large signal dynamic

25

range). One way of getting around this problem is to use frequency domain and CW in-

struments concurrently to extract the baseline µ′s of the medium by using FD data and then

fit for µa using all the wavelengths of whitelight CW data [13]. In my clinical measure-

ments, I used frequency domain measurements to constrain µ′s and large source-detector

separations to extract both µa and µ′s. Another way of getting around this problem is to

use the approximation suggested by Liu et al. [99]. When the source-detector separation ρ

is larger than 20-40 mean transport free paths (1/µ′s), the reflectance solution in Eq. (2.5)

can be approximated as [33, 99]:

R ≈ 1

µt

(µeff +1

ρ)exp(−µeffρ)

ρ2(2.17)

where µt = µa +µ′s , µeff =√

3µaµ′s, representing total and effective attenuation, respec-

tively. After rearranging the terms and taking logarithm of both sides,

ln[ρ2R(ρ)] = −µeffρ− ln(µt) + ln(µeff +1

ρ) (2.18)

Liu et al. [99] studied the linear dependence of Eq. (2.19) equation with respect to ρ. They

showed with simulation that for large source detector separations (ρ > 2 cm), replacing

the third term with a constant gives an error of less than 5%. Then Eq. (2.19) reduces into:

ln(ρ2R(ρ)) ≈ −µeffρ− ln(µt) + ln(µeff +1

ρo

). (2.19)

Here ρo is taken as midpoint of minimum and maximum source detector separations. Us-

ing multi detector separations larger than ρ > 2 cm and calibration model, one can extract

µeff and µt, consequently µa and µ′s. The advantage of this approach is that one can

extract optical parameters possibly with only two separations without the dynamic range

constraint, and it is directly applicable to head and neck tumor patient monitoring, and

26

muscle and brain functional studies, which require large tissue volume measurements.

2.6 Physiological Parameters

Whichever method used, our main aim is to extract clinically relevant physiological pa-

rameters; making a transition from “physical” contrasts of optical absorption and scat-

tering coefficients to physiological contrasts of blood oxygen saturation and hemoglobin

concentration is our next task. Blood oxygen saturation can be compared with a blood

test analysis, and total hemoglobin concentration can be directly compared with clinical

hemoglobin and hematocrit levels obtained by blood sample evaluations.

2.6.1 Traditional Fitting for Physiological Parameters

Extraction of blood oxygen saturation can be obtained easily by writing the absorption co-

efficient as a linear combination of oxy-, deoxy- hemoglobin concentrations. In the NIR,

differences between normal and abnormal tissue can be seen by extracting the concen-

trations of deoxy-, oxy-, and total hemoglobin (Hb), using multi-wavelength and multi-

source/detector separation information. The concentrations can be obtained by solving the

linear equations coming from the Beer-Lambert law [147]:

µλa = ελ

HbO2CHbO2 + ελ

HbCHb + µbackgrounda . (2.20)

Here ε is the extinction coefficient of the given chromophore at the given wavelength, CHb

and CHbO2 are the concentrations of Hb, oxygenated Hb, respectively, and µbackgrounda

is the background absorption coefficient mainly coming from water and lipid absorption

in tissue. After obtaining the hemoglobin concentrations, one can derive percent blood

27

oxygen saturation, StO2, and total hemoglobin concentration, THC, as [147]:

StO2 =CHbO2

CHb + CHbO2

· 100, (2.21)

THC = CHb + CHbO2 . (2.22)

These parameters (CHb, CHbO2 , StO2 and THC) provide unique physiological informa-

tion about the tissue that in some cases is related to tumor metabolism and angiogen-

esis [169], brain functional activities [164, 174] and muscle oxygenation during exer-

cise [118].

2.6.2 Direct Multi-spectral Fitting for Physiological Parameters

Another way to extract physiological parameters is to use nonlinear fitting algorithm to fit

all the data directly for chromophore concentrations. Physiologically relevant parameters

of oxy-, deoxy-, and total hemoglobin concentrations (CHbO2 , CHb, THC) are extracted

using DRS data with multi-wavelength (λ) and multi-source/detector separations (ρ). A

meta-multi-wavelength fitting algorithm is applied directly to all the data to extract CHb,

CHbO2 and the scattering coefficient. We can easily derive tissue absorption from the fitted

information; e.g. if absorption is only due to oxy- and deoxy-hemoglobin, then µa =∑

i εiCi (i = Hb,HbO2). Here εi is the extinction coefficient of the ith chromophore at

a given wavelength and is obtained from the literature [138]. It is also often assumed that

tissue scattering follows a Mie-type behavior [114] in the near infrared spectral window,

i.e. µ′s = Aλ−B. In the meta-analysis we minimize the merit function to directly extract

chromophore concentrations, A and B:

χ2 =∑

λ

∑ρ

∣∣Rm(ρ, λ, Ci, A, B))−Rc(ρ, λ, Ci, A,B))∣∣2, (2.23)

28

where Rm is the measured and Rc is the calculated diffuse reflectance for the semi-infinite

geometry as before. After obtaining the oxy- and deoxy-hemoglobin concentrations, one

can derive total hemoglobin concentration THC, and blood oxygen saturation StO2 as

before. We have tested the multi-wavelength and multi-distance fitting algorithm exten-

sively with Intralipid titration, and we have found good correlation between extracted and

expected values of optical properties (Chapter 3).

2.7 Diffuse Photon Correlation Spectroscopy

Laser APD

Moving blood cells Intensity fluctutations

(a)

10−6

10−4

0

0.2

0.4

0.6

0.8

1

τ (sec)

g 1(τ)

Low flowHigh flow

(b)

Figure 2.7: (a) Photons injected by laser light scatter from static scatterers and from bloodcells, which introduce temporal intensity fluctuations at the avalanche photo-detector(APD). (b) Decay rate of autocorrelation intensity fluctuations related to blood flow: mov-ing blood cells introduces endogenous flow contrast (sharper decay, higher blood flow).

In the previous sections I have discussed the static physical properties of tissue, namely

absorption and scattering parameters. In this section I show that diffusing photons can also

be used to probe for dynamical information such as blood flow.

Near-infrared photons diffuse through thick living tissues [183, 184]. When diffusing

29

photons scatter from moving blood cells they experience phase-shifts which cause the in-

tensity of detected light on the tissue surface to fluctuate in time. These fluctuations are

more rapid for faster moving blood cells. Therefore, one can derive information about

blood flow far below the tissue surface from measurements of temporal fluctuations im-

pressed upon diffusing light (Fig.2.7).

Details of the diffuse photon correlation (or diffusing wave spectroscopy) method can

be found elsewhere [19, 20, 34, 108, 133]. Briefly, the normalized temporal intensity auto-

correlation function of the diffused light,

g2(r, τ) =< I(r, t) · I(r, t + τ) >

< I >2, (2.24)

is measured on the tissue surface. Here I(r, t) is the diffuse light intensity at position r,

and time t, < ... > denotes a time average and τ is the autocorrelation time delay. The

electric field of the diffusing light, E(r, t), is also characterized by a temporal autocorre-

lation function, G1(r, τ) =< E∗(r, t)E(r, t + τ) >. Usually it is derived from measure-

ments of g2(r, t) using the Siegert relation [12], g2(r, τ) = 1 + β|G1(r, τ)|2/ < I >2=

1 + β|g1(r, τ)|2; here β is a constant that depends on source and detection experimental

parameters such as the number of detected speckles and g1(r, τ) = G1(r, τ)/ < I > is

the normalized electric field correlation function.

It has been shown that the electric field autocorrelation function, G1(r, τ), in dynamic

turbid media satisfies the steady-state diffusion equation [19, 20, 73]:

∇2G1(r, τ)− (3µaµ′s + αk2

oµ′2s < ∆r2(τ) >)G1(r, τ) = −3µ′sS(r). (2.25)

Here µa, µ′s are the average absorption and scattering coefficients of the underlying

medium as defined previously, and can be obtained from DRS measurements. ko is the

wavenumber of light in the medium, and α is a factor representing the probability that

30

a scattering event in tissue is from a moving scatterer such as a red blood cell. It is

proportional to tissue blood volume fraction. < ∆r2(τ) > is the mean squared displace-

ment of the scatterers in the turbid medium in time interval τ . The exact form of the

autocorrelation function depends on measurement geometry, tissue optical properties, as

well as on the model that describes the nature of the particle motion. The mean squared

displacement in the “effective” diffusion model is < ∆r2(τ) >= 6DBτ . In the main

text we define Γ = αDB. The mean squared displacement in the random flow model is

< ∆r2(τ) >=< v2 > τ 2, where < v2 > is the mean square velocity of the scatterer in the

vasculature. We adopt the effective diffusion model for our analysis.

As with the diffuse reflectance spectroscopy (DRS) measurements, diffuse correlation

data collected in the reflectance geometry is readily analyzed by solving this diffusion

equation using the semi-infinite medium approximation [72]. The analytical form of the

autocorrelation function within the semi-infinite approximation can be obtained from the

image source approach following Kienle and Patterson [88] as described previously in see

Section 2.2. In particular, for semi-infinite homogeneous fluctuating turbid medium and

for point sources of the form S(r) = S0δ(r), the electric field autocorrelation function on

the tissue surface is

G1(r, τ) =3µ′sS0

4π

(exp(−kr1)

r1

− exp(−kr2)

r2

), (2.26)

where k2 = 3µ′sµa + 6µ′2s k2oΓτ . Here µa, µ

′s are the average absorption and scattering

coefficients of the underlying medium. These can be obtained from DRS measurements,

ko is the wavenumber of light in the medium, and r1(r2) is the distance between source

(image source) and the detector on the surface. Γ = αDB characterizes temporal fluc-

tuations in the medium due to scatterer motions such as blood flow. Here α is expected

31

to be proportional to tissue blood volume fraction. DB is an “effective” diffusion coef-

ficient for the blood cells. It should be noted that the effective diffusion coefficient need

not (and generally is not) be the “thermal” Brownian motion predicted by Einstein [54];

non-thermal random forces in the vasculature can also give rise to diffusive particle (cell)

motions. It is assumed herein that measured relative changes of αDB are proportional

to relative changes in tissue blood flow. Larger Γ implies faster electric field (or inten-

sity) autocorrelation function temporal decay and higher blood flow (Fig. 2.7). A detailed

microscopic model relating tissue blood flow to Γ is not available; it is the subject of cur-

rent research. Nevertheless, the proportional relationship has been verified [52, 187, 188]

against a variety of traditional blood flow/perfusion measurement methods in a variety of

physiological contexts. We will adopt this relationship as a fundamental assumption in

our experimental approach. We note here that another related microscopic interpretation

which takes “bulk” blood displacement into account explicitly has been proposed recently

in the context of laser Doppler flowmetry [16, 100].

Using a Matlab (Mathwork, Inc.) fitting function (e.g. lsqcurvefit), the flow pa-

rameter, Γ, and the experimental constant, β, are obtained by minimizing the differ-

ence between the predicted analytical form of the autocorrelation function in the re-

flectance geometry, g1,th(r, τ), and the measured autocorrelation function, g1,exp(r, τi),

i.e., χ2 =∑

i

(g1,th(r, τi) − g1,exp(r, τi)

)2

. The exact form of the autocorrelation func-

tion depends on measurement geometry as well as on tissue optical properties. Here we

report rBF . rBF is a blood flow index defined as the blood flow parameter (Γ) relative

to its pre-treatment value (in percent units with 100% implying no change). rBF is thus

unitless.

32

Chapter 3

Experimental Methods

In this chapter, I describe the instruments that were used for my preclinical and clinical

measurements. First I describe the diffuse reflectance spectroscopy instruments. Fre-

quency domain instruments were used mainly for clinical applications, and continuous

wave whitelight spectroscopy instruments were used for preclinical applications. Next,

the diffuse correlation instrument that was used for blood flow measurements is briefly

described. Validation experiments for each instrument are also presented.

3.1 Instrument Design Requirements

The instrument designs were chosen to address different physiological issues:

• The sensitivity of the instruments must be high enough to probe deep into human

tissues. Since photon penetration depth scales with source-detector separation [58,

129], an optical probe with large source-detector separations is needed, light sources

with sufficient intensity are needed, and sensitive detectors are needed.

• The instruments and data acquisition need to be rapid and portable in clinical set-

tings.

33

• Since clinical therapy monitoring lasts about 7 weeks, the instruments must provide

repeatable results and must not be prone to systematic errors due to probe placement,

etc.

• In the preclinical applications, the probed tissue volume is fairly small, so that a

robust instrument with a small optical probe is preferred.

• In the preclinical therapeutic drug testing experiments, we are sometimes interested

in pharmacokinetics, requiring an instrument with high time resolution and ability

for continuous monitoring.

We have achieved high sensitivity by optimizing the signal-to-noise ratio (SNR) and by

upgrading to faster optical switches which have low optical loss. The DCS instrument was

optimized with fast photon counting detectors. In cases of low signal, longer averaging

times were used and multiple detection fibers at roughly the same spatial location were

averaged to improve SNR. In clinical applications it was desirable to extract blood flow

and oxygenation at the same time. Therefore, a hybrid, compact and mobile instrument

was developed for the clinical environment. The combined instrumentation scheme was

first introduced by Cheung et al. [34] in rat brain experiments. A frequency domain (70

MHz) diffuse optical tomography instrument with 4 source positions, 3 wavelengths (690

nm, 786 nm, 830 nm) and four detector channels working in parallel were combined

with a diffuse correlation spectroscopy instrument, which has a continuous wave, long

coherence length laser source operating at 786 nm. Source positions and wavelengths

were multiplexed by a series of optical switches, which required approximately 3 seconds

per data point in our clinical measurements.

The previous generation of this instrument had only a single channel with very slow

data acquisition; furthermore, the source fibers were changed manually, introducing quan-

tification problems. Moreover, because a photomultiplier tube (PMT) was used on the

34

detection side of the instrument, the measurements had to be carried out in a dark room,

a situation difficult for patients and medical doctors. My instrument utilizes fast optical

switches and four detector channels in parallel, allowing measurements to be done in a

couple of minutes. Moreover, inclusion of large active area (3 mm diameter) avalanche

photo-diodes (APDs) on the detection side permitted measurements to be done in room

light while the patients were waiting for their radiation treatments, and for a few cases of

hospitalized patients, bed-side monitoring could be done.

It should be stressed that these improvements critically increased participation in our

study; for example, 10 patients enrolled in our study for the last year, while only 3 patients

enrolled for a related MRI study. Moreover, the faster instrument enabled us to include

blood flow measurements in our protocols with no significant increase in measurement

time. Furthermore, rapid parallel data acquisition permitted repeated measurements with

different observers. Needless to say, variability tests with different observers are very im-

portant for acceptance of this technique in clinical settings, and for capture of statistically

significant large-scale clinical data in the future.

For preclinical applications, the broadband whitelight spectroscopy instrument with a

hand-held small optical probe permitted satisfactory extraction of intrinsic blood flow and

blood oxygenation contrasts in small tissue volumes.

3.2 Diffuse Reflectance Spectroscopy Instruments

In this section, I present two different instruments for blood oxygenation measurements:

a frequency domain (FD) instrument, and a continuous wave (CW) broadband whitelight

instrument. The FD instrument was used in clinical settings, and the CW broadband white-

light instrument was used for preclinical applications. Then I briefly describe the diffuse

correlation spectroscopy (DCS) instrument, which was used for blood flow quantification.

35

Validation experiments for each instrument are also presented.

3.2.1 The Frequency Domain Instrument

1 2 4 5 6 7 8 9 103

Figure 3.1: RF Instrument consists of NIM boxes. 1: RF generator. 2: 690 nm and 780nm laser diodes with drivers. 3: 830 nm laser diode with its driver. 4: APD block fortesting. 5: Optical switch. 6: DAQ card. 7-10: APD blocks.

A four-channel frequency-domain instrument was constructed for clinical studies (Fig.

3.1). The instrument was designed in a modular form in that each part was put in a differ-

ent Nuclear Instrumentation (NIM, Mech-Tronics, IL) box for extra noise shielding. The

instrument consists of ten NIM boxes: (1) a 70 MHz signal generator (Wilmanco VSA-70

MHz-17 dBm); (2) laser diodes at 690 nm and 780 nm (Thorlabs, Inc); (3) a third laser

diode, 830 nm (Thorlabs, Inc); (4) a 3 mm diameter avalanche photodiode (APD) (Hama-

matsu C5331-04) for testing the unit; (5) two 1 by 2 optical switches (Dicon Fiberoptics,

Inc) to switch laser diodes; (6) a Data Acquisition Card (DAQ6034, National Instruments);

(7) to (10) are the APD blocks for detection.

36

An RF (70 MHz) signal from a signal generator is split into two parts. One part is

directed to the laser diode drivers to modulate the light intensity, and the other part is

used as a reference signal for demodulation. The intensity-modulated light is delivered to

tissue and the diffuse light is collected by four 3 mm diameter APD’s at the same time.

After amplifying and filtering, the signal from the APD is mixed with a reference channel

through the in-phase and in-quadrature (IQ) demodulator (Mini-Circuits), and converted

into I and Q components which carry the amplitude and phase information. After the low-

pass filter, the DC signals from I and Q components are used to calculate the amplitude

and phase of the diffused photon density waves passing through tissue.

In the following sections the components are described briefly.

3.2.1.1 RF Generation Module

12-16 V DC

Generator 70MHz

Wilmanco, 13dBm

ZFSC-3-1

1*4 RF splitter

Minicircuit R

To laser 830 nm driver



Reference channel to

4 detector modules

RF amplifier

Minicircuit ZHL-2010

Figure 3.2: RF generation module. RF generator produces 70 MHz RF signal, which isamplified by Minicircuit ZHL-2010 RF amplifier. Amplified signal is split into four parts,one is directed to reference channel and the other three are used to modulate the laserdiodes.

The RF source is shared by three lasers and a reference signal. The RF source module

produces a 70 MHz signal, which is divided into four by a splitter (Mini-Circuits). Three

37

signals are used to modulate three laser diodes and one signal is used as a reference for

the IQ demodulator (Fig. 3.2).

3.2.1.2 Laser Module

Three laser diodes with different emission wavelengths were used (Sanyo, 780 nm and

Hitachi, 680 nm and 830 nm). They are custom fiber coupled (OZ Optics, Canada). The

lasers have different package configurations. Because this instrument was built to be used

for clinical applications, a compact laser driver was needed. Therefore, a very compact

and cheap (∼$100) laser driver (Ld1100, Thorlabs Inc.) was chosen for our requirements.

The Ld1100 is a general-purpose analog hybrid circuit for use in ultrastable laser diode

driver applications. The Ld1100 maintains precise laser diode current (constant current

mode) or stable photodiode current (constant power mode) regulation using electronics

that are compatible with many laser diode types. For high power laser diodes, up to 200

mA of current can be applied from a single +12V power supply. The operating currents of

our laser diodes were below 200 mA. This laser driver had a very stable signal for these

laser diode types (amplitude fluctuations were below 1 %). To modulate the light at 70

MHz, a bias-tee (ZFBT-4R2G, Mini-Circuits) was used. A bias-tee takes a CW and RF

signal as input and gives a CW+RF signal as output (Fig. 3.3).

The frequency response of the bias-tee extends up to 4.2 GHz; therefore, laser diodes

may be modulated up to 4.2 GHz (in theory!). However, one should keep in mind that

modulation depth and modulation stability of laser diodes decrease quickly with respect

to modulation frequency.

38

Figure 3.3: CW laser diode driver combined with bias tee for RF modulation of laserdiodes. Ld1100 driver can apply up to 200 mA, depending on the laser diodes. Bias-teetakes this CW signal with RF signal from the generator and gives out CW+RF signal tothe laser diode.

BPF AMPAMP IQLPF

LPFAPD +PREAMP

5V15V

LO REF

I

Q

Q IDC DC

Figure 3.4: Detector module. The module consists of preamplified (PREAMP) avalanchephotodetector (APD), two 20 dB amplifiers (AMP), a 70 MHz band-pass filter (BPF), andIQ demodulator. The 70 MHz signal coming from the tissue is mixed with local reference(LO REF) signal in IQ demodulator and after low pass filtering (LPF) DC components ofI and Q components are extracted (IDC , QDC) to obtain amplitude and phase of diffusephoton density waves.

3.2.1.3 Detection Module

The RF instrument contains four detector blocks (channels). Each detector block consists

of an avalanche photo-diode (APD), two amplifiers, an I/Q demodulator, and filters (Fig.

3.4). The photon density wave signal was collected by a 3 mm fiber bundle and an APD

(5331-04, Hamamatsu), which has a 3 mm diameter active area, and a built in preamplifier.

The signal coming from the APD, is amplified (ZFL-500LN, Mini-Circuits), filtered by a

band-pass filter (SBP-70, Mini-Circuits), and then amplified again (ZFL-500HLN).

39

After amplifying and filtering, the signal from the APD is mixed with a 10 dBm RF

reference signal through the in-phase/quadrature (I/Q) demodulator, and converted into I

and Q components which carry the amplitude and phase information of the DPDW. The

AC signal is further filtered out by low-pass filters to get the DC component of the signal.

This extra filtering is needed to extract DC components of the signals, and then amplitude

and phase of the DPDW in tissue is obtained. The details of homodyne detection are

explained below.

I/Q Homodyne Detection

There are two kinds of phase detection methods: homodyne and heterodyne detection. A

homodyne system detects the phase shift at the radio frequency (RF), while the hetero-

dyne system downconverts the signal to a lower frequency for phase detection [31, 33].

Although heterodyne systems are more sensitive in phase detection, homodyne systems

are also attractive for their simplicity and low cost. Because a homodyne detection scheme

is used in our RF instrument, it is desirable to explain the details of homodyne detection,

which uses in-phase quadrature (IQ) demodulation [104]. The diagram of the IQ demod-

ulator is shown in Figure 3.5.

LPF

LPF

2Asin(ωt+θ)

RF Signal

Q(t)

Cos(ωt)

Sin(ωt)

I(t) IDC

QDC

Reference

sin(ωt)0

O

90O

Figure 3.5: I/Q homodyne detection demodulator. RF signal coming from tissue andreference signal has the same frequency (homodyne).

40

Suppose the detected RF signal is 2Asin(ωt + θ), where the A and θ are the ampli-

tude and phase of the DPDW, and suppose the reference signal is sin(ωt). As seen in

Figure 3.5, the detected signal is divided into two parts by an in-phase power splitter, and

reference signal sin(ωt) is split into two by the out-of-phase power splitter, generating

sin(ωt) and cos(ωt) components. The resulting RF input and reference signals are mixed

(multiplied) by a pair of double balanced mixers. The in-phase and in-quadrature outputs

are:

I(t) = 2A sin(ωt) sin(ωt) + I0 = A cos(θ)− A cos(2ωt + θ) + I0 (3.1)

Q(t) = 2A sin(ωt) cos(ωt) + Q0 = A sin(θ) + A sin(2ωt + θ) + Q0, (3.2)

where I0 and Q0 are the DC offset values (usually less than 1 mV) when there is no RF

signal input. After low pass filtering (LPF), time dependent terms drop out and only DC

terms survive. Thus amplitude A and phase θ of the DPDW can be obtained as:

A =√

(IDC − I0)2 + (QDC −Q0)2, (3.3)

θ = tan−1(QDC −Q0

IDC − I0

). (3.4)

3.2.1.4 Dynamic Range and Linearity Tests

The dynamic range of the RF instrument is assessed using the set-up illustrated in Figure

3.6. Laser light is divided into two components by a splitter (OZ Optics): one fiber has

10% of the light directed into multiply scattering media and the other 90% is connected to

an optical power meter. The power meter is relatively insensitive and sending 90% of the

light to the power meter allowed for a much higher dynamic range. Initially, the optical

attenuator is fully opened by turning the screw mechanically so that detector signal levels

are set to saturate optically. Then the intensity of laser light is reduced via the optical

41

Figure 3.6: The setup used for linearity and dynamic range tests. Attenuated laser lightis split into two, one going into multiply scattering Intralipid, and the other monitoredby a power meter. Light is collected by APD. Here, Sfiber = Source fiber, and Dfiber =Detector fiber.

attenuator in fixed steps. The fully closed attenuator gives the offset value of the signal.

The measured signal (offset subtracted) of the detection system is plotted with respect

to the input light signal measured by the optical power meter in Figure 3.7(a) and Figure

3.7(b), showing the deviation of the measured value from the fitted linear line. The voltage

range (amplitude in vertical axis) defines the dynamic range. Typically, we chose the range

as the deviation of the fit from linearity by ∼1%. This system has a ∼65 dB dynamic

range.

3.2.1.5 Validation In Vitro: Intralipid Titration Tests

In our laboratory we use liquid and solid tissue phantoms with well known optical prop-

erties for testing instruments and algorithms. Intralipid, normally used for intravenous

nutrition of patients who cannot digest regular food, is used as a liquid phantom. Its scat-

tering properties, come from fat (lipid) particles suspended in water and can be adjusted

to be very close to those of living tissue. For a stock suspension of 10% Intralipid (10%

42

-40 30 -20 -10 0 10-100

-80

-60

-40

-20

0

20

Power(dBm)

Am

p(dB

m)

datafit

~65 dB

-

(a)

51.5

1

0.5

0

0.5

1

1.5

Power(dBm)

Line

arity

Err

or (

%)

-30

(b)

Figure 3.7: Linearity test. (a) Horizontal axis shows input level of the signal level (readfrom power meter). Vertical axis shows the signal levels read through the turbid media(phantom). (b) Linear fit error. Dynamic range obtained by extracting the input powershaving fit error less than 1%.

of the content is fat), the scattering coefficient is given by [171]:

µs = 2.54× 109 × λ−2.5 (3.5)

g = 1.1− 0.58× 10−3λ (3.6)

µ′s = µs(1− g) (3.7)

where λ is in [nm] and µ′s is in [cm−1]. To mimic the absorption in tissue, I used black

India ink added into an Intralipid solution. Ink absorbance (A) is measured with a spec-

trometer (USB2000, Ocean Optics). As seen from Figure 3.8, absorbance of ink and µ′s

of Intralipid solution spectra decrease monotonically with respect to wavelength. After

measuring absorbance, µa can be obtained as µa(λ) = A(λ)ln(10). The factor ln(10)

originates from the literature where absorbance and absorption coefficient are defined as

A = log10Io

I, µa = ln Io

I, respectively. After obtaining absorption and scattering spectra,

one can prepare Intralipid phantoms with desired optical properties by diluting the ink and

43

Figure 3.8: Ink absorbance and Intralipid scattering coefficient spectra.

Intralipid in the container.

0.02 0.04 0.06 0.08 0.1 0.120.02

0.04

0.06

0.08

0.1

0.12

Expected µa [cm−1]

Ext

ract

ed µ

a [cm

−1 ]

ExtractedExpected

(a)

4 6 8 10 12 144

6

8

10

12

14

Expected µs′ [cm−1]

Ext

ract

ed µ

s′ [cm

−1 ] Extracted

Expected

(b)

Figure 3.9: Titration experiment results with RF instrument for (a) µa and (b) µ′s. Theabsorption coefficient was varied by adding known ink concentrations to the Intralipidsolution, while fixing µ′s = 8cm−1, and the scattering coefficient was varied by addingknown Intralipid concentrations to the Intralipid solution while fixing µa = 0.08cm−1.

We have tested the multi-wavelength and multi-distance fitting algorithm extensively

with Intralipid titration, and we have found good correlation between extracted and ex-

pected values of optical properties (Fig. 3.9). Figure 3.9(a) shows the titration experiment

results for absorption coefficient, µa obtained with the RF instrument. The absorption co-

efficient was varied by adding known ink concentrations to the Intralipid solution, while

44

fixing µ′s = 8cm−1. Both diffuse reflectance amplitude and phase data are fit. Points show

extracted µa values, the solid line shows expected values. Figure 3.9(b) shows the titration

experiment results for the scattering coefficient µ′s obtained with the RF instrument. The

scattering coefficient was varied by adding known Intralipid concentrations to the solution

while fixing µa = 0.08cm−1. Both diffuse reflectance amplitude and phase data are fitted.

Points show extracted µ′s values, the solid line shows expected values. The µa and µ′s

titration data show that extracted values are systematically underestimated. This may be

due to prepared titrated quantities are less than the correct values.

3.2.2 The Broadband CW Spectroscopy Instrument

Figure 3.10: Schematic diagram of the broadband CW spectroscopy instrument usedmainly for tissue oxygenation in preclinical applications. The instrument consists of awhite light source, dispersion system (monochromator), and a charged coupled device(CCD) camera.

Broadband CW reflectance spectroscopy is used mainly for small animal imaging to

quantify tissue blood oxygenation with a small tissue volume probe. The original design

principle of our in vivo spectrometer is due to Wilson [180] and Cope [39]; it consists of

a tungsten halogen lamp (Cuda Fiberoptics), dispersion system (monochromator, Acton

45

Reseach), and liquid nitrogen cooled charged coupled device (CCD, Roper Scientific)

camera [175, 176] (Fig. 3.10). Light is delivered to tissue with a single source fiber

and diffused light is collected by multiple detector fibers arranged in a linear array with

source-detector separations of ρ = 0.6, 1.2, 1.8, 2.4, 3, 4, 5, 6, 8 and 10 mm (Fig. 3.11(b)).

The detection fibers are coupled to the entrance slit of the monochromator where fiber

tips are arranged vertically with equal spacing between them. The detection fiber tips are

imaged through a grating onto the surface of a CCD detector. The image plane of the

monochromator output is projected onto the CCD array which then contains bright lines

representing signals from the individual fibers (Fig. 3.11(a)). The signal at the detector is

thus the spectrally-resolved diffuse reflectance data (Rtissue(λ, ρ), in Chapter 2) collected

at several distinct distances (ρ) from the source fiber.

ρρ

λλ(a)

0.6 mm

1.0 mm(b)

Figure 3.11: (a) Image on CCD camera detector. White stripes corresponds to detectorfibers. (b) Small probe for small animal imaging. The fibers are arranged in a line withsource-detector separations, ρ = 0.6, 1.2, 1.8, 2.4, 3, 4, 5, 6, 8 and 10 mm.

3.2.2.1 Validation In Vitro

Validation tests for the whitelight setup were performed with a small probe previously

used for preclinical applications (Fig. 3.10) [175, 176]. For a detailed discussion see a

recent publication [176]. Briefly, after adding fresh human blood into the 1% Intralipid,

46

StO2

StO

(%)

222S

tO(%

)2

Figure 3.12: StO2 and THC measured in hemoglobin phantoms using broadband re-flectance spectroscopy were plotted versus electrode probe measured oxygen partial pres-sure (pO2) and time respectively. Human blood dissociation curve (solid line) was plottedto compare and correct measured StO2, BV was measured at 50 and 100 µM. Courtesyof Wang [176].

the sample oxygenation was decreased by pumping nitrogen gas into the solution. The

oxygen partial pressure (pO2) is monitored simultaneously using a Clark-type electrode

probe. The measured blood oxygen saturation (StO2) is plotted versus pO2 in Figure

3.12(a). The oxygen dissociation curve (Hill’s curve) of human blood [77] is also plotted

in the same figure, showing a good correlation. Figure 3.12(b) shows extracted values of

two different concentrations of blood volumes, 45± 2 µM and 112± 4 µM, were close to

the prepared solutions of 50 and 100 µM respectively, and blood volume concentrations

were stable over time.

Intralipid Titration Test for Large Source-Detector Separations

I tested the system with a probe having large source detector separations for possible use in

the clinic. As discussed earlier, at large separations, sensitivity to µ′s variations decreases.

To check this, a larger probe with source-detector separations of ρ = 2.8, 3.1, 3.4, 3.7 and

47

4 cm (Fig. 3.11(b)) was arranged. The titration test fitting scheme is described previously

in Chapter 2.

650 700 750 8000

0.2

0.4

0.6

0.8

1

λ(nm)

log(

R(ρ

)/R

(ρ=

2.8

cm))

ρ = 2.8 cm

ρ = 4 cm

data

fit

(a)

2 4 6 82

4

6

8

Expected µs′ [cm−1]

Ext

ract

ed µ

s′ [cm

−1 ] Extracted

Expected

(b)

Figure 3.13: (a) Measured and fit of reflectance spectra. (b) µ′s titration test for largesource-detector separations. µ′s is increased by adding Intralipid while fixing absorptionµa = 0.021cm−1 coming from only water.

Expected µ′780nms (cm−1) 2 4 6 8

Extracted µ′780nms (cm−1) 2.56 ± 0.01 4.27 ± 0.02 5.96 ± 0.04 6.96 ± 0.04

Expected µ780nma (cm−1) 0.021 0.021 0.021 0.021

Extracted µ780nma (cm−1) 0.0229 ± 5e-4 0.022 ± 5e-4 0.0219 ± 5e-4 0.0216 ± 5e-4

Table 3.1: Titration test results for λ = 780 nm. µ′780nms is incremented by adding a

fixed amount of Intralipid while µa is fixed coming from only water absorption. Waterabsorption at 780 nm is obtained from the literature [139].

As can be seen from the Figure 3.13(b) and Table 3.1, µ′s fitting accuracy is not as good

as that of the RF instrument, showing that there is an underestimation with increasing µ′s.

One reason is that all the separations I used were large, and sensitivity is expected to be

lower in this range. However, fitting accuracy could be greatly improved if CW whitelight

48

spectroscopy data had specific spectral shape signatures (like sharp peaks). It is clear from

Figure 3.8 that both Intralipid and ink spectra do not have this spectral property since they

both have monotonically decreasing features. In fact, apart from a scale factor they are al-

most indistinguishable. Therefore, if these experiments were repeated with specific dyes

which had specific absorption peaks, then the results would improve. To eliminate dy-

namic range constraints while using multi detector fibers, optical attenuators (OZ Optics,

Canada) were added to the detector fibers closer to the source fiber. However, the probe

itself became very rigid and bulky and therefore impractical in clinical measurements, es-

pecially on rigid head and neck tumors (see Chapter 5). This problem can be resolved

by using only one or two collection fibers concurrently with the frequency domain instru-

ment as successfully applied by Bevilacqua et al. [13, 83]. One of the main advantages

of whitelight system, apart from using information from many wavelengths, would be the

possibility of extending the available wavelengths to above 900 nm where water absorption

exhibits a large contrast in tumors [26, 82]. Since above 900 nm commercially available

laser diodes are very limited and rather expensive, a whitelight setup utilizing a broad band

spectrum is compact and economical. However, our current system is constructed and op-

timized for photodynamic therapy where absorption peaks of photosensitizers are around

∼630 nm; above 800 nm the signal intensity decreases quickly. Furthermore, signal acqui-

sition is not parallel with frequency domain and blood flow setups, which imposes extra

time constraints during clinical measurement. For these reasons, I used this system mainly

for the preclinical applications described in detail in Chapter 4.

3.3 The Diffuse Correlation Spectroscopy Instrument

A basic blood flow instrument has a long coherence length laser (Crysta Laser, Nevada)

operating at 785 nm, a photon-counting detector (avalanche photodiode, Perkin-Elmer,

49

Figure 3.14: A basic, one channel blood flow instrument. The instrument consists of along coherent laser, a photon counting detector, and an autocorrelator board.

Canada), and a custom built autocorrelator board (Correlator.com, New Jersey) (Fig. 3.14).

The source light is delivered to the tissue by a multi-mode source fiber. A single-mode de-

tector fiber is used to collect the light. Photodetector outputs are fed into a correlator board

and resulting intensity autocorrelation functions and photon arrival times are recorded by

a computer. From the normalized intensity autocorrelation function, the diffuse electric

field temporal autocorrelation function is extracted (Chapter 2).

3.3.1 Validation In Vivo: Arm Cuff Ischemia

An arm cuff ischemia experiment was done to monitor blood flow changes in vivo. During

arm cuff ischemia, pressure was applied to occlude blood flow (Fig. 3.15(a)). As Figure

3.15(b) shows, during the baseline measurements, blood flow (rBF ) was constant. Here,

rBF is defined as percent changes in blood flow relative to the baseline value. After ap-

plying pressure, the blood flow dropped sharply as expected. Releasing pressure resulted

in a hyperemic overshoot, and then blood flow returned to the baseline value.

50

(a)

0 50 100 150 2000

100

200

300

400

500

Time (s)

rB

F (

%)

(b)

Figure 3.15: Arm cuff ischemia experiment. (a) Pressure cuff is applied to occlude bloodflow in an arm. (b) Relative blood flow (rBF ) changes during cuff ischemia. After pres-sure is applied, blood flow drops sharply, and after release, it overshoots and then goesback to baseline values.

3.4 Optical Probe Design

Probe design is very important in diffuse optical spectroscopy. Each probe must be de-

signed for its intended application. One should arrange source-detector separations ac-

cording to the depth of tumors. If a tumor is deep, large source-detector separations need

to be used. On the other hand, if a tumor is on the superficial layer, then a small probe

with small source-detector separations is desirable. The golden rule is that source-detector

separations need to be two/three times bigger than the depth of the tumor being investi-

gated [58, 129]. Figure 3.16 shows the case of a relatively large probe used in clinical

measurements of head and neck tumor patients where deep photon penetration is required.

Fibers were arranged in such a way that the tissue was imaged with many source detector

separations (Fig. 3.16). Generally, the probe consists of a simple black pad and fibers

placed on it. The pad can be constructed of a plastic or rubber material according to de-

sired flexibility. Black color eliminates background light leakages. The size of the probe

51

3 cm

1.8 cm

D1D2

S1

S2

S3

S4

Figure 3.16: Optical probe used in clinical setup: Two detector fiber bundles and 4 sourcefibers are arranged with the shortest separation at 1.8 cm, and the longest separation at 3cm. S1, S2, S3, S4 are source fibers and D1 and D2 are detector fibers.

is approximately 5-6 cm but can be changed easily according to tumor size and depth.

52

Chapter 4

Preclinical Applications

In this section I present work related to small animals. The work demonstrates our tech-

niques in a controlled environment, supplementing our clinical techniques, and in some

cases providing insight about related biological mechanisms. First, the results from a

study of the vascular targeting drug, Combretastatin A4-phosphate (CA4P), are presented.

In Section two, we investigated the chemotherapy drug, Onconase (Onc), an enhancer of

Cisplatin, a clinical chemotherapeutic agent.

4.1 Non-invasive, Continuous Monitoring of Antivascu-

lar Tumor Therapy

Currently, there is a great deal of research on vascular targeting and anti-angiogenic agents

that modulate the response and sensitivity of tumors to chemotherapy and radiation ther-

apy. Noninvasive repetitive measurements of blood flow and oxygen saturation are there-

fore of potential value for clinical evaluation of these drugs. In this section I present a

pilot study on the vascular targeting drug Combretastatin A4-phosphate (CA4P) in K1735

malignant melanoma tumor models. The results demonstrate that blood flow and oxygen

53

saturation decrease significantly after the drug injection. The results also correlate well

with contrast enhanced ultrasound, tumor histology, and nitroimidazole (EF5) binding.

We conclude that noninvasive diffuse optical measurements can quantify acute effects of

CA4P on tumor blood flow, blood oxygen saturation and blood volume changes.

4.1.1 Introduction

Combretastatin A4-phosphate (CA4P) is a vascular targeting agent which modifies blood

flow and tissue oxygenation in the tumor. The drug targets tumor blood vessels by disrupt-

ing the capillaries that feed tumors and thereby inducing a shut-down of the tumor blood

flow [126, 168]. After intravenous or intraperitoneal infusion, the drug rapidly spreads

throughout the bloodstream. It is then converted into active Combretastatin, which enters

the endothelial cells lining the blood vessels. In tumors, these cells are immature and are

particularly sensitive to Combretastatin’s effects, compared to the endothelial cells in nor-

mal tissue. Recent MRI studies showed that tumor endothelial cells are more permeable to

Combretastatin compared to normal tissue endothelial cells [9, 167, 168]. Once inside the

endothelial cells Combretastatin destroys the cytoskeleton, changing cell shape from flat

to round and effectively clogging the capillaries that feed the tumors. Phase I clinical trials

of patients bearing a variety of solid tumors exhibit statistically significant reductions in

blood flow within four to six hours after the infusion [166].

CA4P’s working mechanism is different from anti-angiogenic drugs; CA4P causes the

vascular structure inside a solid tumor to collapse, cutting the blood supply the tumor

needs to survive. Many anti-angiogenesis drugs, by contrast, keep new blood vessels from

forming and do not act on blood vessels that feed existing tumors. CA4P has acute effects

on tumor vasculature; ten minutes after CA4P injection the vessels start shrinking, and

blood flow decreases significantly. One to two hours after injection some vessels disappear

completely [126, 166]. Our results show that CA4P induces rapid shutdown of the blood

54

flow and hypoxia.

4.1.2 Materials and Methods

4.1.2.1 Animal and Tumor Models

All animal experiments were approved by the University of Pennsylvania Animal Care

and Use Committee. The K1735 melanoma tumor cell lines were cultured and injected

subcutaneously into anesthetized mice. During the measurements mice were anesthetized

with 1.25 % isoflurane and air mixture for immobilization and the hair underlying the

tumor was removed with a depilatory salve.

4.1.2.2 Contrast-enhanced Ultrasound Imaging

Power Doppler ultrasound imaging of tumor perfusion was done with a Philips ATL 5000

ultrasound scanner (Philips ATL, Bothell, WA) [187, 191]. The imaging transducer was

aligned with the long axis of the tumor. Acoustic gel was used to better contact between

the transducer face and tumor. Initial scanning of each tumor was performed in grayscale

ultrasound mode to define the boundary of the tumor mass. One hundred microliters of

micro-bubble ultrasound contrast agent (Optison, Amersham, Princeton, NJ) were then

injected via tail vein catheter. The area of contrast enhancement denotes perfused regions

in the tumor. Tissue regions with blood flow are coded in color. The brightness of the

color represents the strength of the Doppler signal and is related to the concentration of the

moving red blood cells. The color level is expressed in arbitrary units from 0 to 100, where

the values 0 and 100 represent no power Doppler signal and maximum power Doppler

signal, respectively [145, 146]. Images were recorded on videotape (S-VHS format) and

digitized frame by frame at 24-bit resolution using a Macintosh AV-7600 frame grabber.

55

4.1.2.3 Tumor Histology and Immunohistochemistry

Tumor histology analysis of tumor sections were performed using Hematoxylin and Eosin

(H&E) staining. Hematoxylin is a salt that dissociates in water into positive and neg-

ative ions [140]. Its positive ion readily combines with negatively charged regions of

cellular macromolecules, especially nucleic acids, coloring them ranging from dark blue

to black. Eosin is also a salt that dissociates in water into ions. Its negative ion readily

combines with positively charged regions of cellular macromolecules, especially cytoplas-

mic proteins, coloring them ranging from pink to orange. With H&E staining, therefore,

the nuclei of the tumor cells stain dark bluish and the cytoplasmic portions of the tumor

cells stain pinkish. All histological sections specimens were viewed under a Nikon E600

Eclipse (Nikon, Melville, NY) equipped with a krypton-argon laser and optical filters for

visualization of FITC (fluorescein isothiocyanate), Texas-red and cyanine-3 fluorescence.

Images were acquired by a charged-coupled device camera (Roper Scientific, Trenton,

NJ). Nitroimidazole (EF5) is provided by Dr. Cameron Koch, University of Pennsylvania,

and staining was performed to determine tumor cell hypoxia. Immunofluorescent analysis

of EF5 in tumor sections were performed as previously described [55], using a cyanine

3-conjugated anti-EF5 monoclonal antibody (provided by Dr. Cameron Koch). In case of

intracellular hypoxia, EF5 binding occurs and hypoxic tumor cells stain reddish.

4.1.3 Results and Discussion

4.1.3.1 Combretastatin Induces Significant Blood Flow Reduction

A representative example of DCS blood flow kinetics is shown in Figure 4.1A. The data

represent tumor average responses before drug injection (baseline), and up to 1 hour after

drug injection. It is clearly seen that following an initial quick (spike-like) increase, blood

flow decreases substantially after the drug injection. After 1 hour an ∼60% decrease

56

0 20 40 600

50

100

150

200

Time [min]

rBF

(%)

injection

Pre-drug 1 hour 0

20

40

60

80

100

120

rBF

(%

)

A B

C D

12 mm

Figure 4.1: DCS recordings show the acute effects of the drug (A). Mean percent change(±SD) in relative flow for N = 9 mice (B). Effects of antivascular drug on tumor vas-culature, imaged with micro-bubble contrast enhanced power Doppler ultrasound. In ul-trasound images, yellow regions occur due to contrast enhancement in perfused bloodvessels. Pretreatment tumor is uniformly perfused (C); post treatment vasculature is de-stroyed, and blood perfusion is reduced (D).

in blood flow is observed; the trend continues with time beyond one hour. Figure 4.1B

summarizes the average response of 9 mouse measurements, pre-drug (baseline) and 1

hour after drug injection. The average decrease in blood flow after 1 hour is about 64%

(p = 0.0009). Power Doppler ultrasound images of tumor (Fig. 4.1C, D) show the effects

of CA4P on vasculature. Yellow pixels denote perfused blood vessels of the tumor. It is

clear that K1735 tumors were uniformly perfused with no evidence of avascular regions,

suggesting that blood vessel growth kept up with tumor growth [191]. After injection of

the drug, much of the vasculature is destroyed and blood perfusion is reduced (Fig. 4.1D).

57

B

1 mm

A C

Control 5 hr post 5 hr post (Magnified)

Vessel

Occlusion

0.1 mm

Figure 4.2: Histology showing untreated (control) and CA4P-treated tumor sections. (A)Untreated tumor, dark blue spots represent tumor cell nuclei. (B) 5 hours post-treatment,red spots represent red blood cells. (C) 10 times magnified version of (B). It is clearlyseen that after treatment, vessel occlusion occurs, blood vessels become congested andcells coagulated, forming “blood cell lakes” get forms (B and C). Data courtesy of Dr. W.Lee.

Histological examination of tumor sections shows the effects of blood vessel conges-

tion and collapse after 5 hours as the formation of plugged packed red blood cells, or

“blood cell lakes” (Fig. 4.2). The biological reason for an initial increase in tumor blood

flow during first 10 minutes is not well understood. This effect was very consistent across

all mice. Perhaps this early increase is due to heart rate increase induced by the stress

during injection.

Blood flow reduction due to CA4P has also been observed by other modalities. An

LDF study showed a 73% decrease in tumor perfusion after 1 hour in a CH3 mouse

mammary carcinoma model [91]. DCE-MRI [10] and Doppler ultrasound [67] showed

a significant decrease in blood flow in preclinical studies. Moreover, Phase I/II clinical

trials of CA4P showed a significant tumor anti-vascularity after the injection of tolerable

doses. DCE-MRI detected reduced flow rate in recent clinical trials [64,109,166] and PET

observed a significant reduction in absolute blood flow [1].

58

4.1.3.2 Combretastatin Induces Significant Blood Oxygen Saturation Reduction

Pre-drug 1 hour 0

20

40

60

StO

2 (%)

A CB

1 mm

Figure 4.3: Mean percent change (±SD) in for N = 5 mice StO2 (A). EF5 immunoflu-orescence shows no binding for the control mice (B), but binding (shown in red) in thetreated tumors showing hypoxia is induced (C). Data courtesy of Dr. W. Lee.

Changes in blood flow (rBF ) were accompanied by changes in blood oxygen satura-

tion (StO2). Our data from 5 mice shows that mean blood oxygen saturation decreased

significantly (p = 0.002) after one hour (Fig. 4.3A) from 42±11% to 14±8%. Uptake of

the hypoxia marker EF5 (i.e., EF5 Binding [55]) showed no binding for the control mice

(Fig. 4.3B), but substantial binding (shown in red) in treated tumors, again suggesting

that hypoxia is induced by the drug (Fig. 4.3C). These data demonstrate that, in this tu-

mor model, there is good correlation between intravascular oxygen status and intracellular

oxygenation. It is also seen from Fig. 4.4 that total hemoglobin concentration (THC)

decreased, but this decrease was not significant (p = 0.06). Kragh et al. [91] noted that

CA4P caused no change in tumor blood volume, and suggested that blood was trapped in

the tumor by the vascular shut down.

59

Pre-drug 1 hour 0

50

100

150

200

TH

C (

µM

)

Figure 4.4: Blood volume before and after CA4P injection.

4.1.4 Conclusion

We have shown that tumor vasculature response to an antivascular drug, CA4P, can be

assessed by using noninvasive diffuse optical spectroscopies in a preclinical model. Com-

bined blood flow and blood oxygen saturation information may be valuable for under-

standing and assessing the working mechanism of antivascular drugs in both pre-clinical

studies and clinical trials. Eventually, this method holds potential to provide informa-

tion about drug efficacy and the prognostic value of concurrent therapies such as radiation

therapy [115, 116].

60

4.2 Monitoring a New Chemotherapy Drug (Onconase)

We show that the chemotherapy drug Onconase (Onc) induces a significant increase in

blood flow and oxygen saturation, and significant tumor growth inhibition in A549 hu-

man non-small cell lung carcinoma (NSCLC). We also investigated whether Onc-induced

increased rBF could lead to removal of tumor acidic metabolites such as lactate. Further-

more, stimulated by prior observations of reduced oxygen consumption by treatment with

Onc, we investigated whether ATP levels decreased in tumors. The results suggest that

Onc may be a promising drug for the treatment of NSCLC patients.

4.2.1 Introduction

The novel cytotoxic chemotherapy drug Onconase (Onc), isolated from the eggs and early

embryos of the leopard frog Rana pipiens, is known to significantly improve the blood

flow, oxygenation and decrease interstitial fluid pressure (IFP) of murine adenocarcinoma

(MCaIV) tumors [95]. An increase of blood flow in chemotherapy is desirable since

chemotherapy utilizes blood flow to deliver toxic agents and oxygen to tumor cells. Oxy-

genated tumor cells respond better to radiation therapy. Interestingly, it has been recently

reported that the cytotoxic Onc produces an enhanced radiation response in A549 human

non-small cell lung carcinoma (NSCLC) in vitro and in vivo [105, 157]. This effect is

likely as a result of improved tumor oxygenation and blood flow [89, 93–95]. In solid

tumors IFP is generated by growing tumor cells in a confined space, which in turn induces

compression of blood vessels and ultimately inefficient blood flow delivery [70]. There-

fore, IFP is known to be one of the physiological barriers for cytotoxic drug delivery in

chemotherapy [79]. Tumor cell death decompresses blood vessels allowing better blood

flow [70]. In cell culture studies, it is shown that apoptosis (programmed cell death) was

induced by Onc [89, 93, 95], which possibly could lead to decreasing IFP.

61

Dynamic contrast-enhanced MRI (DCE-MRI) has been used to demonstrate that Onc

transiently increased tumor perfusion in A549 tumors [89]. This led to consideration of

the therapeutic effectiveness of Onc + Cisplatin on A549 tumors. Testing this combina-

tion is important since Cisplatin is a chemotherapy agent currently being used in head and

neck cancer patients during their radiation therapy treatment at the Hospital of the Uni-

versity of Pennsylvania and at other institutions. A growth delay assay showed changes

in tumor volume after treatment with Onc + Cisplatin in A549 tumor xenografts of nude

mice [105]. Onc also proved to be a radiation enhancer, therefore it may be used concur-

rently in chemo-radiation treatments in clinical settings.

To confirm the increased tumor perfusion observed by DCE-MRI, rBF was non-

invasively monitored with diffuse correlation spectroscopy (DCS). Diffuse reflectance

spectroscopy (DRS) concurrently quantified tumor blood oxygen saturation (StO2) changes.

It was found that Onc induced a significant blood flow increase, confirming DCE-MRI re-

sults. Moreover, blood oxygenation also increased significantly, possibly due to increased

rBF and decreased MRO2. We also tested whether Onc-induced blood flow could tran-

siently remove acidic tumor metabolites such as lactate in tumors, and we tested whether

ATP levels decreased in tumors, as suggested by on prior observations of reduced oxygen

consumption (MRO2) by treatment with Onc.

4.2.2 Materials and Methods

4.2.2.1 Animal and Tumor Models

Institutional guidelines for the care and use of laboratory animals were followed. Eight-to-

ten-week-old, female athymic NCR-nu/nu nude mice (purchased from the NCI, Bethesda,

MD) bearing human tumor xenografts of A549 human NSCLC cells were utilized. 2 x 106

viable cells were injected subcutaneously into the right thighs of mice. Experiments were

62

carried out when the tumor volume was between 200 and 400 mm3 (tumor volume = length

× width2 × π/6) [187]. For the in vivo Onc treatment, Onc was dissolved in sterile 0.9%

NaCl (saline) solution before the experiments. The mice were given an intraperitoneal

(i.p.) injection of Onc at 2.5 mg/kg at a volume of 0.2 ml/ 20 g of body weight. During

the physiological measurements animal body temperature was maintained constant by a

heating pad.

4.2.2.2 Magnetic Resonance Spectroscopy

Lactate level in the tumor tissue was measured by Magnetic Resonance Spectroscopy

(MRS) 0-2 hours post-treatment with Onc using a 9.4 T 30 cm vertical bore spectrome-

ter equipped with 55 mm, 55 G/cm gradients, and a slotted tube resonator. Onc-induced

changes in ATP levels were monitored by non-localized Phosphorus (31P) MRS spec-

troscopy. The animal body temperature was maintained at 37 oC during the MRS experi-

ment by blowing warm air through the magnet bore.

Lactate is a measure of acidic metabolites in tumor cells. Because metabolism is higher

in tumor cells, tumor acidic level (Lactate level) is generally higher compared to normal

tissue. Phosphorus is used to create cell membranes and the energy currency of the cell,

adenosine triphosphate (ATP). In tumors, high levels of ATP tell us that tumor cells are

rapidly dividing, and that ATP usage and tumor metabolism are high.

4.2.3 Results and Discussion

4.2.3.1 Onconase Enhances Radiation Response

As previously noted, radiation therapy is often utilized concurrently with chemotherapy.

Therefore it is valuable to investigate whether Onc is an enhancer of radiation. As seen

in Figure 4.5, Onc-2.5 (2.5 mg/kg) slightly retarded the A549 tumor growth by 2 days.

63

Figure 4.5: Growth delay assay after treatment with Onc and X-radiation at 5 Gy (N= 5mice per group). Data courtesy of Dr. I Lee [157].

However, Onc-2.5 significantly improved radiation response in nude mice bearing A549

tumors. X-radiation (X-rays) at 5 Gy with an i.p. injection of saline (control) significantly

retarded tumor growth compared to the untreated control during the 50 day observation

period. When Onc was administered i.p. 2 hr prior to X-radiation, the tumor growth delay

was >20 days compared to the radiation alone group. This is more than a simple additive

effect. Therefore, Onc proves to be a radiation enhancer and chemotherapy patients treated

with radiation concurrently (such as NSCLC patients) may benefit from Onc.

4.2.3.2 Onconase Induces Significant Blood Flow and Oxygen Saturation Increase

Our results also show that Onc induced a significant blood flow increase in A549 tumors.

Previously, an ∼25% increase in perfusion of A549 tumors was observed using a DCE-

MRI method at 90 min post-treatment of Onc [89]. Figure 4.6A shows the average diffuse

optical measurements of all 8 mice at time points of 0 min (baseline), 60 min, and 90 min

64

Blood flow in A549 tumors

0

20

40

60

80

100

120

140

160

180

0 min 60 min 90 min

Minutes after treatment with Onc

rB

F(%

)tumor

muscle

Blood oxygen saturation in A549 tumors

0

10

20

30

40

50

60

0 min 60 min 90 min

Minutes after treatment with Onc

tumor

muscleA B

StO

2 (

%)

Figure 4.6: Mean (±SD)relative flow change for N = 8 mice (A) (100% implying nochange, the baseline value). Mean percentage change (±SD) in blood oxygen saturationfor N = 5 mice (B).

after drug injection. The average increase in blood flow after 60 min is∼40% (p = 0.008)

and after 90 min it is ∼70% (p = 0.004). In contrast, blood flow changes in skeletal

muscle after 60 min (p = 0.16) and 90 min (p = 0.14) were not significant, suggesting

side effects were minimal.

Figure 4.6B shows that Onc also induced a significant blood oxygen saturation increase

in A549 tumors, suggesting changes in blood flow (rBF ) are accompanied by changes in

blood oxygen saturation (StO2). Our data from 5 mice show that mean blood oxygen

saturation increased significantly (p = 0.006) (Fig. 4.6B) from 14 ± 4% to 32 ± 3% in

60 min and that at 90 min there is still a slight increase, 27 ± 4% (p = 0.018) relative

to baseline. The changes in skeletal muscle after 60 min (p = 0.09), and after 90 min

(p = 0.07) were not significant. A histogram of the StO2 distribution from all mice

clearly shows that median and mean StO2 at 0 minutes were very low, confirming the

tumor was hypoxic; further, administration of Onc significantly increased median and

mean values of intratumoral StO2 (Fig. 4.7). It is well known that the A549 tumor type

is very hypoxic [95]. High StO2 values compared to pretreatment may be due to both

65

0 10 20 30 40 500

5

10

15

20

25

StO2(%)

Fre

quen

cy

beforeafter

Figure 4.7: Histogram of StO2 distribution from all mice (N=6, 15 recordings for eachmice) before and after a single injection of Onc (5 mg/kg). Median and mean StO2 be-fore administration were very low, confirming the tumor was hypoxic. Onc significantlyincreased median, and mean values of intratumoral StO2.

increased rBF and reduced MRO2. Interestingly, ATP levels decreased slightly in tumors

(Fig. 4.8), and an ∼20% decrease of lactate level was observed using non-localized MR

spectroscopy (N=3) caused by the removal of tumor acidic metabolites.

4.2.4 Conclusion

Previous preclinical studies showed that the high tumor interstitial fluid pressure (IFP) of

A549 tumors was significantly reduced by Onc [94,95]. The reduced IFP, in part, might be

expected to improve the penetration of Onc into the tumor regions by overcoming physio-

logical barriers such as resistance to blood flow [79]. It has recently been observed using

a non-invasive DCE-MRI technology that permeability at the rim of the tumor was tem-

porarily increased at 1.5 h post-injection of Onc [89]. This is in agreement with previous

observations using a laser Doppler flowmetry [94, 95].

We have shown with DCS/DRS that administration of Onc increased both tumor blood

66

nc

Figure 4.8: Time dependence of lactate and ATP levels after i.p. administration of 10mg/kg of Onc.

flow and oxygenation in a preclinical model. Onc may be a potential candidate for a

radiation enhancing chemotherapeutic agent and may improve the treatment of NSCLC in

clinical settings. Diffuse optical spectroscopies thus show promise for monitoring acute

effects of tumor blood flow and oxygenation during therapy.

67

Chapter 5

Clinical Applications

In this chapter I briefly introduce head and neck tumors and radiation therapy basics. Then

I show measured clinical responses of head and neck tumors to radiation therapy. Lastly,

new optical instrumentation is proposed for primary head and neck tumor detection and

monitoring during therapy.

5.1 Head and Neck Tumors

Head and neck cancer refers to malignancies arising from the mucosal surfaces of the oral

cavity, pharynx, nasal cavity and sinuses (Fig. 5.1). The term malignant defines those

tumors having the ability to metastasize or spread to other parts of the body. Tumors from

other parts of the body can spread to the head and neck region as well. The most common

type of malignant tumor in the head and neck region is squamous cell cancer, also known

as squamous cell carcinoma. The lining of much of the mouth, nose and throat is made

up of a type of cell known as the squamous cell. When a malignancy arises in these cells,

the tumor is called squamous cell carcinoma (SCC). This tumor is most often associated

with heavy smoking and/or heavy consumption of alcohol. It can also occur in people who

68

have never smoked or have consumed alcohol only lightly, but this is less common.

Figure 5.1: Head and neck nodes [66].

Most of my Ph.D. work has been concerned with monitoring radiation therapy by non-

invasive diffuse optical methods. Before showing the response to the therapy, I describe

the basics of the radiation therapy. Later, I will use these concepts to interpret my results.

I will first describe the basic mechanism of radiation therapy. Then I will describe the

clinical aspects of radiation therapy. Most of the explanations developed in this chapter

are based on reference [159]. These are very important concepts in understanding the

response of the tumor to a given therapy, because the treatment method (e.g., radiation

therapy) also determines therapy response. For example, from a number of preclinical

and clinical studies, it appeared that a single large dose of radiation would destroy the

microvasculature and may lead to normal tissue cell death, while fractionated radiation in

a clinically relevant doses could temporarily improve tumor oxygenation, thus facilitating

improved radiation treatment [5, 21, 172, 192].

69

5.2 Molecular and Cellular Basis of Radiation Therapy

X-rays are used widely in medicine for cancer treatment. X-rays or gamma-rays are com-

posed of high energy photons whose interaction with matter often causes an electron to

be knocked from its outer orbital leaving a positive charge (ion). This is the origin of the

term ionizing radiation. Typical binding energies of the electrons are on the order of 10

eV (electron volts). Thus, photons having energies greater than 10 eV are considered to

be ionizing radiation, while photons with energies of 2 to 10 eV in the visible-UV range

are non-ionizing.

5.2.1 Physical Interactions

When X-rays interact with the tissue, they give up their energy by one of the three pro-

cesses: the photoelectric effect, the Compton Effect, or pair production. In the energy

range most likely used in radiotherapy (100 eV to 20 MeV), the Compton effect is the

most important mechanism for deposition of energy into biological tissues.

In a Compton scattering interaction, incident photons scatter off an outer orbital elec-

tron and the energy of the scattered photon is reduced. The electron leaves the atom

carrying the energy difference of incident and scattered photons since it was only weakly

bound in the first place. In the photoelectric effect, the incident photon interacts with an

inner bound orbital electron, the incident photon is completely absorbed by the electron

(no scattering), and the electron leaves the atom carrying off the difference of the energy

of the photon and the binding energy of the atom. In pair production, the incoming photon

is completely absorbed in an interaction with the nucleus, and an electron-positron pair

is produced. The positron causes ionization similar to an electron until it comes to rest

and annihilates producing a 511 keV photon pair. A typical depth-dose curve for clinical

photon and electron beam radiation is illustrated in Figure 5.2(a) [159] and shows that the

70

photon beam penetrates much better into the living tissues.

1.0

0.8

0.6

0.4

0.2

05 10 15

Penetration depth (cm)

Re

lative e

nerg

y a

bsorb

ed

photon

electron

(a)

1.0

0.8

0.6

0.4

0.2

05 10 15R

ela

tive e

nerg

y a

bsorb

ed

Penetration depth (cm)

neutron

(b)

Figure 5.2: (a) Photon and electron depth-dose curves. Photons penetrate deeper thanelectrons. (b) Neuron depth-dose curve. The vertical scale relates to the three types ofradiation independently and does not provide an inter-comparison (Adapted from [159]).

All three of the interactions induce energetic electrons, which in turn excite or ionize

target atoms and molecules and set more electrons in motion in the tissue. It should be

noted that because neutrons do not have charges, they do not interact with electrons in the

atom, but they deposit energy by collision with nuclei (protons), thereby transferring their

energy to create moving charged particles capable of both ionization and excitation (Fig.

5.2(b)).

Radiation doses are measured in terms of the amount of energy deposited in tissue

(joules) per unit mass of tissue (kg), with units of Gray (Gy), 1 Gy = 1 J/kg. It will be seen

that the delivery mechanism of the dose (e.g., fractionation) and the total radiation dose

are important factors that determine the response of living tissue.

71

5.2.2 Biological Effects of Radiation

5.2.2.1 Free Radicals

The biological effects of ionizing radiation are primarily the result of damage to DNA of

target cells. This can occur via either the direct or indirect action of radiation. Direct

action occurs when any form of radiation interacts directly with the DNA. Atoms within

the DNA may be ionized, initiating a biological effect due to the damage of the DNA.

Indirect action of radiation occurs when ionizing radiation interacts with other atoms or

molecules in the cell, especially water, to produce free radicals that are able to damage the

cell’s DNA. A free radical is a molecule that has an unpaired orbital electron in the outer

shell. For the important molecule of water, the interaction of a photon or charged particle

with the water molecule removes an outer shell electron and creates an ion radical. This

reaction is expressed as,

H2Oradiation−−−−−→ H2O

+ + e−. (5.1)

The H2O+ exists either as an ion radical or a free radical. The free radical has an unpaired

electron in the outer shell and is highly reactive. The ion radical form will react with

another water molecule (probability of interaction with water is high since cells are 80%

water) to form a reactive hydroxyl radical (OH), which in turn interacts with the cells’

DNA, breaks chemical bonds, initiates chemical changes, and initiates a chain of events

that result in biological damage.

5.2.2.2 Oxygen Effect in Radiation Therapy

The biological effects of radiation are influenced by oxygen. It is often the case that when

oxygen is present during the interaction, it reacts with free radicals to produce a non-

restorable change in the chemical composition of the material exposed to radiation. If

oxygen were not present, some of these reactions would not take place, and many of the

72

ionized molecules in the cell could be repaired, eventually allowing the cell to function

normally. Oxygen may be envisioned to induce unrepairable damage. Damage is said

to be “fixed”. This concept is known as the Oxygen Fixation Hypothesis. In order to

quantify this effect for different types of radiation, survival curves of mammalian cells

under different exposures have been constructed. The ratio of hypoxic cell doses to the

aerated (oxygenated) cell doses required to achieve the same biological effect is called the

Oxygen Enhancement Ratio (OER).

0 10 20 3010

3

10 2

10 1

100

Dose (Gy)

Su

rviv

ing

Fra

cti

on

Air (oxic)Nitrogen (anoxic)

(a)

1

2

3

Rela

tive R

adio

sensitiv

ity

Oxygen concentration (µΜ)100021055

40 mmHg Air

100%

O2

(b)

Figure 5.3: Effect of oxygen as a radiosensitizer. (a) Survival curves of tumor cells withlow doses of radiation in the presence (Air) and absence (Nitrogen) of oxygen. (b) Therelative radiosensitivity of tumor cells as a function of oxygen concentration. Adaptedfrom ref [159].

Data obtained over the years has shown that for X-rays and gamma rays, the OER at

high doses (5-30 Gy) generally has a value of 2.5 to 3, (i.e. 3 times as many cells die from

the same amount of radiation when oxygen is present at the time of exposure) (Fig. 5.3(a)).

For lower doses, at about the level of daily dose given during a fraction of radiotherapy

dose (1-2 Gy), this OER has a slightly smaller value of 2. Reduction of OER at low doses

of radiation may be important clinically, since clinical doses of radiation are often of the

order of 2 Gy or less. As shown in Figure 5.3(b), the radiosensitivity of tumor cells also

73

depends on oxygen concentrations. At very low oxygen levels, the cells are resistant, but

as oxygen level increases their sensitivity increases quickly.

5.2.3 Tumor Hypoxia and Therapy

Experimental and clinical studies have established the presence of hypoxic cells in tumors.

As the tumor size increases, the blood supply to the internal core is often cut off. When

this occurs, cells at the tumor center become hypoxic. As the tumor grows, the number of

hypoxic, and thus radiation resistant, cells increases. The cells of most importance are the

cells hypoxic enough to be resistant to ionizing radiation therapy, yet containing enough

oxygen to be viable and thus continue to grow after treatment.

The pioneering work of Gray et al. [69] showed that the sensitivity to radiation dam-

age depends on the oxygen level in the cells. It has been shown that well-oxygenated

tumors are more sensitive to the killing effects of ionizing radiation than hypoxic tumors.

This is because oxygen molecules react rapidly with the free-radical damage produced by

ionizing radiation in DNA, causing permanent DNA damage and cell death. A number of

studies using oxygen electrodes have demonstrated that hypoxic tumors respond poorly to

radiotherapy and result in much lower survival rates [3]. Hypoxic tumors may also be re-

sistant to chemotherapy because hypoxic cells in the tumor are far from functioning blood

vessels, because the majority of anticancer drugs are only effective against rapidly prolif-

erating cells, and because chemotherapy drugs have to reach tumor cells from the blood

vessels (Fig. 5.4a). Figure 5.4b shows that the efficacy of radiation and chemotherapy

decreases as a function of distance from the vasculature [23].

74

Tumor cell proliferation

O2 O2 O2

Drug concentration

Capillary

Oxygenated tumor cells

Hypoxic tumor cells Dead cells

(a)

0 50 100 150

0.1

1.0

Surv

ivin

g fra

ction

of tu

mor

cells

Distance from capillary (µm)

(b)

Figure 5.4: Part of the tumor surrounding capillary. (a) As oxygen concentration decreaseswith increasing distance from the capillary, cell proliferation and drug concentration de-crease (Smaller sized arrows indicate smaller O2 and drug concentrations). (b) The levelof cell kill in response to radiation, and to many other anticancer drugs, decreases withincreasing distance from the capillary [23].

5.3 Clinical Radiation Therapy

To truly understand the clinical responses of tumors to radiation therapy there is a need for

a transition from experiments with individual cells to tissue level experiments. This in turn

will permit application of clinical knowledge to cancer treatment to monitor therapies.

In clinical radiation therapy, the dose of radiation that can be applied is limited be-

cause of the damage to surrounding normal tissue (e.g muscle). Efficacy of the therapy

can be improved both by more efficient radiation delivery to the tumor while minimizing

normal tissue damage, and by increasing the sensitivity of the tumor to radiation. Effec-

tive radiation delivery to the tumor is achieved by better X- and γ-ray treatment machines,

which use a better arrangement of source distribution so that even deep human tumors

(compared to those at the surface) can be treated effectively without too much normal

tissue damage. To increase the sensitivity of the tumor, one first needs to understand the

biologic responses of both tumors and normal tissues to determine whether they respond

75

differently. The deployment of fractionated radiation, which involves 1-2 Gy dosage a

day over a total of 5 to 7 weeks, is one of the most important developments in this respect.

During fractionation, dosage adjustment can be made according to tumor response, as in

accelerated radiation therapy. Furthermore, increasing oxygen sensitivity, and modulating

the vascular response can lead to improved treatment efficacy.

5.3.1 Importance of Fractionation and Re-oxygenation

Radiation

Da

ma

ge

Normal cell repair

24 hours

Normal level

(a)

Da

ma

ge

Radiation 24 hours

Tumor cell repair

Normal level

(b)

Figure 5.5: (a) Normal tissue responses to radiation better than tumor tissue. After 24hours almost all cells repair back. (b) Tumor cells cannot repair quickly since DNA ismodified.

Non-surgical methods, such as radiation and chemotherapy kill tumor cells. However,

while killing tumor cells, there is a probability of killing normal cells too. To minimize

this effect, a very localized beam is used in radiation therapy, and that limits the dose to

normal tissues. In chemotherapy, rapidly dividing cancer cells are targeted but rapidly

dividing cells in normal tissue, such epithelial cells lining the gastrointestinal tract, limit

drug dosage. Similarly, radiation kills normal cells about as well as cancer cells, and

cells growing and dividing quickly (such as cancer cells, skin cells, blood cells, immune

system cells, and digestive system cells) are most susceptible to radiation. Fortunately,

76

most normal cells are better able to repair radiation damage than cancer cells (Fig. 5.5).

Therefore, radiation and chemotherapy treatments are parceled into component treatments

that are spaced throughout a given time interval. Cells are given a chance to repair during

the time between treatments. Since the repair rate of normal cells is greater than the repair

rate of cancerous cells, a smaller fraction of the radiation-damaged cancerous cells will

have been repaired by the time of the next treatment.

This procedure is called “fractionation” because the total dose is divided into frac-

tions. Fractionation allows greater killing of cancer cells with less ultimate damage to

the surrounding normal cells. Ideally all cancer cells will be dead after the last treatment

session.

TUMOR

ANOXICCELLS(Radioresistant)

OXYGENATEDCELLS

(a)

TUMOR

ANOXIC

CELLS

OXYGENATED

CELLS

DEAD

CELLS

REOXYGENATED

CELLS

(b)

Figure 5.6: Tumor shrinkage with radiation. (a) Solid human tumors have an anoxic innercore. (b) Shrinkage of the tumor starts from outer shell.

As noted previously, solid tumors are hypoxic in the center and better oxygenated/perfused

on the periphery (Fig. 5.6a). Therefore, during the fractionation process, destruction starts

at the periphery, allowing oxygen to penetrate further to the hypoxic cells in the interior.

During the next cycle of radiation, newly oxygenated cells are killed, and inner layers

become more oxygenated and become more sensitive to radiation (Fig. 5.6b). Four possi-

ble mechanisms of reoxygenation have been proposed [87]: Reduced oxygen metabolism,

77

improved circulation, shrinkage and migration. First, the radiation-sterilized tumor cells

may be assumed to consume less oxygen without changing the patency of the vasculature.

Second, the capillaries may become capable of carrying more blood per unit length (with

increased blood flow and/or permeability-surface area) and thereby releasing more oxy-

gen. Third, the shrinkage undergone by a tumor during the course of treatment may cause

reoxygenation by bringing the blood supply close to the less oxygenated tumor cells. The

shrinkage may move capillaries centripetally, thereby shifting the diffusion of oxygenation

centripetally as well. Disintegration of killed cells may also help this movement. Fourth,

reoxygenation may be accomplished by the preferential centrifugal migration of surviv-

ing tumor cells from the formerly hypoxic zone into zones of more oxygenated zones. It

is still unknown which mechanism play main important role but the hope is that during

reoxygenation the tumor mass will eventually be completely destroyed by multiple doses.

This re-oxygenation effect is the primary reason for fractionating the radiotherapy doses in

clinical settings and for multiple treatments being necessary to completely kill the tumor.

78

5.4 Non-invasive Diffuse Optical Measurement of Blood

Flow and Blood Oxygenation for Monitoring Radia-

tion Therapy in Patients with Head and Neck Tumors

This pilot study, that is the primary subject of this thesis, explores the potential of nonin-

vasive diffuse correlation spectroscopy (DCS) and diffuse reflectance spectroscopy (DRS)

for monitoring early relative blood flow (rBF ), tissue oxygen saturation (StO2) and to-

tal hemoglobin concentration (THC) responses to chemo-radiation therapy in patients

with head and neck tumors. rBF , StO2, THC in superficial neck tumor nodes of 8 pa-

tients were measured before and during the chemo-radiation therapy period. The weekly

rBF , StO2 and THC kinetics exhibit different patterns for different individuals, includ-

ing significant early blood flow changes during the first two weeks. Averaged blood flow

increased (52.7± 9.7)% in the first week and decreased (42.4± 7.0)% in the second week.

Averaged StO2 increased from (62.9 ± 3.4)% baseline value to (70.4 ± 3.2)% at the end

of second week, and averaged THC exhibited a continuous decrease from pretreatment

value of (80.7 ± 7.0) [µM ] to (73.3 ± 8.3) [µM ] at the end of second week and to (63.0

± 8.1) [µM ] at the end of fourth week of therapy. These preliminary results suggest daily,

diffuse optics based therapy monitoring is feasible during the first two weeks and may

have clinical promise.

5.4.1 Introduction

Head and neck cancer refers to malignancies arising from the mucosal surfaces of the oral

cavity, pharynx, nasal cavity and sinuses. Often these tumors metastasize to lymph nodes

in the neck. Several methods for treatment of head and neck cancer are used including

surgery, radiation therapy (RT), chemotherapy, and combinations thereof [59]. The vast

79

majority of head and neck cancers are squamous cell carcinomas (SCC) and treatment for

this type of cancer, especially when locally advanced, often uses radiation therapy.

Radiation therapy efficacy is known to be dependent on oxygen status [173]. Thera-

peutic treatment is less efficacious in patients with poorly vascularized/hypoxic tumors,

and it is therefore desirable to identify and target such patients for special treatment

[22, 59, 141]. To date, some correlations between oxygenation status in human solid

tumors and tumor response to therapy have been evaluated [154], but the mechanisms

associated with tumor oxygenation and blood flow variation during chemo-radiation are

poorly understood [103]. Studies including head and neck carcinoma have exhibited an

increase of positive response in tumors with high pretreatment oxygenation compared to

poorly oxygenated tumors [102, 103]. However, in these studies some well-oxygenated

tumors failed to respond, while some hypoxic tumors responded, possibly due to changes

in tumor oxygenation during treatment. One factor that modulates tumor tissue oxygena-

tion is blood flow. Recent MRI and CT investigations have demonstrated significant blood

flow changes during therapy and have suggested that these early blood flow changes may

have prognostic value [47, 75, 110]. Clearly functional assessment of blood oxygenation

and flow variation during the early weeks of treatment holds potential for assessment of

therapy efficacy/outcome. Moreover, blood flow and oxygenation changes during therapy

may enable clinicians to adjust treatment dosage.

Several methods exist for measurement of oxygenation and blood flow. The oxygen-

sensitive micro-electrode needle method provides a “reference standard” for measure-

ment of tumor oxygenation [21, 154]. However it is invasive and inconvenient for clin-

ical use [96]. Thus there remains a need for reliable non-invasive techniques that mea-

sure tumor hemodynamic responses. Tumor blood flow measurements are particularly

attractive for this application, since blood flow has been correlated with tumor oxygena-

tion [24, 60, 173]. Blood flow has been measured in clinical studies by several imaging

80

modalities including positron emission tomography (PET) [3,96,107], dynamic computed

tomography (CT) [74,75], dynamic contrast enhanced magnetic resonance imaging (DCE-

MRI) [47, 110, 143], MRI with spin labelling [144], and ultrasound color Doppler [134].

Some of these techniques require contrast agent administration (PET, DCE-MRI) or ioniz-

ing radiation (CT); others are surface sensitive (laser Doppler) [76], and most are difficult

to employ routinely with high throughput. The near-infrared diffuse optical methods pre-

sented herein offer a non-invasive, rapid, portable and low-cost alternative for repetitive

bedside monitoring of tumor therapies.

The concept of non-invasive repetitive blood flow and oxygenation measurements

is particularly attractive in the context of recent research on vascular modulating and

anti-angiogenic agents which affect the response and sensitivity of tumors to chemother-

apy and radiotherapy [71, 80, 81]. The work of Folkman [17] and other investigators,

for example, has demonstrated potential therapeutic benefits of targeting tumor vascula-

ture and tumor angiogenesis, and clinical trials of the anti-VEGF (vascular endothelial

growth factor) monoclonal antibody, Bevacizumab, have confirmed this new therapeutic

paradigm [85, 86, 179, 181]. To facilitate clinical translation of agents which target tumor

vasculature, an ability to frequently assess tumor vessel blood flow and oxygenation with

repetitive measurements is desirable, and, potentially, might lead to a means for individu-

alized radiation therapy.

Diffuse optical spectroscopy and imaging has very recently emerged as a candidate

for tumor therapy monitoring. In a case study, Jakubowski et al. [82] showed that the

greatest breast tumor physiological (hemoglobin concentrations, water content, lipid con-

tent) changes occur within the first week of neoadjuvant chemotherapy. In a similar vein,

combined diffuse optical imaging with ultrasound localization by Zhu et al. [190] demon-

strated changes in the heterogeneous hemoglobin distribution in breast tumors during

chemotherapy, and in a case study with comparison to DCE-MRI, Choe et al. [35] used

81

the diffuse optical imaging technique to quantify optical contrast of breast tumors during

chemotherapy.

In this contribution we use non-invasive diffuse optical methods to investigate tumor

responses to chemo-radiation therapy in a new class of patients with head and neck tumors.

In contrast to previous work, our instruments concurrently incorporate diffuse correla-

tion spectroscopy (DCS) as well as the more traditional diffuse reflectance spectroscopy

(DRS). The DCS methodology permits assessment of tumor blood flow before and during

radiation therapy, and the DRS measurements enable quantification of the concentration

of tissue chromophores such as oxy- and deoxy-hemoglobin. The DCS method detects

moving blood cells, and has been successfully employed in animal studies [183], for ex-

ample, burn depth estimation in pigs [20] and cerebral blood flow in rats [34, 42]; very

recently, the technique has been applied in human brain [51, 97], human muscle func-

tional studies [186] and in breast cancer patients [49]. Moreover, validation of DCS in

some cases has been provided by comparison to the power Doppler ultrasound [187],

laser Doppler [52] and arterial spin labelled MRI [188]. DRS provides information about

oxygen saturation and total hemoglobin concentration and has been widely used in tumor

and normal tissue functional studies [29, 41, 44, 57, 63, 68, 148, 152, 169].

The investigation of a limited number of patients in this pilot study reveal that weekly

relative blood flow (rBF ), tissue oxygen saturation (StO2) and total hemoglobin concen-

tration (THC) kinetics exhibit different patterns for different individuals, including (on

average) a significant early increase in rBF followed by a significant decrease in rBF .

The averaged StO2 exhibits an increase in the early weeks, while averaged THC tends to

decrease continually during therapy.

82

5.4.2 Clinical Instrumentation

We have combined DCS and DRS instruments so that the clinical instrument is compact

and mobile for convenience of clinical settings (Fig. 5.7). Below I briefly describe the

individual clinical instruments.

Figure 5.7: The clinical instrument in radiation treatment room, where head and necktumor patients have been measured. The instrument is on a mobile chart, consist of 2main parts: Blood flow instrument (DCS instrument and blood oxygenation instrument(IQ RF instrument).

83

5.4.2.1 The Clinical Diffuse Correlation Spectroscopy (DCS) Instrument

Hand-heldOptical Probe

785 nmLaser

OpticalSwitch

APD

Computer

Source Fiber

Detector Fiber

(a)

Correlator

source fibers

detectorfibers

(b)

3 c

m

Figure 5.8: (a) Diagram of the flow instrument (only 1 source fiber and 1 detector channelis shown for simplicity). The instrument consists of a 785 nm coherent laser, opticalswitch, photon-counting avalanche photo diodes (APDs), and auto-correlator board. Thedata is stored in the computer for post-processing. (b) Hand-held Optical probe: Sourceand detector fibers are inserted into a soft pad. Maximum source detector separation is 3cm.

We constructed a portable 4-channel blood flow system for use in the clinical study

(Fig. 5.8a). The instrument used a long coherence length laser (Crysta Laser, Nevada) op-

erating at 785 nm, an optical switch (DiCon Fiberoptics, California), four photon-counting

fast avalanche photodiodes (Perkin-Elmer, Canada), and a custom built four-channel auto-

correlator board (Correlator.com, New Jersey). The source light delivered to the neck was

switched between two multi-mode source fibers. Single-mode detector fibers were used

to collect the light. All fibers were inserted into a soft pad so the operator could place this

single hand-held probe onto the patient’s neck (Fig. 5.8b). Photons transmitted into the

neck were collected by the single-mode detector fibers in reflectance. The shortest and

largest separations between source and detector fibers were 2 cm and 3 cm, respectively.

84

When our signals were small we increased the number of fibers at large separation in or-

der to increase the measurement signal-to-noise (SNR) ratio [189]. Typically the average

photon penetration depth into the tissue is one-third to one-half of the source-detector sep-

aration [58,129]; thus we believe the signal originates largely from superficial neck tumor

nodes.

5.4.2.2 The Clinical Diffuse Reflectance Spectroscopy (DRS) Instrument

A four-channel frequency domain instrument was used in the clinical study for blood

oxygenation measurements (Fig. 5.9a). The details of the instrument are described else-

where [182, 185]. Briefly, the instrument uses 690 nm, 785 nm, and 830 nm laser diodes

(Thorlabs Inc., New Jersey), each of which were modulated at 70 MHz. Two 1 × 4 op-

tical switches (DiCon Fiberoptics, California) were used to switch the wavelength and

source fiber positions. The light was collected by two avalanche photodetectors (APD,

Hamamatsu, C5331-04) and two PMT’s (R928, Hamamatsu), which were coupled onto

the tissue surface via 3 mm fiber bundles. After amplifying and filtering, signals from

the detectors were mixed with a reference signal in an in-phase and in-quadrature (I&Q)

demodulator (Mini-Circuits, New York), thus generating the I and Q signal components.

After the low-pass filter, the dc I and Q signals were used to calculate the amplitude and

phase of the diffuse photon density waves (DPDWs) that passed through the tissue. In

the measurements an optical probe using two detector fiber bundles and four source fibers

were employed (Fig. 5.9b). Source and detector fibers were arranged such that at least

four different source detector separations (1.8 cm, 2.2 cm, 2.6 cm and 3 cm) were used

for each patient in order to quantify oxygenation parameters with fidelity. To calibrate the

unknown source-detector coupling and to normalize the instrument response, the symme-

try of the source-detector fibers and an Intralipid solution with known optical properties

85

Optical

Switches

LD1

LD2

LD3

RF

70 MHz

APD BPF

Mixer

IQ

A/D

A/D

LPF LPF

Amp

(a)

reference signal

3 cm

1.8 cm

D1D2

S1

S2

S3S4

(b)

2.2

cm

Figure 5.9: (a) The diagram of frequency-domain instrument. (b) Optical probe: Twodetector fiber bundles and 4 source fibers are arranged with shortest separation is 1.8 cm,and longest separations is 3 cm. S1, S2, S3, S4 are source fibers and D1 and D2 aredetector fibers.

86

were used. The absorbance of the ink was determined and calibrated using an Ocean Op-

tics spectrometer; for scattering we used the well-known formula for Intralipid from the

literature [138].

5.4.3 Measurement Protocol

week-3week-2week-1

radiat ion radiat ion radiat ionB

as

eli

ne

(DCS/DRS) (DCS/DRS) (DCS/DRS) (DCS/DRS)

week-0

Figure 5.10: Treatment and measurement schedule. See Methods section for details.

The study protocol was approved by the review board of human subjects of the Uni-

versity of Pennsylvania and informed consent was obtained from all patients. In our mea-

surements, CT and MRI scans provided additional information about the location and

size of each tumor. DCS and DRS measurements were carried out consecutively. The

protocol (Fig. 5.10) consisted of pre-radiation measurements as baseline data. Subse-

quently, weekly measurements were carried out for each individual until his/her treatment

was completed. Each patient received daily fractionated irradiation from Monday through

Friday, and the optical measurements were completed just before treatment began each

week. Patients were concurrently treated with weekly Carboplatin (Area Under the Curve

= 2 mg/ml × min ) and Paclitaxel (30 mg/m2). Daily fractionated radiotherapy was ad-

ministrated with an intensity-modulated parotid-sparing radiotherapy technique. A simul-

taneous in-field boost prescription technique was used prescribing 7040 centiGray (cGy)

87

in 220 cGy per fraction over 32 fractions to both primary and gross neck disease. The

overall treatment time was about 6.4 weeks. Standard RECIST [127] (Response Evalua-

tion Criteria In Solid Tumors) response criteria was applied to classify tumor responses.

A responder is defined as a patient with no evidence of residual cancer in the neck dis-

section specimen at the end of treatment. Formal assessment of treatment response was

conducted 6 weeks after completing therapy. Post-chemoradiotherapy neck dissections

were evaluated using pathologic response.

The optical measurements were carried out by three different operators in order to

assess the repeatability of the method. Each operator placed the probe onto the neck and

arm muscle (for control purposes) three times. The data reported in this paper represents

an average (± standard error) of the three operator measurements. Placement of the probe

and consecutive measurements of both blood flow and oxygenation took ∼15 minutes in

total, including the different observers. The largest nodal mass was selected for weekly

optical measurements. Palpation and measurements with a ruler were the main tools used

for identifying tumors during therapy. A trained radiology nurse was present during each

measurement to assist in the identification of tumors. Tumor locations were measured with

the ruler and systematically noted with respect to the ear and chin of the patients. Diffuse

optical measurements placed the probe at the same center location of each tumor, and the

measurements were repeated at that particular location. The repeatability error was small

(∼5%). No obvious trends with respect to operators were found. Only the diffuse optical

method was available for weekly measurements. Structural images such as from CT were

available at pre-therapy only.

5.4.4 Statistical Analysis

All statistical analyses were performed using Matlab (Mathwork, Inc.). Nonparametric

procedures were applied, because of some deviations from a normal distribution. Paired

88

comparisons were performed using the Wilcoxon (Mann-Whitney U) test, 2-tailed, to

identify trends and substantial changes. Differences were considered significant for p ≤0.05.

5.4.5 Results

Patient Age/Sex Histologic TNM Size/Depth TreatmentNo Type Stage (cm) Response

P-1 68/ F SCC T4b N2 M0 5.4 × 3.7 /0.4 CompleteP-2 66/ F SCC T1 N2b M0 5.0 × 3.2 /0.3 CompleteP-3 61/ M SCC T4a N1 M0 2.5 × 2.0 /0.5 CompleteP-4 63/ M SCC T2 N2b M0 3.6 × 3.1 /0.5 CompleteP-5 50/ M SCC T2 N2c M0 3.8 × 4.1 /0.4 CompleteP-6 74/ M SCC T4 N2c M0 5.1 × 4.0 /0.6 CompleteP-7 63/ M SCC Tx N2a M0 4.8 × 2.8 /0.3 CompleteP-8 49/ M SCC T4b N2b M0 5.5 × 4.5 /0.4 Partial

Table 5.1: Characteristics of patients with head and neck cancer (SCC = squamous cellcarcinoma, TNM = Tumor, Node, Metastasis stage [128]).

A total of 8 patients were examined weekly. The patient and tumor characteristics are

given in Table 5.1. Tables 5.2 to 5.4 summarize data from tumor (t) and arm muscle (m)

of 8 patients (labelled P-1, P-2, ..P-8). Table 5.2 exhibits rBF [%] at the end of week-

1, week-2, week-3 and week-4 of chemo-radiation therapy. The pretreatment value at

week-0 was defined as 100% in all patients. Similarly, Table 5.3 and Table 5.4 summarize

weekly changes of StO2 [%] and THC [µM ], respectively. For patient-2 (P-2), optical

measurements were stopped at the end of third week because the tumor was no longer

palpable. For patient-3 (P-3), optical measurements could not be acquired after the third

week of the therapy because of scheduling difficulties. It is clear from the tables that

89

Patient Tissue Week-1 Week-2 Week-3 Week-4No Type (%) (%) (%) (%)

P-1 t 152 ± 5 135 ± 9 98 ± 3 120 ± 9P-1 m 103 ± 7 119 ± 6 104 ± 6 112 ± 4P-2 t 167 ± 6 110 ± 11 108 ± 6 -P-2 m 97 ± 20 83 ± 9 80 ± 12 -P-3 t 150 ± 11 98 ± 10 118 ± 16 -P-3 m 111 ± 14 91 ± 10 119 ± 17 -P-4 t 199 ± 15 92 ± 33 71 ± 42 79 ± 34P-4 m 65 ± 15 66 ± 10 62 ± 10 63 ± 4P-5 t 148 ± 12 134 ± 12 185 ± 20 189 ± 14P-5 m 83 ± 14 81 ± 14 100 ± 22 105 ± 18P-6 t 117 ± 4 113 ± 9 104 ± 9 116 ± 8P-6 m 113 ± 18 94 ± 11 87 ± 11 109 ± 10P-7 t 136 ± 15 90 ± 5 183 ± 8 95 ± 14P-7 m 77 ± 9 90 ± 18 94 ± 8 92 ± 14P-8 t 116 ± 9 144 ± 16 197 ± 29 270 ± 17P-8 m 111 ± 5 81 ± 7 79 ± 10 74 ± 5

Table 5.2: Individual tumor (t) and arm muscle (m) relative blood flow changes (rBF (%))at the end of week-1, week-2, week-3 and week-4 of chemo-radiation therapy. Pretreat-ment value at week-0 was defined as 100% in all patients. For patient-2 (P-2), opticalmeasurements were stopped at the end of third week since tumor was not palpable any-more. For patient-3 (P-3), optical measurements could not be acquired after third week ofthe therapy because of scheduling difficulties.

our individual results varied greatly. This has also been the case in animal experiments

[107] and clinical trials [173] employing radiation therapy; apparently this variation is

only partly a result of methodological factors such as differences in probe handling and

positioning on the tissue.

90

Patient Tissue Week-0 Week-1 Week-2 Week-3 Week-4No Type (%) (%) (%) (%) (%)

P-1 t 56 ± 4 53 ± 4 59 ± 2 48 ± 4 48 ± 3P-1 m 57 ± 4 58 ± 1 60 ± 1 58 ± 3 59 ± 3P-2 t 76 ± 9 71 ± 2 74 ± 2 66 ± 2 -P-2 m 68 ± 4 69 ± 5 68 ± 3 65 ± 2 -P-3 t 50 ± 3 78 ± 4 79 ± 2 73 ± 6 -P-3 m 56 ± 4 55 ± 4 55 ± 3 56 ± 5 -P-4 t 70 ± 6 71 ± 2 74 ± 3 71 ± 4 78 ± 11P-4 m 60 ± 3 59 ± 3 58 ± 2 67 ± 2 65 ± 3P-5 t 58 ± 5 67 ± 4 75 ± 3 67 ± 10 75 ± 2P-5 m 58 ± 3 59 ± 2 58 ± 2 56 ± 1 50 ± 4P-6 t 62 ± 3 56 ± 2 58 ± 3 67 ± 6 54 ± 2P-6 m 63 ± 4 69 ± 2 64 ± 2 75 ± 3 62 ± 3P-7 t 68 ± 3 67 ± 3 74 ± 2 67 ± 3 57 ± 8P-7 m 66 ± 2 61 ± 7 62 ± 4 67 ± 4 57 ± 3P-8 t 67 ± 5 69 ± 4 73 ± 4 76 ± 3 76 ± 2P-8 m 73 ± 4 76 ± 4 75 ± 3 78 ± 2 78 ± 4

Table 5.3: Weekly blood oxygen saturation (StO2) (%) changes during chemo-radiationtherapy for both tumor (t) and arm muscle (m). For patient-2 (P-2), optical measurementswere stopped at the end of third week since tumor was not palpable anymore. For patient-3(P-3), optical measurements could not be acquired after third week of the therapy becauseof scheduling difficulties.

91

Patient Tissue Week-0 Week-1 Week-2 Week-3 Week-4No Type (µM ) (µM ) (µM ) (µM ) (µM )

P-1 t 82 ± 4 77 ± 4 74 ± 3 66 ± 5 51 ± 5P-1 m 73 ± 9 70 ± 7 75 ± 6 69 ± 5 64 ± 5P-2 t 53 ± 5 42 ± 5 40 ± 4 43 ± 3 -P-2 m 58 ± 14 56 ± 14 54 ± 8 45 ± 3 -P-3 t 75 ± 16 81 ± 20 97 ± 12 50 ± 15 -P-3 m 70 ± 6 70 ± 3 65 ± 9 61 ± 8 -P-4 t 75 ± 10 94 ± 15 80 ± 10 74 ± 12 63 ± 15P-4 m 80 ± 6 73 ± 6 80 ± 8 82 ± 4 81 ± 6P-5 t 76 ± 8 56 ± 7 51 ± 6 39 ± 6 40 ± 8P-5 m 104 ± 17 116 ± 5 113 ± 10 103 ± 6 112 ± 8P-6 t 90 ± 2 85 ± 3 99 ± 7 96 ±4 92 ±4P-6 m 86 ± 8 80 ± 3 82 ± 5 84 ± 10 80 ± 10P-7 t 114 ± 9 94 ± 18 72 ± 11 87 ± 6 64 ± 14P-7 m 73 ± 8 70 ± 17 74 ± 7 60 ± 11 67 ± 11P-8 t 30 ± 6 40 ± 8 30 ± 7 37 ± 9 50 ± 7P-8 m 45 ± 6 47 ± 9 53 ± 9 49 ± 8 56 ± 10

Table 5.4: Weekly total hemoglobin concentration (THC) (µM ) changes during chemo-radiation therapy for both tumor (t) and arm muscle (m). For patient-2 (P-2), opticalmeasurements were stopped at the end of third week since tumor was not palpable any-more. For patient-3 (P-3), optical measurements could not be acquired after third week ofthe therapy because of scheduling difficulties.

92

1 0 1 2 3 4 580

100

120

140

160

180

Number of Weeks

rBF

[%]

(a)

1 0 1 2 3 4 530

40

50

60

70

80

Number of WeeksS

tO2 [%

](b)

1 0 1 2 3 4 530

40

50

60

70

80

90

100

Number of Weeks

TH

C [µ

M]

(c)

Figure 5.11: (a) Tumor relative blood flow changes (rBF [%]) during chemo-radiationtherapy for one of the responding patients (P-1). Pretreatment value at week 0 was definedas 100%. (b) Tumor blood oxygen saturation (StO2) during chemo-radiation therapy. (c)Tumor total hemoglobin concentration (THC) during chemo-radiation therapy.

93

A representative optical response of one of the complete responders is given in Fig-

ure 5.11 (corresponding to P-1). In this case rBF increased in the early weeks, and a

subsequent decrease followed; StO2 exhibited a small decrease with a subsequent small

increase at the second week; THC exhibited a continuous drop-off during therapy.

1 0 1 2 3 4 5

100

150

200

250

300

Number of Weeks

rBF

[%]

(a)

1 0 1 2 3 4 550

60

70

80

90

Number of WeeksS

tO2 [%

]

(b)

1 0 1 2 3 4 520

30

40

50

60

70

80

Number of Weeks

TH

C [µ

M]

(c)

Figure 5.12: (a) Tumor relative blood flow changes (rBF (%)) during chemo-radiationtherapy for partial responder (P-8). Pretreatment value at week 0 was defined as 100%.(b) Tumor blood oxygen saturation (StO2) during chemo-radiation therapy. (c) Tumortotal hemoglobin concentration (THC) during chemo-radiation therapy.

Patient 8 (P-8), a partial responder, was excluded from the statistical analysis since P-

8 exhibited substantially different tumor hemodynamic response during the therapy (Fig.

5.12); in this case rBF exhibited a continual increase, while StO2 and THC also tended

94

to increase over the course of treatment. For this patient, pre-therapy CT showed a large

necrotic nodal mass measuring ∼5 cm diameter, and the tumor was still relatively large

and palpable at the end of the therapy. Post-surgical pathology confirmed the existence of

residual tumor and so the patient was considered to be a partial responder.

5.4.5.1 Average rBF Response

1 0 1 2 3 4 580

100

120

140

160

180

Number of Weeks

rBF

[%]

(a)

-1 0 1 2 3 4 550

55

60

65

70

75

80

Number of Weeks

StO

2 [%]

(b)

(c)

1 0 1 2 3 4 550

60

70

80

90

Number of Weeks

TH

C [µ

M]

Figure 5.13: (a) Tumor relative blood flow changes (rBF (%)) during chemo-radiationtherapy averaged over all patients excluding P-8. Pretreatment value at week 0 was de-fined as 100% in all patients. (b) Average tumor blood oxygen saturation (StO2(%)) dur-ing chemo-radiation therapy. (c) Average tumor total hemoglobin concentration (THC)during chemo-radiation therapy.

95

Figure 5.13 shows the trend of rBF , StO2 and THC averaged over patients 1 to 7

(P-1, P-2, ..P-7). A significant (p = .0002) increase ((52.7 ± 9.7)% ) was observed in

rBF during the first week of the therapy (Fig. 5.13(a)). Our data also showed that tumor

blood flow decreased ((42.4±7.0)%, p = .007) during the second week of the therapy and

remained low in the third and fourth weeks. The changes measured in the third (p = 0.52)

and fourth (p = 0.92) weeks were not significant. Arm muscle levels had tendency to

decrease in early weeks, but overall the changes were not statistically significant (p =

0.54, 0.25, 0.30, 0.19, respectively). Our observations are in reasonable agreement with

other studies using different methods. Mantyla et al. [107] reported absolute blood flow

changes in 43 patients (including squamous cell carcinoma of head and neck); a 56%

(mean) increase at the end of the first week and a statistically significant decrease was

observed at the end of the second week using the 133Xe clearance method. MRI studies

have also reported an enhancement in blood flow after the first week of the therapy [46,47,

110, 143, 144]; De Vries [47] quantified the blood flow changes and found a statistically

significant increase of 21% after the first week and 25% after the second week of the

radiation therapy in patients with rectal carcinoma.

The biologic significance of an increase in tumor blood flow is not well understood. It

is possible that the increase may improve tumor oxygenation and therefore tumor radiosen-

sitivity [59, 110, 173]. The mechanism for such a favorable response might be reflected

in the observations of Sonveaux et al. [151]. These investigators concluded that clinically

relevant doses of radiation elicit a vascular stress response with increased secretion of tu-

mor endothelial nitric oxide which, in turn, can cause vasodilation, increased blood flow

and increased vessel permeability. Alternatively, this early increase in blood flow may

reflect a corresponding decrease in the interstitial fluid pressure affecting the tumor vessel

distensibility and consequentially, blood flow [113]. Preferential damage to a subpopu-

lation of oxygenated cells may lower the interstitial pressure on microvessels within the

96

tumor, thus opening capillaries and increasing tumor blood microcirculation [112]. This

effect can facilitate improved chemotherapy delivery to tumors as has been demonstrated

in pre-clinical xenograft models [70, 131, 132].

5.4.5.2 Average StO2 Response

Average tumor StO2 exhibited an increase in the first two weeks and a subsequent decrease

(Fig. 5.13(b)). The changes measured in the second (p = .006) and third (p = .002) weeks

were significant, but those measured in the first (p = .08) and fourth (p = .43) weeks were

not significant. The biggest difference from baseline ((62.9 ± 3.4)%) occurred at the end

of second week ((70.4± 3.2)%, p = .0003). The corresponding weekly arm muscle StO2

levels gave p = 0.92, 0.58, 0.78, 0.03, respectively. Preliminary work has suggested that

tumor oxygenation response is dose dependent [21, 192]. Small doses of radiation may

facilitate an increased tumor oxygenation; however, relatively large doses of radiation can

also damage tumor capillaries and reduce tumor oxygenation [192]. Clinical studies on

tumor oxygenation during radiation are very scarce and are only limited to case reports

[21]. Quantitative pO2 measurements during the radiotherapy were first done by Badib

and Webster [4]. At weekly intervals, a progressive increase in tumor oxygenation was

observed. Bergsjo and Evans [11] reported a slight increase in the average oxygenation of

tumors of the uterine cervix in the early phase (within 2 weeks) period of the therapy. In

a recent study, 25 metastatic head and neck tumor nodes were investigated during chemo-

radiation therapy [21]. A clear increase was observed at the end of second week and

overall pO2 values were decreased at the end of the therapy.

5.4.5.3 Average THC Response

Average THC exhibited a continuous decrease during therapy (Fig. 5.13(c)). Weekly

changes (p = 0.40, first week), (p = 0.15, second week), (p = 0.72, third week),

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(p = 0.47, fourth week) were not statistically significant. The difference from baseline

((80.7 ± 7.0) [µM ]) to the end of the second week ((73.3 ± 8.3) [µM ]) was significant

(p = .034), however, and the biggest difference from baseline occurred at the end of

fourth week ((63.0 ± 8.1) [µM ], p = .015). THC levels are related to tumor vascularity,

and reduction of tumor vascularity after radiation and chemotherapy has been previously

reported [82,106,163]. However, revascularization (neovascularization) has also been ob-

served in tumor tissue during radiation therapy [76, 107]. Therefore it should be noted

that diffuse optical signals might be affected by combination of two opposing phenom-

ena, resulting in fluctuations in individual THC levels during chemo-radiation therapy.

Weekly arm muscle THC levels also changed, but not in statistically significant fashion

(p = 0.59, 0.80, 0.63, 0.40, respectively). Arm THC levels had some tendency to decrease,

possibly due to chemo-drugs which may induce anemia.

5.4.5.4 Average µ′s Changes

−1 0 1 2 3 4 54

5

6

7

8

9

10

Number of Weeks

µ s′ (cm

−1 )

Figure 5.14: Tumor scattering coefficient changes (µ′s(cm−1)) during chemo-radiation

therapy for an average of patients P1 to P7.

Weekly mean µ′s for P1-P7 is plotted in Figure 5.14. The results show ∼5% variation

in µ′s, suggesting that changes in functional parameters are more significant than changes

98

in the structural parameter µ′s.

5.4.6 Discussion

We have demonstrated the feasibility of diffuse optics for chemo-radiation therapy moni-

toring in head and neck cancer patients. In this section we outline some of the limitations

of the current approach. We also indicate variations in approach that will facilitate future

improvement.

In our measurements it is possible that different observers may have applied different

probe-tissue pressure and introduced different probe positioning; both of these effects can

induce variations in quantification. These variations were quantified and reported as error

bars in the figures. In addition, the head and neck region consists of different anatom-

ical tissues such as muscle, fat which can vary across patients. Thus the semi-infinite

homogeneous medium approximation is unlikely to be exactly valid. For example, our

multi-distance fitting scheme uses the short separations (ρ = 1.8, 2.2, 2.6 cm) and one

long separation (ρ = 3 cm). On the other hand, rBF was extracted using the longest sep-

aration (ρ = 3 cm) only. Therefore, StO2 and THC measurements are likely to be more

affected by near surface tissues compared to blood flow measurements. Nevertheless, we

have confined to employing the semi-infinite approximation because it simplifies our anal-

ysis enormously and enables us to extract trends from our weekly measurements. Better

quantification, as well as increased ability to distinguish different tissue structure (tumor,

muscle, fat) can be obtained from similar measurements using larger numbers of sources

and detectors, and also by image segmentation based on other available anatomical in-

formation [123, 190]. However, in practice the larger number of sources and detectors

also introduces some difficulty interfacing the probe to the tissue. From experience we

99

have found that the relatively large probe having many fibers had disadvantages, espe-

cially when trying to contact all fibers to the tissue surface with equal pressure. There-

fore a bulkier single probe containing many blood flow and oxygen saturation fibers was

avoided. In the future a better probe design may enable better quantification by preserving

good probe tissue contact.

Additional benefits may be obtained by comparing the tumor to surrounding healthy

tissue. Line-scanning, as suggested by Jakubowski et al. [82], across the tumor would be

more favorable during therapy monitoring; however, it was not possible in our case due

to time constraints. Ultimately, line scanning and/or imaging the whole tumor with rapid

data acquisition should generate a richer dataset. Although radiation therapy science has

improved with the recent technological developments to better optimize beam localization

in the tumor, normal tissue damage near the tumor may still exist. In the longer term it

would be interesting to coregister radiated volume with diffused photon path in order to

better discriminate radiation effects on normal and tumor tissues.

Finally, limitations such as uncertainties in tumor boundaries can potentially be elim-

inated by coregistering the diffuse optical methods with other structural imaging modali-

ties such as hand-held ultrasound [190] and MRI [123, 149], and by correcting for tumor

shrinkage. Since CT is available only at pre-therapy, adding a structural imaging method-

ology such as ultrasound into our protocol in the future would enable us to assess tumor

size changes weekly, during therapy. Tissue heterogeneity effects may also be investigated

with imaging techniques [123, 190].

In this study we have primarily focused on the changes of hemodynamic responses

of the tumor as a result of a perturbation, i.e. chemo-radiation therapy. Because assess-

ment of early response could potentially improve treatment outcome, the results we have

presented encourage one to focus on early weeks. Indeed, as suggested by Jakubowski

100

et al. [82], one can carry out more frequent measurements (e.g. on a daily basis) to ex-

tract trends within the first week. It might even be interesting to focus on pre-treatment

conditions, possibly targeting patients for special protocols. In its current form, however,

normalization of the blood flow to the first week precludes use of DCS before therapy

begins. Extraction of absolute measures of blood flow will require better absolute calibra-

tion. In principle DCS can be calibrated with other techniques at particular physiologic

conditions, as was done recently with MRI spin labelling technique [188], but further work

remains.

Our results suggest early clinical tumor response to radiation therapy can be detected

and quantified by diffuse optical spectroscopies. The data clearly exhibits significant

changes within two weeks of therapy. The early flow changes may be significant in affect-

ing drug delivery efficacy and/or tumor oxygenation during chemo-radiation therapy and

the early tumor oxygenation changes may be related to tumor response. The responses of

patients P1-P7 were similar, and different from that of the partial responder patient, P8,

but our statistics are not sufficient to draw significant physiological conclusions. Since

a primary aim of therapy diagnostics is to predict the response as early as possible, the

early blood flow and oxygenation changes observed here suggest the potential utility of

daily measurements during the first two weeks of treatment. Due to very low accessibility

of most other diagnostics methods, diffuse optical techniques have advantages for daily

based therapy monitoring.

5.4.7 Conclusion

Several techniques such as MRI, CT and PET have been employed for monitoring tumor

therapies, but the desire for a non-invasive, real-time bedside monitoring device makes op-

tical technique very attractive for clinical applications. Future clinical applications might

also include concurrent use of optical methods with established modalities [35, 82, 123].

101

With possible clinical requirements in mind, we have quantified tumor rBF , SO2

and THC changes non-invasively during chemo-radiation therapy using the diffuse op-

tical spectroscopies. These techniques do not require contrast agent administration and

are suitable for bedside examinations with rapid data acquisition. Our preliminary data

showed patients exhibit significant changes of rBF , SO2, THC even in the first two

weeks of the treatment. In one patient (P-8), a different trend was observed with a pre-

liminary indication that it coincided with a different treatment outcome. This anecdotal

observation should be further studied with better statistics. At this point it is difficult to

determine what prognostic role these techniques will have in the future. More statistics

are required for assessment of the prognostic value of these new methods. The present

studies suggest such experiments should be the next step.

102

5.5 Future Work: Detection and Monitoring of Primary

Head and Neck Tumors

So far we have investigated head and neck tumors that have already metastasized. Initially

these tumors originate from the oral cavities as primary tumors. It is interesting and valu-

able to detect and monitor the response of these primary tumors. Based on our research

we propose a compact instrument utilizing a hybrid optical approach. The instrument is

unique in that it combines reflectance, correlation and autofluorescence spectroscopies to

extract tumor blood oxygenation, blood flow and autofluorescence non-invasively in real

time. We expect to improve detection of early cancer by the use of autofluorescence spec-

troscopy. We expect that spectroscopically guided biopsies will yield both high sensitivity

and specificity compared to visually guided biopsies. Moreover, a low-cost, very accessi-

ble instrument will allow frequent screening to reduce the time delay of detecting possible

second primary tumors and assess early tumor response to the chemo-radiation therapy.

5.5.1 Early Detection (Autofluorescence Spectroscopy)

Anatomical exams do not tell much about early functional changes. Several studies were

presented by using different imaging techniques (PET [2,90,101,153], single photon emis-

sion computed tomography (SPECT) [158], CT [75], MRI [15]) in detection of primary

tumors. However, sensitivity and specificity parameters were not great for all these modal-

ities [158], and all of these techniques have limited availability.

During the last decade new optical techniques have been developed for screening high-

risk population in order to detect tumors earlier. Kortum et al. [37, 38, 38, 104] suggested

that optical confocal and optical coherence microscopies can differentiate oral mucosa nor-

mal and abnormal tissues with high sensitivity and specificity with high resolution com-

parable to histology. While these imaging modalities utilize only one or two wavelengths,

103

autofluorescence spectroscopy obtains more detailed information over a wide spectral re-

gion with much more compact and low-cost instrumentation. One of the most important

chromophores in autofluorescence spectra is NADH (nicotinamide adenine dinucleotide),

which reflects cellular energy metabolism [27, 30]. Endoscopic autofluorescence spec-

troscopy has been recently applied to superficial cancer in the oral cavity, showing that

diseased tissue can be discriminated from healthy tissue with a sensitivity of 86% and a

specificity of 100% [6, 7].

5.5.2 Early Therapy Monitoring (DRS and DCS)

Chemo-radiation induces toxicities to both normal and cancer cells in the oral cavities of

the head and neck region, causing irreversible injuries to oral mucosa muscle and bone,

and permanent dysfunction vasculature, which may results in a necrosis in those regions.

Oral injuries may result in changes in applied dose quantity, or/and treatment schedule.

Assessment of oral status during the cancer therapy to pick up these changes is very im-

portant for patient’s survival. Moreover, there is a report suggesting that the patients who

achieved good response histopathologically have better survival rates compared to exten-

sive residual tumors resected in the surgery [158]. Therefore to determine how much to

resect and to predict the prognosis, it is important to evaluate the chemo-radiation therapy.

It has been shown with many research modalities that assessment of early response predics

the clinical outcome [3, 75, 96, 107, 110].

5.5.3 The Hybrid Instrument

The hybrid instrument allows noninvasive measurements of blood flow, oxygen satura-

tion, and autofluorescence in a single measurement setup in real time (Fig. (5.15)). The

instrument consists of two white light sources (WL1,WL2) with built in shutters (shutter1,

104

probe

shutter1 shutter2

ON/OFFswitch

APD

spectrometer

NIRL(flow)

fiberscope

WL1 WL2autofluorescencereflectance

ACB

12

34

PC

Figure 5.15: The hybrid instrument. Near-infrared laser (NIRL) is used for blood flowmeasurement. (SM): single mode fiber. (MM): multimode fiber. (APD): avalanche photo-diodes, (ACB): autocorrelator board. End view of the fiberscope probe. 1: MM sourcefiber for DRS and DCS, 2: MM source fiber for AFS, 3: MM detector fiber for RS, AFS,ACS, 4: SM fiber for DCS.

shutter2) to monitor both reflectance and autofluorescence with a spectrometer. Shutters

allow external triggering to turn on the light sources consecutively. A near-Infrared laser

(NIRL) is used for the blood flow measurement. An On/Off switch allows the blood flow

and blood oxygen saturation measurements to be made consecutively with a small time

delay. Speckle photons are collected by a 7 micron single mode (SM) fiber. Other fibers

are chosen to be 400 micron multimode (MM) fibers. Photons are collected by fast pho-

ton counting avalanche photo diodes (APD) and the autocorrelation function is recorded

by an autocorrelator board (ACB). Figure 5.15 also shows the probe to be used during

105

these measurements. The probe consists of a fiberscope with source and detector fibers

are placed inside of fiberscope, which will allow measurements during usual check-up of

the patients.

106

Chapter 6

Summary and Future Prospects

In this thesis, I have presented the motivation, theoretical background, instrumentation,

and applications of diffuse optical spectroscopies to monitor therapies at preclinical and

clinical settings. Our results demonstrated that near-infrared diffuse optical spectroscopies,

as emerging technologies in biomedical imaging, have a promising future for monitor-

ing therapies and ultimately predicting the therapeutic outcome in clinical settings. Non-

invasively extracted intrinsic blood flow and blood oxygenation contrasts promise to have

an impact on better planning of individualized therapies, leading to better survival rates.

For clinical applications, absolute quantification as well as monitoring capabilities with

repetitive measurements in longitudinal studies are presented. Next steps include im-

provement of hand-held probes with better probe-tissue contact and more source-detector

separations. Moreover, CCD based non-contact instrumentation would allow imaging of

the whole tumor with quick data acquisition, leading to more information content as well

as patient satisfaction. This should lead to more patient enrollment in the study, which is

very important in obtaining statistically significant clinical data. Moreover, introducing

fiberscope based instrument, which can additionally measure autofluorescence, would in-

crease sensitivity and specificity in detection and quantification of primary tumors during

107

biopsies, which may result in early prediction of therapy outcome.

Molecular imaging has been a growing research field in recent years which aims to

probe the malignancy at an early stage. Furthermore, new therapy drugs are being de-

veloped so that higher tumor to normal tissue targeting is possible. Near infrared diffuse

optical techniques hold great potential to test these newly developed molecular probes in

clinical settings with deep tissue penetration [65,121,122,178]. These molecular beacons

might have direct impact on therapy monitoring; with higher sensitivity and specificity,

one can monitor tumors in their early stages, which should lead to better survival rates.

The application of near-infrared (NIR) optical methods for tumor detection and mon-

itoring is attractive for several reasons. Apart from being non-invasive, simple and fast

as pointed out above, the technique allows simultaneous measurements of tumor blood

oxygen saturation, blood volume and blood flow. Therefore, the optical method has sev-

eral unique measurable parameters with the potential to enhance tumor sensitivity and

specificity. Blood dynamics, blood volume, and blood oxygen saturation are often sub-

stantially different in the rapidly growing tumor, and will alter tissue optical absorption

coefficients. Similarly, the optical absorption and fluorescence of contrast agents such

as Indocyanine green (ICG) that occupy vascular and extravascular space provide useful

forms of sensitization. This approach has attracted increasing attention in the research for

breast cancer imaging [124] and functional brain studies [45]. Future clinical applications

might also include concurrent use of optical methods with established modalities, such as

ultrasound [32,92,120,177,190], MRI [35,123,124,149,155,165] and PET [125]. These

hybrid approaches may overcome the obstacles of low resolution of the optical image

by combining the high resolution techniques such as MRI and ultrasound with the high

specificity to blood content of NIR diffuse optical methods.

108

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