QUANTIFICATION OF MICROVASCULAR RESPONSE TO IONIZING TOMOGRAPHY LEIGH CONROY, B.ENG.MGT. RADIATION WITH SPECKLE VARIANCE OPTICAL COHERENCE

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1 QUANTIFICATION OF MICROVASCULAR RESPONSE TO IONIZING RADIATION WITH SPECKLE VARIANCE OPTICAL COHERENCE TOMOGRAPHY BY LEIGH CONROY, B.ENG.MGT. A thesis submitted in conformity with the requirements for the degree of Masters of Science Department of Medical Biophysics University of Toronto Copyright by Leigh Conroy, 2012

2 Abstract Quantification of Microvascular Response to Ionizing Radiation with Speckle Variance Optical Coherence Tomography Leigh Conroy Master of Science Graduate Department of Medical Biophysics University of Toronto 2012 Cancer cells require access to blood vessels for oxygen and nutrients to enable growth and metastasis, making the tumour vasculature an attractive potential target for cancer therapies. Recent evidence suggests that the tumour vasculature plays a significant role in tumour response to high dose radiation therapy; however this effect is not well characterized due to limitations in quantitative imaging of the microvasculature. Speckle variance optical coherence tomography is an emerging imaging modality capable of 3D, non-invasive imaging of in vivo microvasculature. This thesis outlines the work done to test the hypothesis that svoct imaging can be used to quantitatively monitor the vascular effects of high dose radiotherapy in a preclinical model. This was achieved through the development of a quantification pipeline for longitudinal 3-D svoct images of microvascular radioresponse. ii

3 ACKNOWLEDGEMENTS There are a number of people who contributed to this work that I would like to acknowledge. First I would like to thank my supervisor, Dr. Alex Vitkin, for taking me on as a graduate student and providing me with the opportunity to work on this project. His mentorship, encouragement, and enthusiasm for scientific discovery are admired and appreciated. I am grateful to Dr. Ralph DaCosta for his support in the biological aspects of this project, and for providing me with many opportunities to participate in novel research outside of this thesis. Much of the biology knowledge I have acquired over the course of this Master s was gained through my collaborations with members of the DaCosta group. This work would not have been possible without the extensive technical and intellectual support given by Dr. Adrian Mariampillai. I would like to express my sincere gratitude for the many helpful discussions and hours of technical trouble shooting assistance that he provided. I would like to thank my partner in crime in this vascular radioresponse project, Azusa Maeda, for her patience, her support with the biology and animal work, and her camaraderie. I wish her the best as she continues this research in her PhD. I would like to thank the members of my supervisory committee, Dr. Robert Bristow and Dr. John Sled, for challenging me with insightful questions, and for providing their expertise and support throughout the duration of this project. Special thanks to the other members of the Vitkin group: Bahar Davoudi, Sanaz Alali and Andras Lindenmaier for making the lab a fun and intellectually stimulating place to be each and every day. Finally, I would like to thank my friends and family for their continuing support and encouragement iii

4 TABLE OF CONTENTS Chapter 1: Introduction Canadian Cancer Incidence and Survival Rates Radiation Therapy The Tumour Microenvironment and Microvasculature High Dose Radiation Therapy and the Vascular Effect Motivation and Hypothesis: Quantification of Vascular Radioresponse... 9 Chapter 2: Optical Coherence Tomography Optical Coherence Tomography Principles of SS-OCT Speckle Variance Optical Coherence Tomography Comparison of svoct to Other in vivo Preclinical Vascular Imaging Modalities Chapter 3: Materials and Methods Animal Model Small Animal Irradiator Stereotactic Microscope Experimental Timeline Chapter 4: Development of Vascular Quantification Metrics svoct Images Noise and Artifact Removal Image Registration Image Segmentation: Binarization and Skeletonization Metrics Derived from Segmented Images Vascular Volumetric Density, Vascular Length Density, and Average Vascular Length iv

5 4.5.2 Tortuosity Fractal Dimension Tissue Vascularity Chapter 5: Quantification of Vascular Response to Single Fraction High Dose Radiation Therapy Longitudinal Tumour Vascular Response to Radiation Normal Vasculature Discussion Tumour Vasculature Heterogeneity Biological Model: Cell Line and Mouse Strain Considerations Small Tissue Volume Dose Effect Chapter 6: Conclusions Summary Future Work References Appendices A1 Quantifying Tissue Microvasculature with Speckle Variance Optical Coherence Tomography A2 In Vivo Optical Imaging of Tumor and Microvascular Response to Ionizing Radiation A3 A Spinal Cord Window Chamber Model for In Vivo Longitudinal Multimodal Optical and Acoustic Imaging in a Murine Model Permissions v

6 LIST OF FIGURES Figure 1: Ten year cancer survival ratios in Canada ( ). Some cancers, such as prostate and breast, have high survival rates however others, such as pancreas, have poor survival rates (Source: Statistics Canada, Canadian Cancer Statistics, 2012 [5]. Reproduced and distributed on an as is basis with the permission of Statistics Canada) Figure 2: Direct and indirect action of radiation (adapted from [8]). Indirect action is the more common cause of biological damage from radiation Figure 3: The therapeutic index describes the ratio between the probability of tumour control and the probability of normal tissue complications for a given dose. In radiation oncology, the aim is to increase the therapeutic index to maximize tumour control and minimize normal tissue toxicity Figure 4: Multiphoton laser scanning microscopy images comparing (a) normal and (b) tumour vasculature in a human colon cancer xenograft. The normal vasculature appears well-organized and hierarchal in comparison to the tumour vasculature which appears dilated, tortuous and heterogeneous. Scale bars 100 µm. Copyright APMIS Reproduced with permission of Blackwell Publishing from [18]; permission conveyed through Copyright Clearance Center, Inc.) Figure 5: Schematic of a Michelson Interferometer used in OCT for depth-resolved subsurface imaging. Low coherence light is split between a sample arm and a reference arm where it is reflected back by the sample and mirror, respectively. When the path length difference ( z) is less than the coherence length of the light source ( ), interference will occur vi

7 Figure 6: Cross-sectional grayscale B-scan image of a mouse dorsal skin fold window chamber (see Section 3.1). The yellow line depicts one A-scan. By taking multiple B-scans in the y direction, 3D microstructural images are obtained. Scale bar 1 mm Figure 7: A depth-encoded svoct image of in vivo vasculature. A tumour (ME-180) is present near the centre of the image. Vessels are colour-depth encoded where yellow vessels are more superficial and red vessels are deeper within the tissue. Scale bar 1 mm Figure 8: (a) A DSWC implanted on a nude mouse (b) The custom-designed immobilization frame. The window chamber was secured to a metal plate with screws and fastened to a mount with a built in heater to reduce motion in the chamber and keep the mouse at a comfortable temperature during imaging Figure 9: (a, b) The small animal irradiator, (c) The darkening of the radiochromic film indicated the irradiated volume in the DSWC Figure 10: Tumour fluorescence (cyan) overlaid on svoct image for tumour viability visualization and ROI selection. Scale bar 1 mm Figure 11: Binary morphological opening [(a) to (b)] and closing [(c) to (d)]. (a) Original binary image, before morphological opening. (b) Image from (a) after morphological opening by a disk structuring element of radius 1 (arbitrary units). All foreground regions in (a) with radius less than 1 have been removed, while those with radius 1 or higher remain unchanged. In svoct images, much of the noise originating from animal motion manifests as bright single pixel regions in the xz plane; these were removed using morphological opening. (c) Original binary image, before morphological closing. (d) Image from (c) after morphological closing by disk structuring element of radius 1 (arbitrary units). Dark spots at the edge and within the foreground region (pepper noise) with radius less than 1 have been removed. In svoct images, this smoothed small vascular surface irregularities vii

8 Figure 12: The OCT imaging head. svoct images are taken at an angle to avoid saturation from the high intensity back reflections of the glass cover slip. The red line depicts the plane of the DSWC and the yellow line shows the incident light direction. Correction for this tilt was the first step in image registration Figure 13: Example of imaging head tilt correction of a structural OCT image. Scale bars: 1 mm horizontal, 500 µm vertical. The bottom of the glass cover slip was manually traced in MATLAB and each column in the 3D dataset was shifted such that the top of the image corresponded to the bottom of the glass cover slip. This facilitated colour depth encoding and was the first step in image registration Figure 14: Screenshot of the 2D image registration feature-based point selection. Input image (left, 3 days prior to irradiation) being registered to base image (right, 1 day prior to irradiation) of the same mouse. Upper images are zoomed-in ROIs of the images shown in the bottom row. Large vessel bifurcations (blue dots) were manually tagged as anatomical features for registration Figure 15: Image registration. (a) Input image to be transformed (3 days prior to irradiation), (b) Base image (1 day prior to irradiation), (c) Input image after transformation. The original image from (a) has been translated to the left and rotated so that the large vessels now lie at approximately the same pixel location as in the base image. The scale of the input image was not changed appreciably. (d) Visual verification of image registration with transformed input image (blue) overlaid on base image (red) further demonstrating that the input image has been spatially transformed in 2D to match the base image Figure 16: Image processing flowchart for longitudinal svoct datasets. Images were preprocessed to remove noise and to smooth vessels. Next, each image in the longitudinal dataset was registered to the pre-irradiation image. The tumour region of interest was selected from the pre-irradiation image using the tumour fluorescence overlay and this ROI was automatically viii

9 applied to the remaining images in the dataset. Finally, each ROI was segmented, first into a binary 3D vascular image, and then a 3D skeleton (vessel centre line) image Figure 17: 3D isosurface rendering of binary segmented normal vasculature (1.8 mm x 1.8 mm x 550 µm). Pre-processed svoct images were binarized by applying Otsu s thresholding method in 2D to each image slice in the z (depth) direction. Applying the threshold individually to each plane retained more deep-vessel information in the segmentation because light scattering and attenuation results in decreased signal intensity with depth in svoct images Figure 18: 3D isosurface representation of normal vasculature skeleton (1.8 mm x 1.8 mm x 550 µm). Skeletons were extracted from 3D binary images using a skeletonization algorithm available as freeware [57] Figure 19: ImageJ 2D projection of a 3D vascular skeleton where each pixel has been classified as an end point (blue), slab (orange), or junction (magenta) (1.8 mm x 1.8 mm x 550 µm). Tagged skeletons were used to extract information about the vascular network Figure 20: A demonstration of why average branch length measurements of skeletonized vasculature should be used cautiously. The tree on the right is the same as the tree on the left with the addition of two small spurious vessels. These small vessels are representative of the artifacts seen in vascular skeletons due to vessel surface irregularities and noise. The number of branches increases from 5 to 9 and the average branch length decreases from 2.5 to 1.5 (arbitrary units). However, the total length (and therefore VLD) is more robust against this type of noise and is not as highly affected (11.4 vs. 12.2) Figure 21: Images (a) and (b) were computed to have approximately the same 2D fractal dimension (1.54 +/- 0.04, /- 0.02), although qualitatively there are obvious differences between the networks. Copyright 2005 Taylor & Francis. Reproduced with permission of Taylor & Francis Informa UK Ltd. from [62]; permission conveyed through Copyright Clearance Center, Inc ix

10 Figure 22: 3D visualization of potentially hypoxic regions in a tumour with a large avascular region. Vessels are shown in red and tissue greater than 100 µm from any vessel is shown in blue. 100 µm is the theoretical diffusion distance of oxygen in respiring tissues; therefore the blue regions indicate potential volumes of chronic hypoxia. ROI: 3.3 mm x 2.7 mm x 320µm. 45 Figure 23: IT1 contoured tumour ROIs for days: a) -3, b) -1, c) 1, d) 2, e) 4, f) 7. Scale bar 1 mm. The whole tumour was irradiated with a single fraction dose of 30 Gy on day 0. A large avascular region is visible near the centre of the tumour. Vascular remodeling occurred in the tumour periphery in the days following irradiation; however the avascular region persisted for up to 7 days post-rt Figure 24: The vascular density (VVD and VLD) measurements of IT1. Vessel density increased slightly prior to irradiation; however following irradiation both VVD and VLD remained fairly constant before decreasing slightly at day Figure 25: Vascularity of IT1. The average tissue to vessel distance decreased from day -3 to 1; however this trend towards increased vascularization reversed starting at day 2 post-rt Figure 26: Normalized average vascular length for IT1 showing a steady increase in vascular length starting 2 days following irradiation. This could be indicative of capillary pruning Figure 27: a) IT2 with fluorescent tumour overlay in cyan and approximate irradiation spot (30 Gy) outlined in white. Contoured tumour ROIs for days: b) -1, c) 1, d) 2, e) 4, f) 7, g) 14, h) 21. Scale bars 1 mm. Vascular ablation was evident at day 2, where a large percentage of the vessels in the tumour were no longer visible. This was followed by vascular reperfusion from day 7 to Figure 28: The vascular density (VVD and VLD) measurements of IT2. Both VVD and VLD decreased significantly starting 2 days post-rt, reaching a minimum at 50% of their pre- x

11 irradiation value on day 4 post-rt. Revascularization of the tumour area occurred between day 4 to 21, and metrics reached baseline levels (VLD) or higher (VVD) by day Figure 29: Vascularity of IT2. The tumour was well vascularized prior to and immediately following irradiation, with no tissue further than 100 µm from a vessel. Radiation-induced vascular damage resulted in a large avascular area within the tumour at day 2, resulting in a large increase in the mean tissue-to-vessel distance, reaching a maximum at day 4. While VLD and VVD measurements returned to baseline values or higher after revascularization (cf. Figure 28), the persistence of a small avascular region at day 21 was reflected by the vascularity metric where a small but significant percentage of tissue was still measured to be further than 144 µm from any vessel Figure 30: IT3 contoured tumour ROI for days a) -1, b) 1, c) 2, d) 4, e) 7. Scale bar 1mm. While vessels appeared to change slightly in orientation and depth over the course of imaging, there was no evident vascular damage or remodelling following irradiation of this tumour Figure 31: The vascular density (VVD and VLD) measurements of IT3. There were only very small fluctuations in both VVD and VLD. Overall, the VLD increased slightly following irradiation while VVD stayed relatively constant Figure 32: Vascularity of IT3. The tumour was well vascularized at the time of irradiation; however, unlike IT2 which was also well-vascularized (cf. Figure 29), the mean tissue-to-vessel distance increased slightly following irradiation and then remained relatively constant for one week Figure 33: Comparison of IT3, IT2, and untreated tumour VVD and VLD. The fluctuations in VVD and VLD for IT3 are similar in magnitude to those of an untreated tumour, indicating that IT3 experienced little or no structural vascular damage following irradiation. This is in contrast to IT2, which experienced a large decrease in vascular density following irradiation xi

12 Figure 34: Absolute VVD of the three representative irradiated tumours from day -1 to day 7 demonstrating the large heterogeneity in longitudinal radioresponse between mice. Combining these three sets of data would result in the loss of valuable information about individual response, particularly IT2 which experienced dramatic radiation-induced vascular ablation Figure 35: IT1 tumour centre vs. periphery VVD. When the avascular tumour core and the vascularized tumour periphery were analyzed separately, it could be seen that the VVD of central region of the tumour (which contained a large avascular region) changed very little following irradiation; however the VVD of the periphery decreased quite significantly due to vascular remodeling Figure 36: Three examples of non-irradiated normal window chamber vascular networks. Scale bars 1 mm. The vascular network in (a) appears well organized and hierarchal, as would be expected in normal tissue. The vasculature in (b) also appears hierarchal, although the vessels appear slightly more tortuous than those in (a). The vascular network in (c) contains areas that appear less well-vascularized than others as well as some tortuous vessels. This intra- and intervascular heterogeneity in normal DSWC vasculature is unexpected, and may be a result of the chamber implantation surgery. This is a potential disadvantage of this model when compared to bulk tissue preclinical models Figure 37: VVD and VLD of normal irradiated vasculature. VVD and VLD stayed relatively constant 3 days prior-to and 2 days following irradiation. Based on previous experiments, this would be expected in normal vasculature in response to 30 Gy Figure 38: The normal tissue in the DSWC is very well-vascularized both pre- and post- RT, with no tissue further than 100 µm from a vessel and no change in the tissue-to-vessel histogram distribution xii

13 ABBREVIATIONS µct: µmri: Micro-Computed Tomography Micro-Magnetic Resonance Tomography 2D: Two-dimensional 3D: Three-dimensional CCD: CT: DSWC: FD-OCT: HFUS: MPCM: MRI: NTCP: OCT: OER: PAM: RBC: RT: SBRT: SD-OCT: SNR: SRS: SSD: Charge Coupled Device Computed Tomography Dorsal Skin Fold Window Chamber Fourier Domain Optical Coherence Tomography High Frequency Ultrasound Multi-photon Confocal Microscopy Magnetic Resonance Imaging Normal Tissue Complication Probability Optical Coherence Tomography Oxygen Enhancement Ratio Photoacoustic Microscopy Red Blood Cell Radiation Therapy Stereotactic Body Radiotherapy Spectral Domain Optical Coherence Tomography Signal-to-noise Ratio Stereotactic Radiosurgery Source to Surface Distance xiii

14 SS-OCT: svoct: TCP: TD-OCT: Swept Source Optical Coherence Tomography Speckle Variance Optical Coherence Tomography Tumour Control Probability Time Domain Optical Coherence Tomography xiv

15 CHAPTER 1: INTRODUCTION 1.1 CANADIAN CANCER INCIDENCE AND SURVIVAL RATES Cancer cells are characterized by their ability to continuously replicate in defiance of normal restraints on cell growth and division. Tumours are formed when an abnormal cell grows and divides out of control; however the tumour is only considered malignant if the cells have the capacity to invade other tissues and form secondary tumours (metastases) [1]. In 2000, Hanahan and Weinberg proposed six underlying principles ( Hallmarks ) that are common to all cancers. They proposed that all cancer cells: (i) stimulate their own growth, (ii) resist signals to inhibit their growth, (iii) are able to resist programmed cell death (apoptosis), (iv) stimulate the growth of new blood vessels (angiogenesis), (v) have limitless replicative potential, and (vi) invade tissue and spread to distant sites [2]. Cancer has been the leading cause of premature death in Canada for the past 5 years; it is estimated that this year 186,400 Canadians will be diagnosed with cancer, and 75,700 will die from it [3]. In addition to the personal trauma and loss of life caused by cancer, this chronic disease causes significant burden on the healthcare system and economy; in 1998 it was estimated that premature death due to cancer cost Canada $11.6 billion [4]. While these statistics seem daunting, successful treatment of this disease is steadily improving; the overall 5-year cancer survival rate in Canada rose from 57% to 62% over the past decade. The ability to cure certain types of cancers such as thyroid (5 year survival of 98%), prostate (96%) and breast (88%) has improved significantly in recent years due to improvements in early detection and screening, and new treatment options [5]. However; challenges still remain, especially in lung (16%), esophageal (13%) and pancreatic (6%) cancers. Figure 1 shows the 10 year survival rates of patients in Canada (excluding Quebec) for several types of cancer. 1

16 Figure 1: Ten year cancer survival ratios in Canada ( ). Some cancers, such as prostate and breast, have high survival rates however others, such as pancreas, have poor survival rates. (Source: Statistics Canada, Canadian Cancer Statistics, 2012 [5]. Reproduced and distributed on an as is basis with the permission of Statistics Canada). Radiation therapy (RT) is one of the most commonly used cancer treatments; over half of all cancer patients receive RT as part of their overall treatment, either alone or in combination with surgery and/or chemotherapy. Radiation remains one of the most effective cancer treatments in terms of disease cure [6]. The traditional target in radiation therapy is tumour cell DNA; however, as will be discussed later in this chapter, recent evidence suggests that other biological targets, such as vascular endothelial cell membranes, may also play a critical role in tumour response to ionizing radiation [7]. 1.2 RADIATION THERAPY Biological damage from ionizing radiation occurs in three phases: (i) physical phase, (ii) chemical phase and (iii) biological phase. During the physical phase, which takes place in the first seconds following irradiation, interactions between charged particles and the atoms and molecules of the tissue occur [6]. Direct radiobiological damage occurs when the ionizing radiation causes the ejection of an electron from a molecule (ionization), and this secondary electron interacts with the DNA, creating a lesion. Indirect damage, the more common cause of radiobiological damage, occurs when the radiation interacts with other atoms or molecules in the 2

17 cell (typically water) to produce free radicals which subsequently cause DNA damage [8]. These effects are illustrated in Figure 2 below. Indirect Action OH H 2 O e - e - Direct Action Figure 2: Direct and indirect action of radiation (adapted from [8]). Indirect action is the more common cause of biological damage from radiation. The formation of free radicals through indirect radiation action happens during the chemical phase (~ seconds post-irradiation). When radiation is absorbed in the tissue, the secondary electron interacts with a water molecule which leads to the ionization of the water molecule: + H is an unstable ion radical that quickly decays to form the hydroxyl radical (OH): + 3

18 This hydroxyl radical is a highly reactive molecule that interacts with cell DNA, breaks chemical bonds, and initiates chemical changes that lead to biological damage. During this portion of the chemical phase there are competing chemical processes: those that inactivate the DNA-damage (scavenging reactions) and those that lead to stable chemical changes in the target biological molecules (fixation reactions) [6]. If oxygen is present during this reaction it will interact with the free radicals to produce a peroxide form, which will lead to an irreversible change in the chemical composition of the target molecule. Thus, the damage is chemically fixed and will induce a biological response. In the absence of oxygen, free radicals may interact with, which will return it to its original form, and no permanent biological damage will occur. It is evident that the cellular response to radiation is highly dependent on oxygen. It has been shown that oxygen enhances the biological radiation effect by approximately three times when compared to cells in hypoxic (not oxygenated) conditions. This is known as the Oxygen Enhancement Ratio (OER ~ 3) [9]. The OER is extremely important in radiobiology, as tumours often have regions of hypoxia that are resistant to RT due to this effect. The final phase encompasses the series of biological events that occur following irradiation, on the order of seconds to decades following irradiation. In the first minutes after irradiation, enzymatic reactions work to repair fixated DNA damage. The majority of DNA lesions are repaired in this phase; however the unrepaired breaks may lead to cell death when the cell attempts to divide. Early effects of radiation such as skin/mucosa breakdown and haemapoetic damage occur in the first weeks and months following radiation. This is also the time period during which tumour shrinkage is observed. Late effects such as fibrosis and normal blood vessel damage occur months to years after treatment. Finally, radiation carcinogenesis (secondary tumours as a result of radiation treatment) can develop years or decades after irradiation. The focus of this thesis is the early (~1-10 days) biological effects of radiation, specifically the early response of the microvasculature. The absorbed radiation dose, measured as the energy imparted to the tissue per unit mass, is measured in Gray (Gy) where 1 Gy = 1 J/kg [8]. 1 Gy of radiation will cause up to 10 5 ionizations in a single 10 µm diameter cell [6]. Typical clinical doses of radiation are ~ Gy per day, administered 5 days a week (Monday to Friday) for ~7-8 weeks for a total 4

19 cumulative dose of ~70-80 Gy. The practice of splitting large doses into smaller daily doses is known as fractionation. The benefits of radiation dose fractionation was first discovered in France in the 1930 s, where it was found that a ram could not be sterilized with a single large dose of radiation without causing severe skin damage; however if the radiation dose was fractionated, the ram could be sterilized with minimal skin complication [8, 10]. Applied to tumour biology, fractionation gives better tumour control for a given level of normal tissue toxicity than the equivalent single-fraction dose. An important concept in radiation oncology is the therapeutic index which describes the ratio between the probability of tumour control (TCP) and the probability of normal tissue complication (NTCP) for a given dose (see Figure 3 for an illustration) [6]. Fractionation was found to increase the therapeutic index; that is, produce fewer normal tissue complications for a given tumour control probability than the equivalent single fraction dose. The mechanism behind this is commonly referred to as the 4 R s of radiobiology: Repair of sublethal damage, Repopulation of cells, Reoxygenation, and Redistribution in the cell cycle. Essentially, normal tissue is spared in fractionated radiotherapy because normal tissue is able to repair sublethal damage between fractions and repopulation of healthy cells may occur during this time. Tumour cell kill is enhanced in fractionated therapy because previously hypoxic tumour cells (which have been shown to be radioresistant) will reoxygenate and therefore become more radiosensitive to the next fraction. Additionally, surviving tumour cells will continue through the cell cycle between fractions, and cells that were previously in radioresistant phases of the cell cycle will be redistributed into more radiosensitive phases. 5

20 % Local Tumour Control Probability TCP Dose Therapeutic Index NTCP Maximum Tolerance % Normal Tissue Complications Probability Figure 3: The therapeutic index describes the ratio between the probability of tumour control and the probability of normal tissue complications for a given dose. In radiation oncology, the aim is to increase the therapeutic index to maximize tumour control and minimize normal tissue toxicity. Fractionation increases the therapeutic index by sparing normal tissues and enhancing tumour cell kill. Other strategies to increase the therapeutic index include radiosensitization of tumour cells and radioprotection of normal cells using targeted drugs [11]. Yet another approach is to minimize the volume of irradiated normal tissue by spatial conformation of the radiation beam to the tumor. Recent advances in radiation physics have enabled highly accurate doseconformation, allowing for doses per fraction to be raised well above 2 Gy with acceptable levels of normal tissue complications. However; at such high doses per fraction, it is unclear whether the classical radiobiological models discussed above are still valid. Section 1.4 provides a review of the emerging evidence that high-dose radiation therapy may induce additional tumour cell kill through non-classical radiobiological mechanisms. 6

21 1.3 THE TUMOUR MICROENVIRONMENT AND MICROVASCULATURE Cancer cells do not exist in isolation but are supported by the connective tissues, lymphatic vessels, and blood vessels that make up the surrounding microenvironment. Recent evidence demonstrates that the tumour microenvironment plays crucial role in tumour progression, metastasis and treatment response [12-14]. The microvascular network is a critical component influencing the characteristics of the tumour microenvironment. Solid tumours are dependent on their vasculature for oxygen and nutrients to proliferate and metastasize. For a tumour to grow larger than a few millimeters in diameter, it must develop its own blood supply by recruiting new vessels from existing vessels through a process known as angiogenesis [15]. These quickly-formed vessels are highly irregular and immature, and their growth often lags behind that of the cancer cells [16]. Tumour vessel walls often consist only of a single endothelial layer lacking a basement membrane, with gaps between cells; making tumour vessels hyperpermiable. Structurally, tumour vessels have an abnormal and heterogeneous organization, are highly tortuous, and have irregular diameters, dead ends, and arteriole-venous shunts [17]. The blood flow through this chaotic network is inefficient and slow or even stationary at times. The increased leakiness and lack of proper lymphatic draining causes the interstitial pressure within tumours to be high, and this can cause collapse of small capillaries. This is in contrast to the well-organized and hierarchal structure of the normal vascular network [18]. Figure 4 illustrates the differences in between normal and tumour vasculature. (a) (b) Figure 4: Multiphoton laser scanning microscopy images comparing (a) normal and (b) tumour vasculature in a human colon cancer xenograft. The normal vasculature appears well-organized and hierarchal in comparison to the tumour vasculature which appears dilated, tortuous and heterogeneous. Scale bars 100 µm. Copyright APMIS Reproduced with permission of Blackwell Publishing from [18]; permission conveyed through Copyright Clearance Center, Inc.). 7

22 The abnormal microvasculature and lack of a proper lymphatic drainage system in tumours creates a hostile microenvironment with regions of hypoxia (decreased tissue oxygenation) and acidosis (decreased ph levels) [19]. As discussed in Section 1.2, tumour hypoxia reduces the efficiency of radiation therapy because DNA damage fixation by oxygen will not occur, and the damage will be reversed. The role of the tumour microvasculature in radiation response is discussed next. 1.4 HIGH DOSE RADIATION THERAPY AND THE VASCULAR EFFECT Significant advances in imaging, treatment planning, dosimetry, beam shaping, and radiation delivery have enabled radiation treatments that use higher doses per fraction and fewer total fractions with minimal normal tissue irradiation ( hypofractionated RT). Stereotactic radiosurgery (SRS) for brain tumours and stereotactic body radiation therapy (SBRT) for extra cranial tumours are clinical treatments that deliver spatially conformal doses of ~20-70 Gy in 1-5 fractions [20]. Emerging evidence suggests that tumours receiving high dose radiation therapy respond better than would be predicted using existing radiobiological models of tumour cell death alone [21]. The increased clinical efficacy of hypofractionated treatments has raised questions about the relevance of the 4 Rs of radiobiology in high-dose radiation, and if there are additional radiobiological effects at these elevated doses. Numerous studies have emerged supporting the idea that higher radiation doses (> 8-10 Gy) cause additional, indirect tumour cell kill by damaging the tumour vasculature [22-25]. In 1984 Julie Denekamp demonstrated that the endothelial cells of the tumour vasculature proliferate at times the rate of normal endothelial cells as they undergo angiogenesis [26]. This increased proliferation rate makes the tumour vasculature more sensitive to radiation than normal vasculature. Ablative damage to the tumour vasculature creates a hypoxic, acidic, and nutrient deprived microenvironment, which subsequently causes additional tumour cell death independent from radiation-induced tumour cell DNA damage [27]. In 2003 Garcia-Barros et al. proposed that vascular endothelial cell death is the primary contributor to tumour cell death at doses greater than ~8 Gy. This was demonstrated by showing that tumours in mice with radioresistant vascular endothelial cells (acid sphingomylenase- 8

23 deficient mice) were completely radioresistant to doses as high as 20 Gy when compared to wild type mice [7]. In contrast with the classical model of radiobiology, this apoptotic endothelial cell death is mediated by the cell membrane, and is independent from DNA damage [28]. There is an apparent lower threshold to this vascular effect, which only occurs at doses greater than ~8-10 Gy [29]. Whether tumour cells or tumour stroma/vasculature has a greater effect on tumour radiosensitivity remains a subject of debate in the literature [30-32]. Park et al. recently reviewed over 40 studies investigating the effects of radiation on tumour vasculature, and found that there is very little experimental agreement as to the effects of radiation on the tumour vasculature, and what effect it has on tumour radioresponse [33]. This lack of consensus is in part due to the variation in experimental protocols (animal model, cell line, x-ray energy, dose, etc.), as well as differences in quantification techniques. The authors summarized three general observations: [1] The vasculature of human tumours treated with conventional fractionated RT is unchanged during the first part of treatment, and vascular density and functionality decrease near the end of treatment; [2] At single fraction doses of 5-10 Gy there is mild vascular damage in human and animal tumours; [3] At doses higher than 10 Gy there is severe tumour vascular damage [33]. These trends, however, were not consistent across all of the reviewed experiments, which had variable results. 1.5 MOTIVATION AND HYPOTHESIS: QUANTIFICATION OF VASCULAR RADIORESPONSE Speckle Variance Optical Coherence Tomography (svoct) is an emerging optical modality capable of imaging three-dimensional in vivo microvasculature non-invasively. The motivation of this thesis is to quantitatively assess longitudinal vascular structural response to radiation therapy through the development of quantitative 3D metrics for svoct images. The early vascular response to high dose radiation is thought to have an effect on tumour radioresponse. The hypothesis of this thesis is that svoct can be used to quantitatively assess early vascular response to high dose single-fraction (30 Gy) radiation therapy. 9

24 CHAPTER 2: OPTICAL COHERENCE TOMOGRAPHY 2.1 OPTICAL COHERENCE TOMOGRAPHY Optical coherence tomography (OCT) is an optical imaging modality that enables three dimensional (3D), high-resolution (~10 µm), depth-resolved cross sectional imaging of subsurface tissue microstructure by measuring backscattered or back reflected light using the principles of low coherence interferometry (LCI) [34]. Typically, near infrared light is used for maximum depth penetration in tissues (up to ~ 2mm). The main principle behind OCT is detection using LCI. A simplified schematic of a Michelson interferometer is shown in Figure 5. The source light beam is split evenly between two arms (a sample arm and a reference arm). The light in the reference arm (length z R ) is reflected back from a mirror and light in the sample arm (length z S ) is reflected from the specimen. The difference in the distance travelled in the two arms is known as the path length difference z [ z = z R -z S ]. The two reflected beams are recombined in the interferometer and output as the sum of their electromagnetic fields to a detector. The detector then measures the intensity of this output, which is proportional to the square of the electromagnetic field [34]. Detector Beam Splitter z s Sample Low Coherence Light Source z R Mirror Figure 5: Schematic of a Michelson Interferometer used in OCT for depth-resolved subsurface imaging. Low coherence light is split between a sample arm and a reference arm where it is reflected back by the sample and mirror, respectively. When the path length difference ( z) is less than the coherence length of the light source ( ), interference will occur. 10

25 Low temporal coherence light is required for depth-resolved imaging. A low coherence light source has an amplitude and phase that varies in time along the propagation direction. The coherence length (l c ) is defined as the propagation length over which the temporal coherence is lost [35]. When the source light is highly coherent, interference from the back reflected signals will occur over a large range of path length differences; however if low coherence light is used, interference will only occur when the path length difference is within one coherence length. This coherence length determines the axial resolution in OCT imaging, and is inversely proportional to the bandwidth of the source; therefore broadband sources are used for improved axial resolution [36]. Equation 1 describes the coherence length (and axial resolution, ) in OCT where n is the index of refraction of the sample, λ is the centre wavelength of the source and λ is the spectral bandwidth of the source assuming a Gaussian spectrum. = = (1) By scanning the mirror in the reference arm along the beam direction, different depths within the sample are interrogated. One axial depth-scan is called an A-scan. A 2D cross sectional image (B-scan) is produced by laterally translating the sample arm and acquiring an A- scan at each position. Finally, 3D OCT images are produced from multiple B-scans (see Figure 6). 11

26 x z Figure 6: Cross-sectional grayscale B-scan image of a mouse dorsal skin fold window chamber (see Section 3.1). The yellow line depicts one A-scan. By taking multiple B-scans in the y direction, 3D microstructural images are obtained. Scale bar 1 mm. First generation OCT systems were implemented in the time domain (TD-OCT), where depth information is acquired by translating a mirror in the reference arm, as described above. The imaging speed of TD-OCT systems is limited by the mechanical translation of the reference mirror. In second generation OCT, images are acquired in the spectral or Fourier domain (FD- OCT). In FD-OCT the reference arm mirror is kept stationary and the A-scan depth information is obtained from the Fourier transform of spectrally resolved interference fringes. FD-OCT offers a higher signal-to-noise ratio (SNR) and enables faster imaging when compared to TD-OCT [36]. Spectral resolution is achieved either using a spectrometer and a charge-coupled device (CCD) array (spectral domain (SD-OCT)), or with a wavelength-swept tunable laser source (swept-source (SS-OCT)). All images in this thesis were acquired using an SS-OCT system. 2.2 PRINCIPLES OF SS-OCT The section will briefly describe the theory behind image formation in SS-OCT. The light backscattered from the sample consists of many waves originating from different depths (z) within the sample (Equation 2), where U 0 is the amplitude, k 0 =2π/λ is the wave number and λ is the wavelength of the light: 12

27 ( )= (2) For a monochromatic source, the signal output from the interferometer, measured at the detector for depth z where the roundtrip path length difference is 2 z, will be as follows: ( )= + (3) = ( ) = 2+ + =2 (1+cos(2 )) where I 0 = U 0 2. The intensity from many different depths is then: ( )=2 [1+ ( )cos (2 ) ]+ ( ) ( ) ] (4) The last term in Equation 4 describes the mutual interference of the elementary waves, and is in baseband frequencies at z=0. In OCT imaging this signal is very weak and can be filtered out by having the object signal slightly offset from z =0. Therefore this term can be neglected for the remainder of the derivation. When the measurements are performed over multiple wavelengths the interference signal becomes: ( )= ( ) 1+ ( )cos(2 ) (5) Where S(k) is the spectral intensity distribution of the light source. The depth information is now encoded in the argument of the cosine. To obtain the inverse Fourier transform of I(k), the a(z) term can be expressed as a symmetrical expansion ( ( )= ( )+ ( )) because a(-z) is on the opposite side of the reference plane and therefore equals zero. 13

28 [ ( )] [ ( )] [ ( )+ ( )] (6) Where FT - is the inverse Fourier transform and is the convolution operator. ( ), the depthdependent reflectivity of the sample, is now separated and can be recovered. In Equation 5 it becomes obvious that the axial (depth) resolution of OCT (Equation 1) is dependent on the point spread function (PSF) of the source, where the PSF is the inverse Fourier transform of the source spectrum. The lateral resolution of OCT is independent from the axial resolution, and is dependent on the focusing optics of the system (where d is the spot size on the objective lens and f is the focal length): = (7) OCT is most commonly used to image subsurface tissue microstructure, especially in ophthalmology, where the optical transparency of the eye allows for deeper imaging. The following section describes an emerging OCT imaging technique that uses structural B-mode OCT images to visualize in vivo vasculature in 3D without the use of a contrast agent and without blood flow-dependence. 2.3 SPECKLE VARIANCE OPTICAL COHERENCE TOMOGRAPHY Speckle variance optical coherence tomography (svoct) is an extension of structural OCT that enables 3D, depth-resolved imaging of in vivo vasculature. Speckle variance images are generated by calculating the inter-frame temporal speckle modulation (changes in pixel intensities as a function of time) of N B-mode frames acquired from the same spatial location. Put simply, several (N) consecutive B-mode images are taken at each position in the sample, and the variance of the signal intensity over the N scans is computed for each pixel. The endogenous contrast of svoct images originates from the different temporal scattering properties between 14

29 the liquid blood within vessels and the surrounding solid tissue. svoct image pixel values are calculated from Equation 8, where N is the number of frames used to calculate the mean and variance (known as the gate length ), is the pixel intensity of the (i,j,k) th pixel in the 3- dimensional structural image (frame, transverse, axial), and n is the frame index of the repeated B-mode image for the variance calculation, from n=1 to a maximum of n=n. = (8) This formula (Equation 8) calculates the temporal speckle variance between N frames for each pixel, where each frame is separated by the time between B-mode images (frame rate). The magnitude of the intensity of a pixel containing a liquid (blood) will change more rapidly than that of a pixel containing a solid (stationary tissue). This results in a difference in the magnitude of the calculated temporal variance and provides contrast in svoct imaging. For in vivo vascular imaging, the frame rate must be low enough such that there is complete inter-frame decorrelation of vascular blood, and high enough that stationary tissue does not decorrelate between frames. It has been shown in this lab using microsphere and water phantoms that a non-flowing microsphere undergoing Brownian motion has a decorrelation time of ~ 6 ms, corresponding to a frame rate of ~ 160 fps [37]. This sets the upper-bound for in vivo speckle-decorrelation times. At the imaging rates used (45 fps), we are imaging far below this threshold, and therefore the images are not flow dependent: slow moving and stationary blood can be detected, provided the red blood cells (RBCs) are undergoing Brownian motion. Further investigations using stationary ex-vivo rat blood have found decorrelation times of ~ 8 ms, further demonstrating this capability [38]. svoct is unique because it is both angle- and flow- independent. This offers a significant advantage over Doppler OCT, which requires a minimum blood velocity to detect vessels. The svoct system used in this thesis was built in-house and has been described previously [39]. The set-up consists of a 36 khz Fourier domain mode locked (FDML) swept source OCT system with a fiber ring laser consisting of a polygon-based tunable filter with a ~110 nm sweeping range centered at 1310 nm and an average optical output power of 48 mw. Vascular images were taken using a gate length of N=8, with 800 A-scans per B-scan, corresponding to a frame rate of 45 fps. The system resolutions in tissue are ~8 µm in the depth direction (axially) and ~13 µm laterally. 15

30 A representative svoct image of tumour microvasculature in a mouse window chamber is shown below in Figure 7. svoct images are colour depth-encoded from the surface of the tissue for visualization. Vessels closer to the surface are coloured yellow and deeper vessels are coloured red Depth From Coverslip (µm) 500 Figure 7: A depth-encoded svoct image of in vivo vasculature. A tumour (ME-180) is present near the centre of the image. Vessels are colour-depth encoded where yellow vessels are more superficial and red vessels are deeper within the tissue. Scale bar 1 mm. 2.4 COMPARISON OF SVOCT TO OTHER IN VIVO PRECLINICAL VASCULAR IMAGING MODALITIES There are numerous alternate in vivo microvascular imaging techniques, each with their own advantages and disadvantages when compared to svoct. These include: multi-photon confocal microscopy (MPCM), high-frequency ultrasound (HFUS), computed tomography (CT), magnetic resonance imaging (MRI), and the newly emerging photoacoustic microscopy (PAM). MPCM allows for high-resolution (~1 µm) 3D imaging of in vivo vascular networks using fluorescently labeled probes [40]. MCPM has significant advantages over conventional confocal microscopy because it uses near infrared (NIR) light, which is scattered and absorbed 16

31 less by biological tissue, allowing for greater depth of imaging when compared to conventional confocal microscopy (hundreds of microns). However; 3D dataset acquisition is extremely time consuming, and the shallow depth penetration means that only easily accessible areas such as the skin, eye or an implantable chamber can be imaged with this technique. Additionally, fluorescent dye may leak out from the vessels; this inhibits longitudinal imaging. HFUS has much higher depth penetration than optical imaging modalities, and does not require a contrast agent. There is a tradeoff between resolution and depth penetration in ultrasound imaging, higher frequencies (~20-50 MHz) are able to detect vessels as small as 20 µm, but at reduced penetration depths (~ 5 mm) [41]. HFUS vascular imaging is insensitive to slow flowing blood; however, recently, micro-bubble contrast agents have shown promise in increasing this sensitivity and providing additional information about blood perfusion [42]. Clinical CT and MRI do not have sufficient resolution to resolve microvasculature (<50 µm); however these modalities have been scaled-down and adapted for small animal imaging with higher resolutions (µct, µmri ) [43]. µct can be used with iodinated contrast agents to image microvasculature; however the high radiation dose of this modality makes longitudinal imaging difficult, and is unsuitable for investigations of the effects of radiation therapy [44]. µmri uses low toxicity contrast agents to image in vivo microvasculature at improved resolutions when compared to clinical MRI systems; however these machines can be quite costly and have long acquisition times [43]. PAM is an emerging technology that combines the principles of optical and ultrasound imaging. A short pulsed laser is used to irradiate tissue and photoacoustic waves are created as a result of thermoelastic expansion. The photoacoustic wave is proportional to the optical absorption coefficient, and is detected using an ultrasound transducer [45]. In vivo vasculature is detected based on the high absorption coefficient of haemoglobin at 584 nm. Dual wavelength PAM can separate the signal from deoxy- and oxyhaemoglobin for functional imaging. PAM enables high resolution imaging (~15 µm axially and ~50 µm laterally) at greater depths (> 3 mm) than pure optical imaging [45]. svoct offers imaging capabilities unique from the modalities described above. svoct has a greater imaging depth than MPCM (~ 550 µm vs. ~ 300 µm), and acquisition of highresolution 3D vascular images in svoct is much faster than in confocal microscopy for larger 17

32 fields of view. Additionally, svoct does not require the use of any contrast agents and is capable of imaging slow-flowing and even stationary blood. svoct has improved lateral resolution over HFUS and PAM, but with reduced depth penetration; therefore svoct fills the gap in the depth-resolution scale between these ultrasound-based vascular imaging modalities and optical microscopy. 18

33 CHAPTER 3: MATERIALS AND METHODS This chapter describes the preclinical imaging platform designed for longitudinal vascular imaging and irradiation that was developed previously in this lab. The platform consists of (i) the dorsal skin fold window chamber (DSWC) mouse model, (ii) a custom-built restraint device for minimizing bulk tissue motion, (iii) a small animal irradiator, (iv) stereotactic microscopy for tumour size and viability monitoring, and (v) a swept source svoct system. 3.1 ANIMAL MODEL The DSWC is commonly used in optical imaging to directly visualize in vivo vasculature [46]. This model, shown below in Figure 8a, enables non-invasive optical tumour and vascular monitoring for up to four weeks post-surgery. A titanium chamber was used with a glass cover slip of diameter 1.2 cm and thickness ~0.20 mm. The protocol for surgical implantation of the window chamber is well established, and fully described elsewhere [47]. The major advantage of this model is the ability to serially image in vivo vasculature with minimal invasiveness. However; there are also several disadvantages to this model. The surgery is technically quite difficult and therefore requires a certain level of surgical expertise, as well as specialized tools and frames. The chamber has a limited lifetime (~2-4 weeks depending on the animal and success of the surgery) and the tissue within the chamber stretches and thins at increased times post-surgery. The geometry of the chamber results in pancake shaped tumours ~3 mm in diameter and ~ mm thick. This altered geometry may not have the same physiological properties as typical clinical tumours. Female NCr nude mice (NCRNU, Taconic) were used for all experiments in this thesis. These mice were chosen because they have an immunodeficiency resulting in a reduced number of T-cells which allows for the transplantation and growth of human xenograft tumours. Additionally, their lack of hair makes window chamber implantation and maintenance easier. A previously-developed, custom-designed animal mount was used to immobilize anaesthetized mice during imaging and reduce motion in the chamber. The frame also contained a heating element to maintain a constant body temperature. This frame, shown in Figure 8b, was used for animal irradiation and imaging. 19

34 (a) (b) Figure 8: (a) A DSWC implanted on a nude mouse (b) The custom-designed immobilization frame. The window chamber was secured to a metal plate with screws and fastened to a mount with a built in heater to reduce motion in the chamber and keep the mouse at a comfortable temperature during imaging. The tumour cell line used was ME-180 human cervical carcinoma cells (provided by Dr. Richard P. Hill, Ontario Cancer Institute, University of Toronto). These cells were stably transfected with the DsRed2 fluorescent protein, which enabled tumour size and viability monitoring throughout the life of the window chamber using stereotactic microscopy (described briefly below). This cell line has been shown to be radiosensitive [48]. ~500,000 cells were injected under the fascia during the window chamber surgery for tumour implantation. All experiments were carried out under general anesthesia with intraperitoneal injection of a ketamine-xylazine mixture. Stock solution was 1 part ketamine (100 mg/ml), 1 part xylazine (20 mg/ml) and 18 parts saline. For a ~25 g mouse between µg of stock was administered. During longitudinal imaging, after multiple days of anaesthetic administration mice became resistant to the drugs; as a result the doses were raised slightly as experiments progressed. It was found that the timing of imaging post anaesthetic administration was an important factor in reducing bulk tissue motion from breathing and heartbeat. The optimal start time for imaging was found to be between 8-10 minutes following drug administration. 20

35 3.2 SMALL ANIMAL IRRADIATOR A small animal micro-irradiator (XRAD 225, Precision X-Ray Inc.) was used to focally irradiate tumours and normal tissue within the DSWC (Figure 9). A 2.5 mm lead collimator was used at a collimator to surface distance of ~3.5 cm to produce a focal irradiation spot of 4 mm at the chamber. A photon energy of 100 kvp was used with an aluminum filter and a tube current of 6.4 ma. (a) (b) (c) X-ray source Lead Collimator Animal Stage mouse Irradiated Area Figure 9: (a, b) The small animal irradiator, (c) The darkening of the radiochromic film indicated the irradiated volume in the DSWC The dose rate was calibrated using Gafchromic EBT radiochromic film with a specialized protocol and MATLAB code developed and kindly provided by Dr. Patricia Lindsay (Medical Physicist, Radiation Medicine Program, Princess Margaret Hospital, Toronto). Radiochromic film measures dose through a polymerization reaction caused by the incident photons and electrons. This reaction creates longer polymer chains that increase the optical density of the film in the incident area [49]. This results in a darkening of the film as shown in Figure 9c. The change in optical density can be correlated with the dose incident on the film through calibration with a known source. The optical density of irradiated films was measured using a commercially-available flatbed scanner and the corresponding dose was found using premeasured film calibration curve. The measured dose rate was then used to determine the irradiation time required to deliver the desired dose. To determine the effect of the glass cover slip on the dose delivered to the tissue, calibration was carried out by placing the film in the chamber without glass, and under the glass 21

36 as the mouse tissue would be. The dose rate with glass was 2.33 Gy/min and without glass 2.56 Gy/min at a collimator to film distance of ~3.5 mm. At the doses used in this thesis (30 Gy), this amounted to ~3 Gy difference in delivered dose if the calibration was carried out with or without the glass overtop of the films. Therefore, it is important to perform the irradiator calibrations with the glass in place to ensure accurate dosimetry. To aim the beam delivery focally to the tumour region, a white light image was taken using a stereotactic microscope (described briefly below). The (x,y,z) stage position corresponding to the centre of the chamber was found beforehand, and a calibrated 1 cm 2 grid was overlaid on the white light image to determine the stage offset from the centre of the chamber required to irradiate the tumour. 3.3 STEREOTACTIC MICROSCOPE Tumour size and viability was monitored with a Leica MZ FLIII Stereomicroscope (Leica Microsystems GmbH, Wetzlar, Germany). White light and transmission images were used to assess the tissue within the chamber, determine irradiator stage positions for beam aiming, and clean the glass cover slip in preparation for svoct imaging. A metal halide lamp (X-CITE 120, EXFO Photonic Solutions Inc., Quebec City, Canada) coupled with a 560±40 nm excitation filter and a 610 nm emission filter was used for fluorescent imaging of the DsRed-labelled tumour cells. Images were acquired at each time point immediately prior to svoct imaging. The transmission image was made transparent and overlaid on the 2D depth-projected svoct image to facilitate tumour fluorescence overlay for region of interest (ROI) determination (see Figure 10). The red fluorescent signal was converted to cyan for ease of visualization. 22

37 Figure 10: Tumour fluorescence (cyan) overlaid on svoct image for tumour viability visualization and ROI selection. Scale bar 1 mm. 3.4 EXPERIMENTAL TIMELINE Imaging commenced 1 week following DSWC/tumour implantation to allow for tumour growth and tissue recovery. Mice were imaged one day prior to irradiation to establish baseline vascular status and for radiation beam aiming. In later experiments, mice were imaged 3 days prior to irradiation as well to establish baseline changes prior to irradiation. Mice were imaged using svoct and the stereotactic microscope at the following time-points (in days) post-rt: 1, 2, 4 and 7. Some animals were also imaged on days 14 and 21 if the chamber was still viable. Tissue degradation, stretching, and edema within the chamber often resulted in decreased image quality at these later time-points. Additionally, motion artifacts due to breathing, pulsating vessels and involuntary muscle spasms were present on some days. Tissue degradation and large motion artifacts reduced image quality and had a detrimental effect on accurate image quantification; therefore discretion was used to discard poor-quality images from quantitative analysis. svoct image artifacts are further discussed in Section

38 CHAPTER 4: DEVELOPMENT OF VASCULAR QUANTIFICATION METRICS An image analysis pipeline for 3D quantification of longitudinal svoct images was developed for radiation response monitoring. Each component of the image processing and vascular quantification will be discussed in this chapter. 4.1 SVOCT IMAGES svoct is unique relative to other vascular imaging modalities because it is independent of external contrast agents, flow, and imaging angle. In this section the distinct characteristics of these images, including what vessels can and cannot be resolved, as well as sources of noise and artifacts, will be briefly discussed. It has been shown previously that svoct is capable of detecting the vessels of the capillary bed [50]; however capillaries with diameters smaller than the lateral resolution of the system are not resolved. Consequently, svoct is unable to monitor the effects of radiation on capillaries with diameters less than ~13 µm. There have been some reports that these small capillaries are in fact the most radiosensitive [51]. Vessels up to ~550 µm deep in tissue can be detected with svoct; however the ability to resolve capillary-sized vessels decreases with depth due to field spread and decreased SNR caused by scattering. Additionally, the forward scattering of light by red blood cells causes shadowing artifacts in the axial direction. This has two effects: (i) vessels in svoct appear elliptical in the depth direction; (ii) vessels below other vessels may appear to have discontinuities. The flow- and angle- independent sensitivity to vasculature is both a strength and a weakness in svoct imaging. Because the contrast is derived from the temporal decorrelation of the speckle signal from moving scatterers, svoct is sensitive to even small bulk tissue motion. Bulk tissue motion artifacts manifest as high-intensity streaks across the images in the scanning direction, and tend to appear near the top of the image in the axial (depth) direction. These artifacts can be caused by animal motion, breathing, heartbeat, or pulsating vessels. Several parameters were varied to reduce motion-artifacts that appear to be caused by local muscle spasms including: anaesthetic, anaethetic dose, prevention of dehydration using food 24

39 supplements, and timing of image acquisition. Raising the anaethetic dose, careful timing of image acquisition post-anaesthetic administration, and preventing dehydration all reduced the occurrences of muscle-spasm related motion artifacts; however it was found that these artifacts were not always preventable, and appeared to be partially animal-dependent. It has been discussed previously that at the frame rates used for in vivo imaging (~45 fps), svoct can detect blood that is not flowing provided there is Brownian motion of the RBCs. Therefore, vessels with slow flowing blood and blood in stasis are present in svoct images. This is a significant advantage over Doppler-based vascular imaging, which requires a minimum level of blood flow for imaging. svoct is flow-independent; however if vessels are blocked there will be no blood and therefore no svoct contrast in the vessel. This blockage may be transient and therefore in longitudinal imaging vessels may be present in images on some days and absent in others. Due to the flow-independence, interstitial fluid and blood from leaky vessels also produce signals in svoct images. Vessel leaking appears as a high-intensity blot, whereas tissue swelling (edema) decreases the overall vascular contrast and depth of signal. The following sections in this chapter describe the image processing used to minimize artifacts and extract meaningful 3D vascular segmentations from svoct images. 4.2 NOISE AND ARTIFACT REMOVAL An image processing pipeline was developed to minimize contributions from non-vessel signals (such as interstitial fluid and motion artifacts), and reduce noise in order to produce accurate segmentations. The forward scattering of light by RBCs results in an artifact that causes vessels to appear elliptical in the depth direction. This was corrected by applying a step-down exponential filter in the axial direction [52]. This filter had the effect of attenuating deeper voxels by a numerical factor proportional to the sum of the voxels immediately above in the depth-direction. The result of this filter was the minimization of the artificial vascular signal beneath the blood vessels caused by forward scattered light. Small motion such as animal heartbeat can cause artifacts in svoct images. These artifacts often manifest as thin, single-pixel-width lines in the B-scan (y) direction. In the x-z 25

40 plane, they appear as bright pixels in dark regions (salt noise). These artifacts were eliminated by applying morphological opening followed by morphological closing in each plane in the x-z direction (see Figure 11). Morphological operators compare each pixel to the surrounding pixels in the neighbourhood, where the neighbourhood is defined by the size and shape of a structuring element. A 2D circular structuring element with a radius of one pixel was used under the assumption that most vessels have approximately circular cross sections. Morphological opening consists of erosion (reduction of boundaries of regions of foreground pixels) followed by dilation (enlargement of boundaries of foreground pixels). This effect of this filter is the removal salt noise smaller than the radius of the structuring element, i.e. single bright pixel regions in background areas, such as the motion artifacts described above. Closing is the complement of opening (dilation followed by erosion) and is used to eliminate pepper noise, for example small dark pixel regions within a vessel [53]. The use of a disk-shaped structuring element also reduced some vessel surface irregularities and made the vascular cross sections more circular. Although this operation caused overall attenuation of the svoct signal, the contrast between vessels and tissue was greatly improved. Motion artifacts from larger movements, which tend to have a larger radius in the x-z direction, were not removed by this algorithm. Figure 11 shows a simplified example of binary opening and closing. This concept is easily extended to grayscale images with similar results. 26

41 (a) (b) (c) (d) Figure 11: Binary morphological opening [(a) to (b)] and closing [(c) to (d)]. (a) Original binary image, before morphological opening. (b) Image from (a) after morphological opening by a disk structuring element of radius 1 (arbitrary units). All foreground regions in (a) with radius less than 1 have been removed, while those with radius 1 or higher remain unchanged. In svoct images, much of the noise originating from animal motion manifests as bright single pixel regions in the xz plane; these were removed using morphological opening. (c) Original binary image, before morphological closing. (d) Image from (c) after morphological closing by disk structuring element of radius 1 (arbitrary units). Dark spots at the edge and within the foreground region (pepper noise) with radius less than 1 have been removed. In svoct images, this smoothed small vascular surface irregularities. A 2D median filter was used to remove additional salt and pepper noise in the axial (z) direction. Median filtering replaces each pixel with the median of its neighbours within a 2D window. It is ideal for removing outlying pixels while maintaining edges and therefore was able to remove remaining small motion artifacts and speckle noise [53]. Typically, an odddimensioned window size is used so that the centre is well-defined. A square 3x3 window was found to decrease noise without eliminating vascular signal. This filter was followed by clamping low intensity pixels. Finally, a 3D Gaussian filter with σ=0.5 was used to smooth vessels. A Gaussian filter blurs an image by convolving each pixel in the image with a Gaussian-shaped kernel with standard deviation σ. A low sigma value was used to minimize the effect of the Gaussian smoothing on vessel diameter. 27

42 In a small number of cases, this image processing eliminated some of the vessels at the resolution limits of the system (on the order of ~15 µm in diameter), especially dim or deep ones; however, this level of pre-processing was found to be critical for obtaining meaningful segmentations, as noise from animal heartbeat (present in almost all images) is often higher in intensity than vessel signals and therefore dominates in the histogram-based binarization segmentation (described below) unless removed beforehand. The parameters used in preprocessing were optimized with the goal of eliminating as much noise as necessary to obtain meaningful segmentation with as few small vessels lost as possible. Occasionally after image pre-processing, noise originating from the high intensity reflections between the cover slip and the tissue was still present. This noise was easy visible in the xz plane because it laid several microns above where the vasculature began in the tissue, and therefore was easily manually segmented out of the images 4.3 IMAGE REGISTRATION In longitudinal svoct imaging, the angle of the imaging head and the orientation of the vessels with respect to the scanning orientation changes slightly on each day of imaging (Figure 12). Additionally, treated tumours change in size and shape over the time-course of the experiment, and tissue shifts and stretches within the chamber over time; making localization of the original irradiated tumour vessels difficult. To facilitate selection of the same tumour and/or irradiated ROI for each time point, longitudinal images were spatially registered to a single base time point. 28

43 OCT Imaging Head Mouse Frame Figure 12: The OCT imaging head. svoct images are taken at an angle to avoid saturation from the high intensity back reflections of the glass cover slip. The red line depicts the plane of the DSWC and the yellow line shows the incident light direction. Correction for this tilt was the first step in image registration. The first stage of this registration was correction of the imaging head tilt (see Figure 12). svoct images of the DSWC are typically taken at imaging head angles of ~10-15 to reduce high-intensity back reflections from light incident on the glass cover slip. To correct for this tilt in the image post-processing, the bottom edge of the glass cover slip was manually defined in MATLAB and the columns of the image were shifted upwards such that the top of the image corresponded to the bottom of the glass cover slip (Figure 13). This ensured that all images were in the same orientation in the z-direction. This correction was followed by 2D non-rigid image registration to align images in the x and y directions. 29

44 z (a) (b) Figure 13: Example of imaging head tilt correction of a structural OCT image. Scale bars: 1 mm horizontal, 500 µm vertical. The bottom of the glass cover slip was manually traced in MATLAB and each column in the 3D dataset was shifted such that the top of the image corresponded to the bottom of the glass cover slip. This facilitated colour depth encoding and was the first step in image registration. x Image registration consists of determining a mapping between a pair of images, where the reference image is assumed to remain stationary, and the other is spatially transformed to match the reference [54]. The reference image will henceforth be referred to as the base image and the image to be transformed as the input image. The transform defines each pixel position in the base image to the corresponding position in the input image. Mathematically this can be expressed for the 2D case as: (, )= (, ) (9) where x and y are the original pixel-co-ordinates, T is the transform, and x and y are the transformed co-ordinates. The algorithm used to register longitudinal svoct images was global non-reflective similarity. This algorithm allowed for three types of transformations: translation, rotation, and scaling. Transformations are commonly expressed mathematically as matrices, as shown below, where and are translations in x and y respectively, θ is the angle of rotation, and and the scaling factors in the x and y directions. Translation: 1 0 = 0 1 (10)

45 Rotation: 0 = 0 (11) Scaling: 0 0 = 0 0 (12) The scaling in non-reflective similarity transforms is isotropic ( = ) and therefore can be represented by a single value s. Non-reflective similarity includes translation, rotation, and isotropic scaling while preserving original shapes and angles. These transformations can be combined using matrix multiplication: 2D Non-reflective similarity: = (13) Or simply: = ( cosθ sinθ)+ (14) = ( sinθ+ cosθ)+ (15) The transformation was created with user-defined feature-based registration in MATLAB. 2D projections in the z-direction of the two svoct images to be registered were displayed side-by-side, as shown in Figure 14 below, and corresponding large vessel bifurcations were manually tagged. Large vessel bifurcations were chosen as the feature on which to base the registration because they changed the least over the course of imaging, and tended to shift in the 31

46 chamber with the tissue (and tumour), as opposed to smaller vessels which deviated more substantially. Non-reflective similarity requires a minimum of two point-pairs to create a transform. When more than 2 point-pairs were provided a least-squares solution was found. A pointprediction feature in the software was used to determine the number of point-pairs to be used for each transform. Point-pair selection was terminated when points in three different areas of the image were deemed to be accurately predicted. A transform was created and applied to the input image, while the base image was left unchanged. The reproducibility of the registration method was estimated by repeating the registration of three image pairs and measuring the average distance between three randomly selected non-fiducial targets (i.e. points not used to create the registration). The average distance between targets was ~14 µm (range 8 µm - 32 µm), demonstrating good reproducibility between repeated registrations. The accuracy of the registration method was estimated by measuring the distance between three randomly selected non-fiducial targets in the input and base image for three image pairs. The average distance was ~50 µm (range 11 µm 82 µm), indicating that the expected error between selected ROI regions was ~50 µm. Figure 14: Screenshot of the 2D image registration feature-based point selection. Input image (left, 3 days prior to irradiation) being registered to base image (right, 1 day prior to irradiation) of the same mouse. Upper images are zoomed-in ROIs of the images shown in the bottom row. Large vessel bifurcations (blue dots) were manually tagged as anatomical features for registration. 32

47 All images were registered to the pre-irradiation image (Day -1) used for aiming the irradiation beam (see Section 3.2). This image was used for ROI selection because it contained information about both the location of the tumour at the time of irradiation and the vessels that were within the irradiated volume. A unique transformation was created for each image in the longitudinal dataset, each mapping back to the pre-irradiation time point. The accuracy of each transformed image was further verified qualitatively using visual inspection of the overlaid images as shown in Figure 15 d. From these registered images, an ROI was selected using the pre-irradiation image with the fluorescent tumour overlay (see Section 3.3). The region of interest for tumour-bearing mice was manually delineated around the fluorescent signal overlay (cf. Figure 10). This method of ROI selection allowed for maximum selectivity of tumour vessels from non-tumour vessels; however the manual trace of the fluorescent signal is subjective, and may be slightly different for each user. Errors from small deviations in ROI placement are discussed in Chapter 5. Figure 15: Image registration. (a) Input image to be transformed (3 days prior to irradiation), (b) Base image (1 day prior to irradiation), (c) Input image after transformation. The original image from (a) has been translated to the left and rotated so that the large vessels now lie at approximately the same pixel location as in the base image. The scale of the input image was not changed appreciably. (d) Visual verification of image registration with transformed input image (blue) overlaid on base image (red) further demonstrating that the input image has been spatially transformed in 2D to match the base image. 33

48 A flow-chart summarizing these processing and image registration steps, including image segmentation which is discussed in the following section, is shown below in Figure 16. Image registration Noise & Unwanted Signal Removal Raw svoct image Correct for imaging head tilt Remove glass signal 2D disk opening filter in xz planes, r=1 2D disk closing filter in xz planes, r=1 2D median filter in xyplanes [3 x 3] with threshold Straightened, noise-free svoct image Manual noise removal Yes No Signal from dirt or fluid in image? 3D Gaussian Filter δ= 0.5 Point-based image registration Segmentation Registered 3D svoct image Otsu binarizationin each z-plane Binary 3D image ROI- Bi nary 3D Binary Skeletonization Tumour fl. image ROI selection (Day -1 only) ROI Coordinates 3D ROI ROI- Skeleton Figure 16: Image processing flowchart for longitudinal svoct datasets. Images were pre-processed to remove noise and to smooth vessels. Next, each image in the longitudinal dataset was registered to the pre-irradiation image. The tumour region of interest was selected from the pre-irradiation image using the tumour fluorescence overlay and this ROI was automatically applied to the remaining images in the dataset. Finally, each ROI was segmented, first into a binary 3D vascular image, and then a 3D skeleton (vessel centre line) image. 34

49 4.4 IMAGE SEGMENTATION: BINARIZATION AND SKELETONIZATION Following image pre-processing and registration, svoct images were binarized to facilitate image quantification. A binary image is an image that has two possible values, foreground and background. In this case, foreground (white) pixels represented vessels and background (black) pixels represented tissue. In an ideal grayscale image the pixel intensity histogram will have two distinct peaks (foreground and background) separated by a valley; however in real images this is rarely the case. Otsu s method is a binarization algorithm that can be used to find the optimal threshold in these non-ideal cases [55]. This algorithm iterates through all possible thresholds and selects the one for which the sum of the variance of the two peaks on either side is minimized. The MATLAB implementation of Otsu s method was used for binarization. It was found that applying the binarization algorithm to the entire 3D image eliminated deeper vessels because svoct image intensity decreases with depth due to optical scattering and attenuation. To account for this attenuation, and to retain vessels that lay deeper within the tissue, the binarization algorithm was applied separately to each individual 2D plane in the z (depth) direction. 3D binary images were used directly to compute the vascular density and tissue-tovessel distance distribution (described in Chapters and 4.5.4). Figure 17 shows a 3D isosurface rendering of binary normal vasculature. 35

50 Figure 17: 3D isosurface rendering of binary segmented normal vasculature (1.8 mm x 1.8 mm x 550 µm). Pre-processed svoct images were binarized by applying Otsu s thresholding method in 2D to each image slice in the z (depth) direction. Applying the threshold individually to each plane retained more deep-vessel information in the segmentation because light scattering and attenuation results in decreased signal intensity with depth in svoct images. Image skeletonization reduces objects to their median line representation, while retaining fundamental topology, orientation, and connectivity. 2D skeletons contain only lines, whereas skeletonizations of 3D objects can contain both surfaces and lines. The binarized vascular images were skeletonized using a freeware implementation of the algorithm described by Ju et al. [56]. This 3D skeletonization algorithm used a novel iterative thinning and pruning algorithm. The authors claim that it is ideal for the extraction of cylindrical and plate-like objects from binary 3D images. This algorithm is unique because most skeletonization algorithms use only iterative thinning for centre line extraction, which can produce skeletons that are overly complex. There are two user inputs that control the skeletal pruning: minimum curve length and minimum surface size. The surface value was made as high as possible to eliminate any surfaces from the skeleton. The value for minimum curve length was chosen using trial and error, with starting points based on author suggestions [56]. The same parameters were used for every image. A built-in 3D Laplacian smoothing operation was used on binary images prior to skeletonization and on the resultant skeletons. For each vertex in a 3D mesh, Laplacian smoothing will give it a new position based on its neighbours. This had the effect of removing vessel surface 36

51 irregularities from the binary images and removing jagged edges from skeletons. Smoothing parameters were selected using trial and error and all images were smoothed using the same parameters. Figure 18: 3D isosurface representation of normal vasculature skeleton (1.8 mm x 1.8 mm x 550 µm). Skeletons were extracted from 3D binary images using a skeletonization algorithm available as freeware [57]. Limitations of the skeletonization algorithm included noise sensitivity, and inability to accurately segment vessels with large diameters. Image pre-processing successfully eliminated many errors from noise; however vessels larger than ~70 µm in diameter resulted in spurious branches in the skeletonized image. There are several reasons for this error. Firstly, due to the attenuation of light with depth, vessels in svoct images are brighter at the top than at the bottom. This is most pronounced in thicker vessels, and can cause the appearance of branches at the sides of the bottom of the vessel. Many observed spurious vessels in the skeletonization occurred in the axial direction due to this effect. Secondly, the two user-based parameters in the skeletonization were selected with the aim of segmenting small micro-vessels. The use of different parameters may allow for accurate segmentation of larger vessels, however this could result in elimination of the smaller vessels. Since the focus of this thesis is the microvasculature, retaining accurate small vessel skeletonization was prioritized. Larger vessels were segmented 37

52 out manually when present in tumours, however this was not common and the effect of this error was minimal. 3D skeletons were analyzed using the AnalyzeSkeleton plug-in for Image J [58]. Each voxel in the skeleton was automatically tagged as an end-point, a junction, or a slab depending on the number of neighbouring pixels. An end-point was defined as any voxel with less than two neighbours, a junction: more than two neighbours, and a slab: precisely two neighbours. These tagged voxels were visualized in 3D to verify accuracy, where end point voxels were blue, slab (branch) voxels orange and junction voxels magenta. The program then used the tagged skeleton to calculate the number of branches, number of junctions, length of each branch and the straightline distance between the end points of each branch. The accuracy of this algorithm was verified using small 3D skeletons with known properties. Figure 19: ImageJ 2D projection of a 3D vascular skeleton where each pixel has been classified as an end point (blue), slab (orange), or junction (magenta) (1.8 mm x 1.8 mm x 550 µm). Tagged skeletons were used to extract information about the vascular network. 38

53 4.5 METRICS DERIVED FROM SEGMENTED IMAGES Six vascular metrics for radioresponse monitoring were developed for the two forms of 3D segmented images (binary and skeleton): (i) vascular volumetric density (VVD), (ii) vascular length density (VLD), (iii) average vessel length, (iv) tortuosity, (v) fractal dimension and (vi) tissue vascularity. The developed metrics, how to compute them, and their applicability to radiobiological vascular response are summarized in Table 1 below. The following sections describe each metric in detail. Table 1: Vascular Metrics and their Radiobiological Relevance Metric Computation Radiobiological Relevance Vascular Volumetric Density (VVD) Vascular Length Density (VLD) Average Vessel Length # >0 # # >0 # h h # h Angiogenesis Vascular damage Vessel diameter changes (dilation/constriction) Angiogenesis Vascular damage Angiogenesis Vessel/capillary pruning Tortuosity Fractal Dimension (box counting method) h h h Slope of the linear portion of the plot: ln(# filled cubes) vs. ln(cube side length) Disease indicator Efficiency of oxygen/nutrient delivery Vascular remodelling Space-filling properties Delivery of oxygen /nutrients Network complexity (disease indicator) Tissue Vascularity Histogram of distance transform of binary image. Potential areas of chronic hypoxia 39

54 4.5.1 VASCULAR VOLUMETRIC DENSITY, VASCULAR LENGTH DENSITY, AND AVERAGE VASCULAR LENGTH The vascular volumetric density (VVD) was computed from the 3D binary image (prior to skeletonization) as the number of vascular (white) voxels in the ROI divided by the total volume of the ROI. This 3D density measurement takes vessel diameter information into account and therefore is sensitive to changes in the number and length of vessels, as well as changes in the diameters of existing vessels. Conversely, the vascular length density (VLD), computed as the total number of vascular voxels in the skeletonized image divided by the volume of the ROI, is only sensitive to changes in the length and number of vessels, since in the skeletonized image all vessels are reduced to their centre line regardless of radius. These metrics can be used together as an indication of the source of changes in vascular density. For example: if the number of vessels changes, either increasing due to angiogenesis or decreasing due to vascular damage, both VVD and VLD will change in the same direction. However, if vessels are dilated (increase in diameter) but the number and length of the vessels remain the same, VVD will rise and VLD will remain unchanged because it is independent from vessel radius. The average vascular length was computed as the average length of the branches of the skeleton, where a branch was defined as any segment between branch points or end points. An increase in the average vascular length can be indicative of vascular pruning, and a decrease may be indicative of new vessel growth and budding. This metric; however, is extremely sensitive to the spurious vessel artifacts commonly seen in skeletonizations, as the presence of even one small artifact can halve the length of a branch, as demonstrated in Figure 20 below. Taking the average of all vessel lengths over the entire ROI should improve the reliability of this measurement; however this metric should be used cautiously. 40

55 Number of Branches = 5 Average Branch Length = 2.5 Total Branch Length = 11.4 Number of Branches = 9 Average Branch Length = 1.5 Total Branch Length = 12.2 Figure 20: A demonstration of why average branch length measurements of skeletonized vasculature should be used cautiously. The tree on the right is the same as the tree on the left with the addition of two small spurious vessels. These small vessels are representative of the artifacts seen in vascular skeletons due to vessel surface irregularities and noise. The number of branches increases from 5 to 9 and the average branch length decreases from 2.5 to 1.5 (arbitrary units). However, the total length (and therefore VLD) is more robust against this type of noise and is not as highly affected (11.4 vs. 12.2) TORTUOSITY Increased vascular tortuosity is commonly associated with vascular dysfunction and disease, including cancer [59]. The tortuosity of an individual branch was computed by dividing the length of the branch by the straight-line Euclidean distance between its end points. The average tortuosity of the branches was then computed from the average of these values. The minimum value of tortuosity when measured this way is 1, indicating a completely straight vessel. As vascular tortuosity increases, blood transport efficiency decreases. Computing the average tortuosity weighs each branch equally and therefore can underestimate the tortuosity of the overall network because short branches will deviate very little from a straight line and have values close to 1. To place more weight on longer branches, tortuosity was computed by dividing the sum of all branch lengths by the sum of their individual Euclidean distances. While it was found that, on average, tumour vasculature had a higher tortuosity value than normal vasculature, this result was not statistically significant, and only small changes in tortuosity measurements 41

56 were observed between time points where vascular network tortuosity appeared to visibly change. The cause of this apparent lack of sensitivity may be the use of individual branches in the calculation: the average branch length for most networks was approximately 100 µm, or ~12 pixels in length and therefore, for most branches, very little deviation from the straight line distance was possible before a branching point FRACTAL DIMENSION Fractal dimension is a popular quantification metric for vascular networks. The fractal dimension can be used to characterize the space filling characteristics, complexity, and tortuosity of vascular networks [52, 60]. Mathematically, the fractal dimension describes how well an image fills space as scale is decreased. Biologically, fractal dimension can reflect the efficiency of oxygen and nutrient delivery [61]. The fractal dimension is independent from vessel diameter or number of vessels; however, it is possible for vascular networks that are visually quite different to have the same fractal dimension value [62] (Figure 21). Nonetheless, there has been some success in using fractal dimension to characterize vascular networks [52, 61, 63, 64]. Figure 21: Images (a) and (b) were computed to have approximately the same 2D fractal dimension (1.54 +/- 0.04, /- 0.02), although qualitatively there are obvious differences between the networks. Copyright 2005 Taylor & Francis. Reproduced with permission of Taylor & Francis Informa UK Ltd. from [62]; permission conveyed through Copyright Clearance Center, Inc. There are numerous ways to compute fractal dimension including (i) box counting, (ii) correlation, (iii) Sandbox, and (iv) Fourier Spectrum [60]. The most common method for 42

57 computing fractal dimension is the box counting method. Using the box counting method, the fractal dimension is acquired by decreasing the cube size of a 3D grid overlaid on the skeleton and counting the number of cubes containing at least one skeleton pixel at each scale. The natural logarithm of the number of filled cubes is plotted against the natural logarithm of the cube side length, and the fractal dimension is the slope of the linear portion of this plot. A 3D box counting algorithm was implemented on skeletonized vascular networks using ImageJ [65]. The accuracy of the algorithm was first validated by computing the fractal dimension of images with known fractal properties. Digital images are not pure fractals because they are limited by the boundaries of the size of the image and the size of one pixel; therefore the relationship between number of filled cubes and cube side length will be logarithmic only over a finite number of box sizes. A straight line (R 2 > 0.99) was fit to the linear portion of the ln-ln plot of filled cube vs. cube side length to determine the fractal dimension. A small (but not significant) difference was seen when normal (n=3) and tumour (n=3) vascular networks were compared (normal: /- 0.02, tumour: /- 0.10). This suggested that the normal vasculature filled the 3D space more completely than the tumour networks, which are spatially heterogeneous and prone to large avascular regions. Further analysis, however, found a linear portion of the line could not be found in all skeletonized svoct images, indicating that some vascular networks did not exhibit fractal characteristics on any scale. The issue of very narrow or non-existent regions of logarithmic scaling relationship has been previously reported when computing the fractal dimension of real vascular networks using the box counting method [66]. The absolute value of the computed fractal dimension using this algorithm appeared to be somewhat dependent on the analysis parameters (such as box size scaling and number of repeated counts). The Sandbox method has been shown to be a more robust method to compute fractal dimension for vascular images [66]; however this method is more computationally difficult to implement, and is not available in many commercial packages. Initial results comparing normal and tumour vasculature indicated that the fractal dimension may be a useful metric for characterizing 3D svoct vascular networks; however additional work is required to improve the robustness and repeatability of this technique, and to 43

58 determine the true biological relevance of this metric. Future work should consider the use of alternate methods of computing fractal dimension, such as the Sandbox method TISSUE VASCULARITY A 3D Euclidean distance transform was applied to 3D binary vascular images to quantify the overall vascularity of the region of interest. The output distance transform, or distance map, was the same size as the binary input image, where each voxel in the map represented the straightline distance from that voxel to the nearest non-zero (vessel) pixel. Therefore, the 3D output image quantified the distance between each voxel of tissue and the nearest vessel in threedimensions. While vascular signal is typically detected from up to ~550 µm in tissue, in svoct imaging the ability to resolve small vessels decreases with increased depth, therefore, signals from deeper than ~300 µm are typically from larger vessels only. This effect can be seen in the depth-encoded images by the paucity of red coloured capillary-sized vessels. In computing the tissue vascularity, this 3D image characteristic led to the systematic over-estimation of intervascular distance at larger depths. To minimize this effect and maximize sensitivity to avascular and potentially hypoxic regions, this metric was computed over the first 320 µm of tissue. This depth was chosen because it is the depth at which normal, completely vascularized DSWC tissue was measured to be fully vascularized (no pixels greater than ~100 µm from a vessel in 3D). Visually, the distance map can be used to highlight areas of tissue that are poorly vascularized. Figure 20 shows the 3D distance transform overlay on the 3D vascular image (red); where a threshold was applied to the distance transform to show only those voxels greater than 100 µm from a vessel (blue). This tumour ROI had a very large avascular region, shown here for demonstration purposes. This measurement is particularly relevant in visualizing potential areas of chronic hypoxia, which are likely to be resistant to radiation. It should be cautioned that tissue oxygenation levels are dependent on both vascular structure and function, as well as blood oxygenation levels and tissue oxygen consumption. Therefore, the true physiologically hypoxic areas may be different from those identified using the vascular structure information alone. 44

59 Figure 22: 3D visualization of potentially hypoxic regions in a tumour with a large avascular region. Vessels are shown in red and tissue greater than 100 µm from any vessel is shown in blue. 100 µm is the theoretical diffusion distance of oxygen in respiring tissues; therefore the blue regions indicate potential volumes of chronic hypoxia. ROI: 3.3 mm x 2.7 mm x 320µm The vascularity of the tissue was quantified by constructing a histogram of tissue-tovessel distances from the 3D distance transform map, where voxels containing vessels were excluded. This quantification enables the extraction of information about vascular network heterogeneity that can be lost due to averaging in density-based measurements. The bins of the histogram were chosen to provide information about the distribution of tissue surrounding the theoretical oxygen diffusion distance in tissue (~100 µm) [67]. 45

60 CHAPTER 5: QUANTIFICATION OF VASCULAR RESPONSE TO SINGLE FRACTION HIGH DOSE RADIATION THERAPY This chapter describes experiments using the developed 3D metrics to quantify the longitudinal effects of ionizing radiation on tumor and normal vasculature in the DSWC. Tumour vasculature is known to be heterogeneous, both between and within tumours [68, 69]. Over 20 mice have been imaged using the experimental protocol described in Chapter 3. Great inter-tumour heterogeneity in the vascular structure at the time of irradiation, and in the subsequent response, was observed. Somewhat surprisingly, non-irradiated normal vasculature in the window chamber was also visibly heterogeneous between animals. In this chapter, representative examples of irradiated tumours, a non-irradiated tumour, and irradiated normal vasculature are quantified using the methods described in Chapters 3 and LONGITUDINAL TUMOUR VASCULAR RESPONSE TO RADIATION The longitudinal radioresponses of three individual tumours representative of the observed heterogeneity were quantified. The variation due to ROI placement within one animal was estimated by moving a square ROI encompassing the tumour fluorescence to a slightly different position within a sample tumour and re-calculating each measurement three times. This was repeated for seven images and the average percent standard deviation was found to be ~10%. The 10% value used for error bars on the graphs in this chapter are intended only as a guideline as to the magnitude of changes that may be significant within a single animal, and do not represent statistics. All tumours were treated with 30 Gy single fraction irradiation using the irradiator settings described in Chapter 3.2. Previous experiments with this model have shown that 30 Gy is sufficient to induce tumour cell response (determined by a reduction in tumour size) within the lifetime of the window chamber. The irradiation spot size was 4 mm in diameter at the window chamber and was centered on the tumour for all irradiations. No tumour exceeded 4 mm in diameter; therefore all vessels within the delineated tumour ROI were irradiated. The same protocol was followed for normal tissue irradiation, where the irradiation beam was aimed at the centre of the DSWC. 46

61 Of the three representative irradiated tumours, one had a large avascular region near the centre at the time of irradiation, whereas the other two were well vascularized at the time of irradiation, but had different vascular responses. These examples are quantified here to demonstrate the utility of the developed metrics and to illustrate the complex intra- and intertumour vascular heterogeneity that was observed. Irradiated Tumour #1 (IT1): Avascular Core The first tumour was poorly vascularized in the centre at the time of irradiation, as seen in Figure 23. Qualitatively, the avascular hole became slightly smaller (more vascularized), and there was remodeling of the vessels near the rim of the avascular region following irradiation. There was a notable decrease in the number of deeper vessels visible on Days 4 and 7. It is believed that tissue edema reduced the speckle variance SNR at these depths because the effect was seen throughout the chamber, not just in the area of irradiation. To account for this, densities were computed over the reduced penetration depths on Days 4 and 7. The tumour region was defined by contouring around the tumour fluorescence overlay on the svoct image for Day -1, and this contoured region was applied to each registered image in the time series (Days -3, -1, 1, 2, 4, and 7). (a) (b) (c) 0 (d) (e) (f) Depth From Coverslip (µm) 500 Figure 23: IT1 contoured tumour ROIs for days: a) -3, b) -1, c) 1, d) 2, e) 4, f) 7. Scale bar 1 mm. The whole tumour was irradiated with a single fraction dose of 30 Gy on day 0. A large avascular region is visible near the centre of the tumour. Vascular remodeling occurred in the tumour periphery in the days following irradiation; however the avascular region persisted for up to 7 days post-rt. 47

62 The volumetric and length densities of the tumour (Figure 24), increased slightly from Day -3 to -1, and remained fairly constant until Day 7, where there was a decrease in both density metrics. It is interesting to note that the decrease in VVD and VLD did not correspond with vascular ablation as might be expected (and as seen in the following tumour, IT2), but by what appears to be an increase in intercapillary distance at Day 7. This decrease in density may also be partially attributed to the effects of tissue swelling. Normalized VVD Time Point (Days) Normalized VLD Time Point (Days) Figure 24: The vascular density (VVD and VLD) measurements of IT1. Vessel density increased slightly prior to irradiation; however following irradiation both VVD and VLD remained fairly constant before decreasing slightly at day 7. Tissue vascularity was computed over the smallest rectangular region that fully encompassed the tumour fluorescent signal. The results indicated that in the days before and immediately following irradiation (-3, -1, 1), the tumour became more vascularized as the avascular core began to fill in. This trend towards vascularization of the avascular region ended 2 days following irradiation. The tumour reached a maximum average tissue-to-vessel distance 4 days following irradiation. These small fluctuations and the persistence of the avascular core were in contrast to the observations of a control (non-irradiated) tumour that had a similar avascular core at the start of imaging. The untreated tumour steadily became more vascularized over the course of the experiment, with no obviously visible avascular core by Day 7. This was observed qualitatively; however it is not quantified here because the images in the dataset had 48

63 large motion artifacts due to involuntary muscle spasms, and meaningful quantification was not possible. Based on this observation, it is possible that radiation slowed or delayed the vascularization of the avascular tumour core. The spatial vascular heterogeneity between the centre and periphery of this tumour resulted in averaging of VVD and VLD between the two regions and an overall decrease in the sensitivity to the vascular changes in the periphery region. An approach to reduce this effect is discussed later in this chapter. 100% Tissue-to-Vessel Distance(µm) > 144 Percent Tissue 90% 80% 70% 60% 50% Time Point (Days) Figure 25: Vascularity of IT1. The average tissue to vessel distance decreased from day -3 to 1; however this trend towards increased vascularization reversed starting at day 2 post-rt. It has been noted previously that average vascular length derived from vascular skeletons can be sensitive to image noise; however in this tumour, the increase in average vascular length was consistent with visual observation, and followed a steady and significant trend upwards. This measurement describes the visible vascular remodeling that occurred within the tumour, with fewer short vessels and more long vessels as time progressed. This could be indicative of radiation-induced capillary pruning, or capillary collapse due to elevated interstitial pressure. 49

64 Normalized Average Vascular Length Time Point (Days) Figure 26: Normalized average vascular length for IT1 showing a steady increase in vascular length starting 2 days following irradiation. This could be indicative of capillary pruning. Irradiated Tumour #2 (IT2): Vascular Ablation Vascular ablation in response to single fraction high dose RT was observed in some tumours, one of which is quantified here. This tumour was well-vascularized at the time of irradiation, and a dramatic decrease in vascular density was observed starting on Day 2. Quantifiable images were obtained up to 21 days post-irradiation, and re-vascularization of the tumour was observed starting on Day 7. The centre of the ablated region had not fully revascularized by Day 21 (Figure 27). 50

65 Figure 27: a) IT2 with fluorescent tumour overlay in cyan and approximate irradiation spot (30 Gy) outlined in white. Contoured tumour ROIs for days: b) -1, c) 1, d) 2, e) 4, f) 7, g) 14, h) 21. Scale bars 1 mm. Vascular ablation was evident at day 2, where a large percentage of the vessels in the tumour were no longer visible. This was followed by vascular reperfusion from day 7 to 21. The tumour was contoured according to the fluorescent signal overlay (Figure 27 a) and this ROI was applied to all time points in the dataset (Days -1, 1, 2, 4, 7, 14 and 21). The VVD and VLD of the tumour began decreasing at Day 1 following irradiation, reaching a minimum at Day 4 (Figure 28). VVD and VLD values began to rise again on Day 7 and continued to Day 21, as the tumour began to revascularize. While the VVD rose well above baseline levels by Day 21, there was still a visible avascular region, with a densely vascularized rim surrounding it. Unlike VVD, VLD did not increase above baseline levels, indicating that the post-radiation vessels on average have a larger radius than the pre-irradiation vessels that were destroyed. This result correlates with visual observation of thicker vessels at the revascularizing tumour rim. 51

66 Normalized VVD Timepoint (Days) Normalized VLD Timepoint (Days) Figure 28: The vascular density (VVD and VLD) measurements of IT2. Both VVD and VLD decreased significantly starting 2 days post-rt, reaching a minimum at 50% of their pre-irradiation value on day 4 post-rt. Revascularization of the tumour area occurred between day 4 to 21, and metrics reached baseline levels (VLD) or higher (VVD) by day 21. This tumour was very well vascularized prior to irradiation, with almost no tissue further than ~120 µm from a vessel (Figure 29). A dramatic increase in the mean tissue-to-vessel distance was seen on Day 2, correlating with the visual disappearance of vessels in a significant region of the tumour. The percentage of tissue further than 100 µm from any vessel in 3D reached a maximum on Day 4, when the region of ablated vasculature was largest. The percentage of tissue greater than 144 µm from a vessel decreased steadily from Days 7 21, quantifying the gradual reperfusion of vessels into the avascular ablated core. The tissue vascularity metric provided a clearer indication that the tumour vascular distribution was more heterogeneous on Day 21 than it was prior to irradiation, as demonstrated by the differences in the distribution for tissue greater than 72 µm from a vessel. The percentage of tissue further than ~100 µm from a vessel was ~0% prior to irradiation and ~2% on Day 21, indicating the persistence of the avascular ablated region. 52

67 Tissue-to-Vessel Distance ( µm) 100% > 144 Percent Tissue 90% 80% 70% 60% 50% Time Point (Days) Figure 29: Vascularity of IT2. The tumour was well vascularized prior to and immediately following irradiation, with no tissue further than 100 µm from a vessel. Radiation-induced vascular damage resulted in a large avascular area within the tumour at day 2, resulting in a large increase in the mean tissue-to-vessel distance, reaching a maximum at day 4. While VLD and VVD measurements returned to baseline values or higher after revascularization (cf. Figure 28), the persistence of a small avascular region at day 21 was reflected by the vascularity metric where a small but significant percentage of tissue was still measured to be further than 144 µm from any vessel. Irradiated Tumour #3 (IT3): No Response The final quantified treated tumour was vascularized similarly to IT2 at the time of irradiation; however this tumour did not experience significant vascular ablation as was seen in IT2. There was some vascular remodeling over the 7 day period following irradiation, but overall the vascular metrics (density, vascularity) did not change significantly. 53

68 (a) (b) (c) (d) 0 (d) (e) Depth From Coverslip (µm) Figure 30: IT3 contoured tumour ROI for days a) -1, b) 1, c) 2, d) 4, e) 7. Scale bar 1mm. While vessels appeared to change slightly in orientation and depth over the course of imaging, there was no evident vascular damage or remodelling following irradiation of this tumour. When quantified it was found that the VVD and VLD decreased very slightly following irradiation reaching a minimum at Days 2 and 1, respectively, then climbed above baseline levels by Day 4 and remained higher than baseline levels until Day 7. The magnitudes of these fluctuations were very small when compared to the post-irradiation changes in IT Normalized VVD Time Point (Days) Normalized VLD Time Point (Days) Figure 31: The vascular density (VVD and VLD) measurements of IT3. There were only very small fluctuations in both VVD and VLD. Overall, the VLD increased slightly following irradiation while VVD stayed relatively constant. 54

69 The tumour was well-vascularized at the time of irradiation with <1% of the tissue further than 120 µm from a vessel. The tumour became slightly more vascularized between Days -1 and 1 and the tissue-to-vessel distribution stayed constant at these values throughout the experiment to Day % Tissue-to-Vessel Distance (µm) >144 Percent Tissue 90% 80% 70% 60% 50% Time Point (Days) Figure 32: Vascularity of IT3. The tumour was well vascularized at the time of irradiation; however, unlike IT2 which was also well-vascularized (cf. Figure 29), the mean tissue-to-vessel distance increased slightly following irradiation and then remained relatively constant for one week. The longitudinal vascular density of IT3 following irradiation was compared to an untreated tumour over a similar time period (Figure 33). The vascular densities of the two tumours followed similar trends (a small, gradual increase in vascular density) for one week post-irradiation of the treated tumour. This is in contrast to the response of IT2, where the vascular density (both VVD and VLD) decreased drastically two days following irradiation and remained below 60% of pre-irradiation values through days 4 and 7 (Figure 33). 55

70 Normalized VVD Normalized VLD Time Point (Days) Irradiated Tumour #3 Untreated Tumour Irradiated Tumour # Time Point (Days) Irradiated Tumour #3 Untreated Tumour Irradiated Tumour #2 Figure 33: Comparison of IT3, IT2, and untreated tumour VVD and VLD. The fluctuations in VVD and VLD for IT3 are similar in magnitude to those of an untreated tumour, indicating that IT3 experienced little or no structural vascular damage following irradiation. This is in contrast to IT2, which experienced a large decrease in vascular density following irradiation. From Figure 34, where the absolute longitudinal VVD for all three tumours is shown as an example, it can be seen that the vascular response to radiation of the three representative tumours were very different, and that averaging would result in the loss of valuable information about each individual response. This inter-tumour heterogeneity is representative and was not reduced by increasing the number of animals. Potential approaches to reduce this heterogeneity and tease out underlying trends in future experiments are discussed in Chapter 5. 56

71 7 Irradiated Tumours VVD Time Point (Days) Irradiated Tumour 1 Irradiated Tumour 2 Irradiated Tumour 3 Figure 34: Absolute VVD of the three representative irradiated tumours from day -1 to day 7 demonstrating the large heterogeneity in longitudinal radioresponse between mice. Combining these three sets of data would result in the loss of valuable information about individual response, particularly IT2 which experienced dramatic radiation-induced vascular ablation. From Figure 34 it can be seen that the absolute value VVD of IT1 on Day -1 was between those of IT2 and IT3, despite the fact that IT1 had a large avascular region and IT2 and IT3 were well vascularized. Computing densities over the entire tumour region can cause averaging effects in heterogeneous tumours. The following analysis investigates using geometryspecific ROIs to investigate the differential response between the tumour core and tumour periphery, and is demonstrated on IT1. Tumour Core vs. Tumour Periphery It is evident from the images above that there is vascular heterogeneity within tumours (intra-tumour heterogeneity) as well as between them (inter-tumour heterogeneity). There is some evidence, both clinical and preclinical, to suggest that tumour vasculature is often heterogeneous between tumour centre and tumour rim, and that the subsequent vascular response is dependent on this spatial heterogeneity [70, 71]. To investigate this, the vascular response of the centre 1.5 mm 2 of IT1 (which was largely avascular) was compared to the surrounding 57

72 tumour periphery (Figure 35). The vascular density of the centre of the tumour changed very little in response to radiation therapy, due to the persistence of the avascular region; however the vascular density of the periphery did decrease significantly. Tumour Centre vs. Periphery VVD 7 Periphery 6 Centre Time Point (Days) Figure 35: IT1 tumour centre vs. periphery VVD. When the avascular tumour core and the vascularized tumour periphery were analyzed separately, it could be seen that the VVD of central region of the tumour (which contained a large avascular region) changed very little following irradiation; however the VVD of the periphery decreased quite significantly due to vascular remodeling. By separating the analyzed regions into smaller, biologically relevant ROIs (in this case tumour centre and periphery), the spatially heterogeneous vascular response within the tumour becomes more obvious. This approach may be useful in future work to extract statistically significant trends. 5.2 NORMAL VASCULATURE Surprisingly, vascular heterogeneity prior to irradiation was also observed in the normal DSWC (Figure 36). Normal vasculature is generally assumed to be well organized and hierarchal, as in Figure 36 a; however the vessels in images b and c do not follow the expected normal vascular pattern. The raises questions about the biological characteristics of the vascular network in the DSWC, and its relevance to bulk tissue biology. 58

73 (a) (b) (c) Figure 36: Three examples of non-irradiated normal window chamber vascular networks. Scale bars 1 mm. The vascular network in (a) appears well organized and hierarchal, as would be expected in normal tissue. The vasculature in (b) also appears hierarchal, although the vessels appear slightly more tortuous than those in (a). The vascular network in (c) contains areas that appear less well-vascularized than others as well as some tortuous vessels. This intra- and intervascular heterogeneity in normal DSWC vasculature is unexpected, and may be a result of the chamber implantation surgery. This is a potential disadvantage of this model when compared to bulk tissue preclinical models. The average absolute volumetric densities were higher for non- irradiated normal vascular networks than for non-irradiated tumours (VVD normal = 6.1% +/- 1.1%; VVD tumour= 4.7% +/- 0.6%; n=3). This is thought to be because the normal vascular networks had better developed mesh-like capillary beds, which greatly increased their overall density when compared to the tumours which had poorly formed capillaries and avascular regions. Previous experiments using this experimental protocol have found that single fraction doses of up to 400 Gy are required to induce normal vessel ablation. Normal vessels were monitored for 2 time points prior to irradiation and for 2 days following irradiation with 30 Gy to determine normal vascular changes over this time period in the irradiated chamber. Based on the previous findings, we would expect no acute vascular response in normal vasculature at this dose level. Normal vasculature is considered to be a late-responder in radiation biology [6], and this was reflected in VVD and VLD measurements, with minimal deviation from baseline values (Figure 37). 59

74 Normalized VVD Time Point (Days) Normalized VLD Time Point (Days) Figure 37: VVD and VLD of normal irradiated vasculature. VVD and VLD stayed relatively constant 3 days prior-to and 2 days following irradiation. Based on previous experiments, this would be expected in normal vasculature in response to 30 Gy. The tissue-to-vessel distribution for normal tissue demonstrated very well vascularized tissue without variation over 5 days, including 2 days post-rt. These results correlated well with expected values. Tissue-to-Vessel Distance > % 95% 90% 85% 80% 75% Time Point (Days) Figure 38: The normal tissue in the DSWC is very well-vascularized both pre- and post- RT, with no tissue further than 100 µm from a vessel and no change in the tissue-to-vessel histogram distribution. 60

75 5.3 DISCUSSION TUMOUR VASCULATURE HETEROGENEITY This study used svoct to quantify the effects of high-dose single fraction radiation therapy on tumour microvasculature in a preclinical model. It was shown that the developed 3D svoct metrics can be used to quantify vasculature longitudinally in the DSWC; however there was significant heterogeneity both in starting vascular characteristics and subsequent radioresponse which prevented extraction of a conclusive statistical result. Representative examples to demonstrate the developed metrics and illustrate the level of inter-animal variability were given. While these types of individual (n=1) experiments are useful demonstrations of the utility of the developed metrics, the ultimate goal of this research will be to determine specific trends in vascular radioresponse. A challenge lies in teasing this information out from the vascular heterogeneity between tumours. Two potential approaches are proposed below for future work. Currently, the time of irradiation is based on time following tumour cell injection and tumour size. If the vascular response is in fact dependent on the characteristics of the vasculature at the time of irradiation, then the time of irradiation should be based on the vascular status of the tumour. The assumption that similarly vascularized tumours will respond similarly, however, may not be valid, as demonstrated by differences in the vascular responses of IT2 and IT3. This approach would also require the selection of a metric and metric value at which to irradiate, which would be non-trivial and could potentially bias the results. For example, if tumours were irradiated once they reached a VVD of a certain pre-determined value, the results could be different compared to results from an experiment where a different value of VVD, or a different metric, were used to determine time of irradiation. An alternate approach would be to increase the number of animals and retrospectively stratify tumours based on their vasculature. With this approach tumours could be stratified numerous times using different criteria (i.e. overall vascular density, tissue-to-vessel distribution, overall vascular heterogeneity) to determine what (if any) vascular characteristics predict vascular radioresponse. This approach does not require selection of a vascular criterion beforehand and therefore reduces potential bias. However, it assumes that tumours will be separable into distinct groups, which may not be the case. 61

76 BIOLOGICAL MODEL: CELL LINE AND MOUSE STRAIN CONSIDERATIONS The ME-180 cervical carcinoma cell line was chosen for these experiments for several reasons including: (i) it is a human cancer cell line, which is more clinically relevant, (ii) it has been stably transfected with a fluorescent protein for tumour localization and viability monitoring, (iii) it is known to be radiosensitive [48], (iv) it has aggressive growth which allows experiments to be conducted during the lifetime of the DSWC (~2-4 weeks). In some ME-180 tumours (~ 30-40%), while the tumour was viable and had grown to ~2-3 mm in diameter one week following implantation, the growth of the vasculature lagged behind the tumour cell growth, resulting in avascular holes within the tumour of varying sizes. The time of complete vascularization of the tumour (assessed qualitatively) was variable from within one week of tumour implantation to up to 3 weeks post-implantation. This heterogeneity in the vascularization and vascular characteristics of the tumours may affect the vascular response to radiation [72]. While cervical cancers are generally considered to neovascularize quite early [73], it is known that different tumour cell lines have different angiogenenic properties [74]. Previous experience in this lab with 9L rat gliosarcomas (treated with alternate therapies), showed more consistent vascularization properties; however this cell line has not been tested with radiation or in long-term experiments, is not a human cell line, and is not transfected with a fluorescent protein. Alternate human cell lines are currently being explored for vascular radioresponse monitoring, with the goal of reducing intra-tumour vascular heterogeneity. However, intra- and inter- tumour vascular heterogeneity is a well-documented phenomena both in animal models and clinically, and cannot be completely eliminated [68, 69]. Furthermore, the vascular radioresponse may not be dependent on the vascular network structure at the time of irradiation. NCr nude mice were chosen as the mouse strain in these experiments for several reasons: (i) ability to grow xenograft tumours due to T-cell deficiency, (ii) hairlessness, (iii) skin elasticity. Experimentally, tissue stretching and the development of edema within the DSWC sometimes posed a problem for long-term (>2 weeks) longitudinal svoct imaging. When DSWC were implanted on a different strain of hairless mice with less elastic skin (severe combined immunodeficiency (SCID) hairless outbred (SHO), Charles River) the chambers remained viable for greater than 4 weeks following surgery, and much less tissue stretching and shifting within the chamber was observed (n=2). However, extra care during and immediately 62

77 following chamber implantation was required, as the tightness of the chamber could also impede animal mobility. Additionally, mice with severe combined immunodeficiency (SCID) have increased sensitivity to radiation due to defects in the DNA repair mechanism and this may have an effect on vascular radioresponse. In the future, the use of mice with thicker and less elastic skin should be investigated for long-term longitudinal imaging, while keeping in mind that different strains of mice will have different radiobiological responses SMALL TISSUE VOLUME DOSE EFFECT In the experiments described above, the thickness of the irradiated tissue in the window chamber was ~1-2 mm and the radius of the incident field was 4 mm. It has been shown in normal tissue radiobiology experiments in pigs and mice that the iso-effective dose for orthovoltage beams in skin for field sizes smaller than ~22.5 mm is elevated [75]. The iso-effective dose is a measurement that determines the dose of a treatment that produces the same clinical effects on the target volume as the reference treatment [76]. At field sizes of diameter less than 5 mm the iso-effective dose increases by 50 Gy when compared to a 22.5 mm diameter beam [75]. It is believed that this is a result of migration of non-irradiated cells from the margins into the irradiated volume and that this effect can only occur if the irradiated volume is very small [6]. It is unclear how these normal tissue observations translate to a tumour transplanted in the skin; however there is some speculation that the lack of distinct tumour vascular response in tumours using this model may be due to similar small tissue volume dose effects in the window chamber. This concept, however, cannot explain why vascular response is seen in some DSWC tumours and not in others. 63

78 CHAPTER 6: CONCLUSIONS 6.1 SUMMARY The objective of this thesis was to develop quantitative 3D metrics for svoct images to monitor early vascular response to high dose radiation therapy. Chapter 1 discussed cancer incidence and survival rates in Canada followed by an overview of the biology and physics of radiation therapy treatment of cancer. A summary of what is currently known about the effects of radiation on the tumour vasculature and the subsequent effect on tumour response was given. Chapter 2 introduced svoct as a useful optical imaging modality for non-invasive longitudinal vascular imaging, and a description of the physical concepts behind this imaging technology was provided. Chapter 3 described the preclinical platform developed in our lab for longitudinal radiation response monitoring. This platform consists of: the dorsal skin fold window chamber model for repeatable optical imaging, a custom-designed animal immobilization mount, a stereotactic microscope for tumour viability monitoring, a small animal micro-irradiator for focal dose delivery, an in-house built svoct system for 3D microvascular imaging, and now a vascular analysis toolkit (stemming from this research). Chapter 4 began with a description of the advantages and disadvantages of svoct vascular imaging. These included the ability to visualize vessels with slow or non-existent blood flow without the use of a contrast agent; however with sensitivity to small bulk tissue motions. Some of the unique challenges in segmentation and quantification of these images were discussed, including vessel shadowing effects and bulk tissue motion artifacts. The next section of Chapter 4 described the approach to remove noise and artifacts from svoct images to facilitate accurate vascular segmentation. Next, the image-registration used to enable reproducible longitudinal quantification of vessels in the DSWC was described. This was followed by a description of the iterative thinning and pruning skeletonization algorithm that was used to extract the 3D centre-line representation of the vascular network. Finally, the extracted quantitative metrics of vascular volume density, vascular length density, average branch length, 64

79 tortuosity, fractal dimension, and tissue-to-vessel distribution were described, and their radiobiological relevance was discussed. Chapter 5 described the quantification of the longitudinal effects of ionizing radiation on tumour and normal vasculature in the DSWC using the developed 3D segmentation methods and metrics. This chapter showed representative examples of quantified tumour (irradiated and nonirradiated) and normal tissue vascular radioresponse using the methods described in Chapters 3 and 4. Statistically significant results were not possible due to intra- and inter-animal tumour heterogeneity; however potential solutions for overcoming this in future experiments were discussed. The results of this chapter showed the ability of the developed image processing pipeline to extract meaningful, biologically relevant, quantitative measurements of tumour vascular response to radiation therapy using svoct. 6.2 FUTURE WORK This thesis has demonstrated that svoct is capable of quantitative longitudinal monitoring of tumour microvascular response to radiation therapy. As discussed previously, future work will aim to reduce the effects of inter-tumour heterogeneity to elucidate statistically significant trends in vascular radioresponse using the quantification methods described here. There are opportunities to expand this work into different biological models, as well as emerging technological improvements that may increase the overall reliability of this approach. As addressed in chapter 5.3.2, work has begun to identify alternate cell lines for use in this experiment. This may reduce inter-tumour vascular structure variability and yield more consistent results. Additionally, to investigate whether the very small volume geometry of the window chamber model combined with the micro-irradiator affects the magnitude of vascular response to high dose radiation, future work should investigate the effect of increasing the field size of irradiation to include more tissue. Other window chamber models, such as the cranial and spinal cord models, have more realistic tissue and tumor geometry compared to the flat geometry of the dorsal skin chamber. The feasibility of using svoct for longitudinal vascular imaging in a chronic spinal cord window chamber was demonstrated for the first time during the course of the thesis (see 65

80 Appendix A3). Additionally, preliminary work has begun with Dr. Gelareh Zadeh (Toronto Western Hospital, Toronto) to monitor tumour vascular response to radiation therapy in a previously developed cranial window chamber model. This model offers some advantages over the DSWC: (i) a more clinically relevant 3D geometry, (ii) the window is smaller (3 mm in diameter), and (iii) the brain does not move significantly within the window between time points; therefore the challenge of image registration and ROI selection is reduced because the entire chamber can be imaged in a reasonable amount of time (~3 minutes). Challenges in working with the cranial window chamber include the pulsating movement of the brain, and cerebrospinal fluid, both of which decrease small-vessel signal and overall image contrast. Once the imaging protocol and animal restraint mechanism for this model have been optimized, the methods and metrics developed in this thesis should be applicable to cranial window chamber images with minimal modifications. Despite the extensive efforts to reduce small bulk tissue motion, including the customdesigned animal mount, optimization of the anaesthetic protocol, and development of image post-processing algorithms, bulk tissue motion continues to present a problem for robust acquisition and quantification of longitudinal svoct datasets because it can cause inconsistent image quality between time points. Recently, Lee and Mariampillai demonstrated the first svoct system capable of real time data acquisition, processing, and display [77]. On the current system used in this thesis, the only feedback during svoct image acquisition is the structural B- scan image, which is not always a good indication of the svoct image quality. Images take ~5 minutes to acquire and a further 5-10 minutes to process. Real time feedback would greatly improve the ability to acquire reproducible, high-quality images. Furthermore, the authors used real-time sub-pixel image registration to reduce the decorrelation of stationary solid structures [77]. This technique greatly reduced the effect of small bulk tissue motion noise. Implementation of these technological improvements on the existing svoct system would require several hardware and software upgrades, but has the potential to greatly improve the quality and reproducibility of svoct images. There is opportunity to expand the svoct capabilities to include functional information using decorrelation time measurements. By raising the frame rate of the system above 160 fps, decorrelation time measurements can be extracted by analyzing the temporal statistics of subsets of the data with different time lags (for example measure the speckle variance between every 66

81 other, 4 th or 8 th B-scan to approximate at what scan rate the pixel decorrelates). The decorrelation time in vasculature will be related to blood flow and viscosity. Significant phantom work would be required to establish the true biological meaning of the decorrelation time (i.e. its relationship to flow and viscosity among other unknown parameters such as blood glucose concentration). This is a potential application of svoct that may provide unique functional information about 3D in vivo vascular function in addition to the existing vascular tree architecture information. 67

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88 APPENDICES A1 QUANTIFYING TISSUE MICROVASCULATURE WITH SPECKLE VARIANCE OPTICAL COHERENCE TOMOGRAPHY The results of an earlier implementation of this approach comparing normal and tumour vasculature were published in Optics Letters. The image analysis methods presented in this thesis were built from the techniques discussed in this publication: 74

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