Quantification of Flow Parameters in Complex

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QUANTIFICATION OF FLOW PARAMETERS IN COMPLEX

VASCULATURE FLOW PHANTOMS USING CONTRAST-ENHANCED

ULTRASOUND METHOD

ASAWARI PAWAR

Bachelors of Technology in Biomedical Engineering

Padmashree Dr. D. Y. Patil University

June, 2012

Submitted in partial fulfillment of requirements for the degree of

MASTER OF SCIENCE IN BIOMEDICAL ENGINEERING

at the

CLEVELAND STATE UNIVERSITY

August, 2015 We hereby approve this thesis of

Asawari Pawar

Candidate for the Master of Science in Biomedical Engineering degree for the

Department of Chemical and Biomedical Engineering

and the CLEVELAND STATE UNIVERSITY

College of Graduate Studies

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Thesis Chairperson, Dr. Gregory T. Clement

______

Department & Date

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Thesis Committee Member, Dr. Joanne M. Belovich

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Department & Date

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Thesis Committee Member, Dr. Chandra Kothapalli

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Department & Date

Student’s Date of Defense: 8/4/2015

This thesis is dedicated to my parents Asha Pawar & Prakash Pawar, my sister

Amita Pawar and my best friend Sidharth Oberoi

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ACKNOWLEDGEMENT

First and foremost, I would like to thank God the almighty for providing me this opportunity and granting me the capability to do this thesis project. I owe a debt of gratitude to my thesis advisor and principal investigator at Cleveland Clinic,

Dr. Gregory Clement, he was the first to welcome me into the clinic and encourage my participation in clinical ultrasound lab. I am grateful for his help laying out a fascinating project that I am proud to have been a part. But most of all, I will remember his unconditional and genuine support and quick wit. I would like to offer my sincere thanks to my thesis committee members, Dr. Kothapalli and Dr. Belovich for always being encouraging throughout my study and research. In addition, I express my appreciation to Dr. Kothapalli and Dr. Belovich for giving me valuable suggestions, helpful ideas and constant support. Also I would like to thank the clinic team members for all their help especially; Mark Howell for his untiring support, without him this thesis would not be possible. I would like to thank my family and my sister for all the trust and support they showed me.

Last but not the least; I would like to thank Sidharth Oberoi, the most reliable friend and a beautiful soul one could have, who I absolutely adore. I would have never learned the lessons of life and grown into a stronger person had I never made the decision to be with you. He played a crucial role in my evolution as a person today I am. Thank you for standing tall by me, appreciating me in whatever I did and choosing me to be a part of your life . I will be grateful to him forever.

QUANTIFICATION OF FLOW PARAMETERS IN COMPLEX

VASCULATURE FLOW PHANTOMS USING CONTRAST-ENHANCED

ULTRASOUND METHOD

ASAWARI PAWAR

ABSTRACT

Currently, there are few simple-to-construct in vitro, wall-less phantoms that have accurate acoustic properties while mimicking the complex normal and neoplastic geometries of the vascular network. The purpose of this study was to develop agar-based tissue-mimicking phantoms (TMP) to model such networks and to quantify the mean intensity and the slope measurements of the ultrasound flow. Three types of vascular networks were developed; (1) single vessel, (2) multi-vessel with artery bifurcations and

(3) multi-vessel with artery bifurcations and structural abnormalities typical of diseased

(tumor) vascular networks. Bolus injections of ultrasound contrast agents (UCAs) were performed under varying flow conditions relevant to our ongoing work in developing techniques to simultaneously quantify both the total volume and flow measurements within a tumor phantom

The correlation coefficient between the mean slope measurements and the least squares fitted line was all higher than 0.95, indicating a good linear relationship between the mean slope and the flow-rates. For all single-vessel flow phantom data, the mean proportional difference of the slope measurements and flow-rate was 0.695 ± 0.18 (mean

± SD). It was found that the correlation coefficient between the mean slope

v measurements and the least squares fitted line is 0.90, indicating a good linear correlation between the single-vessel and Multi-vessel flow phantoms. However, the parameters obtained from tumor region 1, 2 and 3 did not show any significant flow pattern and correlation after contrast injection of UCAs.

Finally, the slope measurements of intensity at the tumor regions in-flow and out-flow demonstrated the nonlinear and undefined relationship between the measured intensity and varied flow-rates. Ideally, most information could be obtained when intensities at both the input and the output of the tumor periphery are measured. For further studies, alternative modeling of the problem is required because in physiology, microvasculature flow system has multiple input vessels and multiple output vessels. Developing new time-intensity-based techniques is the focus of future research.

TABLE OF CONTENTS

ABSTRACT……………………………..……………………………………………………..……..……………....v

List of Tables……………………………………………...……..…………..………………………………...…..xi

List of Figures……………….……….…………………………………...……………………………...……..xiii

CHAPTER

I. Introduction………………………………….………………...…………………….…………….....1

II. Background………………………………………………………………………………………..….6

2.1 The Circulatory System……………………………………………………………...6

2.1.1 General Description……………………………………………………..6

2.1.2 Normal Vasculature……………………………………………………..8

2.2 Diseased Microcirculation………………………………………………………….9

2.2.1 General Description……………………………………………………..9

2.2.2 Angiogenesis………………………………………………………….…..10

2.2.3 Tumor vascular……………………………………………………….…11

2.3 Techniques to measure Microcirculation Blood Flow………………..12

2.4 Principles of Ultrasound Imaging……………………………………………..13

2.4.1 DCE-US - Contrast Enhanced Ultrasound……………………..17

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2.4.2 DCE CT - Dynamic Contrast Enhanced Computed

Tomography………………………………...... 19

2.5 Contrast agents…………………………………….…………………………………20

2.5.1 Microbubble Material Preparation……………………………...23

2.5.2 Detection of Microbubbles UCAs…………………………………25

2.5.2.1. Non-linear behavior of Microbubbles…25

2.5.2.2. Relevant Studies…………………………………27

2.5.2.3. Principles for Evaluating the

Microvasculature with Contrast

Agents……………………………………………..…28

2.5.2.4. Evaluating the Microvasculature with

Contrast Agents…………………………………………………….....29

2.6 Flow Phantoms models……………………………………………………………31

2.6.1 Single-vessel flow phantom model…………………………...35

2.6.2. Multi-vessel Flow Model………………………………………….37

2.6.3. Tumor Vessel Flow Model………………………………………37

III. MATERIALS & METHODS……………………………………………………………………42

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3.1 Preparation of Ultrasound Contrast Agents (UCAs)

Microbubbles………………………………………………………………………………..42

3.1.1. Selection of Internal Gas: Octafluoropropane………...... 42

3.1.2. Selection of Outer Lipid Shell………...……………………….…..43

3.1.3. Preparation of Stock Solution………………………………...…45

3.1.4. Observation of Microbubbles under Microscope………49

3.1.5. Analysis of Microbubbles using Invitrogen Counting

Chamber………………………………………………..………………..49

3.2 Preparation of Tissue-mimicking vascular flow phantoms……..….50

3.2.1 Single vessel flow Phantom………………………………………...50

3.2.2 Multi-vessel Flow phantom……………………………………...... 56

3.2.3 Tumor-vasculature flow phantom………………………………60

IV. RESULTS & DISCUSSION……………………………………………………………………..68

4.1 RESULTS…………………………………………………………………………………………..68

4.1.1. Ultrasound Contrast agents………………………….………………….68

4.1.2. Detection by Ultrasound………………………………………….71

4.1.3. Tissue-mimicking vascular flow phantom

experiments…………………………………………………………….73

4.1.3.1. Single Vessel Flow

Phantom…………………………………………….77

4.1.3.2. Multi-Vessel Flow Phantom………….……..79

4.1.3.3. Slopes vs. Flow-rate for Single vessel and

multi-vessel flow phantoms………………86

4.1.4. Measurement of Intensity (Maximum Intensity, Imax) at

Different flow-rat…...... 89

4.1.5. Ultrasound based velocity measurements…………..…….89

4.1.6. Tumor vasculature flow phantoms……………………….…..92

4.1.7. Computed Tomography (CT): Comparative study of

Vessel diameter measurement…………………………………97

4.1.7.1. Data analysis…………………………………...…97

4.2. Discussion…………………………………………………………………………….103

V. Conclusions & Recommendations……………………………………………………….107

5.1 Conclusions………………………………………………………………..…………107

5.1Recommendations…………..…………………..…………………………………119

BIBLIOGRAPHY…………………………………………………………………….………………………….111

APPENDIX………………………………………………………………………………………………………..134

LIST OF TABLES

Table Page

I. The B-mode Ultrasound imaging parameters……….…………………………………...55

II. The B-mode ultrasound imaging-experimental parameters for single-vessel

and multi-vessel phantom………………………………………………………………………..56

III. Summary of the derived slopes of the time-intensity curves with different

flow-rates and imaging planes within a single-vessel flow phantom………….78

IV. Summary of the derived slopes of the time-intensity curves with different

flow-rates and imaging planes within a single-vessel flow phantom…………..80

V. The maximum intensity vs. Flow-rates in single vessel flow Phantom………..83

VI. The maximum intensity vs. Flow-rates in a multi-vessel flow

Phantom…………………………………………………………………………………………………85

VII. The summary of the ultrasound based velocity measurements derived from

the distance between the two ROIs and the time difference (T2-T1) between

the times of delay of initial time of enhancement on the time-intensity curves

with different in a longitudinal view within a single-vessel flow

phantom...... 87

VIII. Summary of Results obtained from the experimental parameters as discussed

in table 5 for tumor region 1………………………………………….…………………………92

IX. Summary of Results obtained from the experimental parameters as discussed

in table 5 for tumor region

2……………………………………………………………………………………………………………..92

X. Summary of Results obtained from the experimental parameters as discussed

in table 5 for tumor region 3…………………………………………………………………….96

XI. Summary of Minimum and Maximum Diameter Measurements with

Difference in Absolute and Percentage Diameter Change between Optical and

CT Measurements……………………………………………...... 98

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LIST OF FIGURES

Figure Page

1. The organizational structure of the microcirculation thought of as a tree-type

network of arterioles that leads into an anastomosing network of

capillaries………………………………………………………………………………………………….7

2. Ultrasound imaging as a diagnostic imaging tool for visualizing and assessing

physiological structures with real time images………………………………………....13

3. The natural characteristics of piezoelectric making the reflected ultrasound

energy that returns to the transducer to reconvert back into an electrical

signal which is processed and displayed as an image on the screen.…………14

4. Ultrasonic beam resolution………………………………………………………………………16

5. Cartoon figure showing structure of a typical microbubble with different

shell compositions…………………………………………………………………………………...20

6. Shows different ultrasound contrast agents and their distribution relative to

the vascular endothelial cells………………………………………………….………………..22

7. Physical mechanisms underlying the biological effects induced when

microbubbles are excited by ultrasound energy……………………………………...... 26

8. Illustrating Flow in Tissue-mimicking Single-Vessel Flow phantom………...…31

9. A system with a single inflow and a single outflow orifice………………………....32

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10. Illustration of Flow in a Tissue-mimicking Multi-vessel flow phantom...... 36

11. Illustration of Flow in Tissue-mimicking Tumor vasculature flow

phantom………………………………………………………………………………………………….38

12. Structural formula of Octafluoropropane……………………………………………….....43

13. Structural formula of DPPC………………………………………………………………………44

14. Structural formula of DPPE………………………………………………………………………44

15. Colored molecules are DPPC head group (shown in blue), DPPE head group

(shown in green), and lipid tails (shown in gray), and water (shown in

pink)……………………………………………………………………………………………………….44

16. Preparation of Stock solution for contrast agents Microbubbles…………...... 46

17. Customized technique to replace air with vacuum. Hose setting connected to

a needle which was injected into the glass bottle to replace air with

vacuum…………………………………………………………………………………………………...47

18. Customized setting of Octafluoropropane gas cylinder outlet to inject of

Octafluoropropane gas into glass bottle through hypodermic needle…….….48

19. Preparation of contrast Agent’s microbubbles: Stock solution in the glass

bottle on the left side was amalgamated for 30 s to get gas-filled lipid shell

microbubble contrast agents under the milky-white foamy layer in the glass

bottle on the right side………………………………………………..…………………………...51

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20. Flow Phantom acrylic box construction of size 10 cm × 7 cm × 4 cm………….53

21. Process of preparation of Single vessel flow phantom………………..……….….….54

22. Single Vessel Flow Phantom Experimental Setup…………………………………..….57

23. The wires folded into arch shape to mimic the shape of the typical capillary

bed. Each wire is of diameter 0.6438 mm…………………………………………….…...59

24. Multi-Vessel Flow Phantom Experimental Setup……………………………….….…..63

25. The foam was modified to a shape as shown in the figure by cutting in a

circular shape of approximate diameter of 10 mm………………………….…………64

26. Tumor vessel flow phantom in a 3D solid work design: The upper phantom is

the side view and lower phantom is the top view of the tumor vessel flow

phantom in a 3D solid work design showing consolidated wires and tumors

embedded in agar TMM to simulate the morphological abnormalities

relevant to tumor angiogenesis which mimic the onset of disease. The wires

are then retracted and tumors are left with wall-less vessels embedded in the

agar tissue……………………………………………………………………………………………....65

27. Tumor vasculature Flow phantom experimental set up………………………….…69

28. Two-dimensional microscopic photo of the UCAs. The shape of the UCAs was

observed under optical microscope at 40X magnification………………………….70

29. Size distributions of UCAs. The size distribution of UCAs was measured with

a cell counter and plotted against number of microbubbles……………………….70

30. Study of the microbubble size on a cell counter……………………….…………..……72

31. The characteristic diffusion of UCA microbubbles by ultrasound in degassed

water………………………………..……………………………………………………………….……74

32 (a) ROIs drawn from still B-mode image from single-vessel flow

phantom………………………………………………………………………………………………….75

32 (b) Time intensity curve extracted from ROIs……………………………………………….77

33. Calculation of slope in the region of initial time of enhancement

(ET)……………………………………………………………………………………………...……...…79

34. The slopes associated with the single-vessel phantom in ROIs are plotted as a

function of the flow rate……………….…...... 81

35. The slopes associated with the multi-vessel phantom in ROIs are plotted as a

function of the flow rate, error bar with the standard error…………………....…82

36. Slopes vs. Flow-rate for Single vessel and multi-vessel flow

phantoms………………………………………………………………………………………………..84

37. Maximum intensity vs. Flow-rates in single vessel Phantom………………..…….86

38. Maximum intensity vs. Flow-rates in multi-vessel Phantom…………….……..….88

39. Ultrasound based Velocity measurements vs. Flow-rate……………………..……..89

40. Representation when performed in conjunction with a mammogram, the

SonoCiné ultrasound can reveal cancers that may otherwise be missed……..93

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41. Up-slope values of in-flow vs. flow-rate in tumor region 1…………………...……93

42. Up-slope values of out-flow vs. flow-rate in tumor region 1……………….……...94

43. Up-slope values of in-flow and out-flow vs. flow-rate in tumor region

1………………………………………………………………………………………………….………….95

44. CT image of Single-vessel diameter measurement in phantom 1……….…….…99

45. CT image of Single-vessel diameter measurement in phantom 2…………….….99

46. CT image of multi-vessel diameter measurement in phantom 1………….…...100

47. CT image of multi-vessel diameter measurement in phantom 2……………....101

48. CT image of tumor-vessel diameter measurement in tumor-vessel

phantom………………………………………………………………………………………………..101

49. US image of Single-vessel diameter measurement in phantom 1 and 2….....102

50. US image of Multi-vessel diameter measurement in phantom 1 and

2……………………………………………………………………………………………………..…….102

51. US image of tumor-vessel diameter measurement in tumor-vessel

phantom……………………………………………………………………………………………….103

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CHAPTER I

INTRODUCTION

Quantitative assessment of vascular blood flow has been a topic of significant interest for research for many years [1–5]. Blood flow is a physiological parameter of clinical importance because it reflects the adaptive response of organs. This response is to either their normal biological environment or to disease, trauma, and the malignant progression of cancer [6-7]. Disruption of flow outside the normal range can pose serious consequences for local tissue and is therefore an indicator of a number of diseases, including cancer and diabetes [5, 8 and 9], ischemic heart disease, stroke and cancer. Typical clinical techniques aimed at monitoring flow are Doppler ultrasound [10, 11], laser speckle [12–14], photoacoustic tomography [15] laser Doppler [16, 17], autocorrelation methods [18], and

Doppler optical coherence tomography [4, 19]. Contrast enhanced ultrasound offers a unique method to quantify the blood to measure the flow, perfusion, vascular

1 volume and morphology of micro vascular networks. The technique relies on the time intensity method; after the injection of the indicators, their concentration is monitored as a function of time, generating a time intensity curve (TIC) from which a series of quantitative flow parameters is extracted and analyzed from this time intensity curves [20, 21]. This is also known as dye-dilution theory. Calibration and validation of these methodologies and techniques have been conducted primarily with simple phantoms mimicking a single blood vessel or vascular bed [13, 16, and

23].

Generally, they comprise i) one or three silicon intertwined tubes immersed in a custom made water tank that mimics the biological environment surrounding the vasculature of interest or ii) dialysis cartridge containing with capillaries dimension to that of one type of vessel found in the micro vascularization (arteriole diameter <

300 μm) [22]. The intertwined silicon tubes or the dialysis cartridge was connected to a peristaltic pump or other mechanical pumping system so as to accurately control the flow rate in the artificial vascular structure. In these studies techniques were based on pumping a fluid through a phantom placed in a water tank. Results showed that several parameters derived from the time-intensity curve had a good correlation with the flow rate under certain conditions [23].

In vitro studies done by other research groups were also based on a volumetric tumor assessment model [24]. There have been many studies recently published which implement ultrasound imaging techniques for monitoring disease state by way of blood flow characterization. Some of these strategies seek to

2 quantify the amount of vascularization within the tissue by using Doppler techniques [25-28]. Other strategies attempt to extract information about the rates at which blood flows through the tissue using contrast-based destruction- reperfusion (or “flash replenishment”) techniques [29-34]. Additionally, others have attempted to extract quantifiable metrics for vessel and vessel network structure as a means to understand disease progression [33, 35-37]. Vessel tortuosity, which is a morphological abnormality exhibited by cancerous vasculature in both humans

[38], and has been used as a marker for disease state and therapeutic response in clinical studies based on image-derived data [39, 40]. The abnormal pathophysiology and micro vascular structure gives rise to temporal and spatial variations in ultrasound signal enhancement that differs from normal surrounding tissue [41], which can be used to provide information on tumor characteristics [42].

One of the major shortcomings of these studies is the lack of a robust, reliable, easily reproducible and stable phantom for quantification and evaluation of blood flow in complex vasculatures. The vascular flow phantoms for ultrasound imaging are needed to [43]: (i) be compatible with the imaging modalities evaluated, i.e., it is important that the materials involved in the construction of phantoms allow clearly the identification of the shape of the vessel lumen on the images, with no or minimum artifacts. (ii) Be anthropomorphic, i.e., their geometry should mimic as close as possible the complexity of real human vessels [44]. (iii) The individual materials used in their construction should have similar acoustic and mechanical properties to soft tissue, vessel tissue and blood in order to avoid artifacts and allow for meaning measurement of blood flow [44] (iv) In case of constructing a tumor

3 vascular phantom the techniques used to split the flow should be such that the fluid flows slower at the downstream. However, the above studies did not focus on developing phantoms with vascular abnormalities combined with flow simulation for quantification of flow parameters, which would otherwise be challenging to construct. On the pathway to developing vascular phantoms, numerous materials have been reported to mimic the vessel wall in these flow phantoms including: glass, plastic, Teflon, latex, C-flex, heat shrink and neoprene [45] with suitable properties for use due to their availability in tube form in ultrasound phantoms. However these materials tend to produce significant distortion of ultrasound due to its high attenuation. Additionally, these materials are only commercially available with fixed dimensions and therefore they are not suitable for use in mimicking the complex vessel shapes such as curvatures, bifurcations, blockages, etc. which are necessary in anatomically realistic flow phantoms. To overcome these problems some researchers have used excised human vessels removed during either autopsy or endarterectomy, but this method is not ideal due to both lack of availability, ethical issues, and differences in acoustic properties between living and dead tissues. Some have attempted to mimic large vessels [46], such as the carotid artery [47]. Other studies have sought to create a vascular phantom using real vessels harvested from cadavers [48, 49]. Some of the major limitations of these designs are the size of the vessels and the absence of a leaky state, where small contrast agents may traverse between the mimicking vascular and tissue types. The ideal solution lies in the development of phantoms with no vessel walls, known as ‘wall-less’ vascular flow phantoms in which vessels are constructed by molding different vessel shapes

4 within a tissue mimic substance known as agar [50]. In this case, the blood mimicking fluid is in direct contact with the TMM. Wall-less flow phantoms resolve the problems of vessel wall attenuation and impedance mismatches, and thus also possible to develop variety of vessel geometries such as curves, bifurcations, trifurcations, tumors, etc. Our simple fabrication procedure allows us to tailor three different types of vascular phantoms with acoustic properties of the vascular material to values relevant for studies of biological tissue, which would provide a system through which blood-mimicking fluid can be pumped to provide a realistic reproduction of flow conditions to be able to quantify the blood flow in complex vasculature.

The purpose of this study is to design and fabricate a fast prototyping of phantoms simulating a superficial vasculature network, which would provide a system through which blood-mimicking fluid can be pumped to provide a realistic reproduction of flow conditions in the relevant vascular territories. The second aim was to quantify the flow through these phantoms to determine the time-intensity curves to calculate the slope, intensity and the velocity of the flow. The third aim was to further investigate the phantoms in conjunction with radiological imaging modalities, such as Computed Tomography (CT) to determine the relative change in diameters comparative to ultrasound (US) measurements.

CHAPTER II

BACKGROUND

2.1 The Circulatory System

2.1.1 General Description

The circulatory system consists of three independent systems that work together: the heart (cardiovascular), lungs (pulmonary), and arteries, veins, coronary and portal vessels (systemic). The microcirculation is responsible for the distribution of blood within tissues, and describes the blood flow throughout the microvasculature, which is comprised of arterioles, capillaries, and venules. Healthy blood flow is responsible for sustaining a healthy environment for the individual cells of all organs and tissues (Fig 1). The primary function of the microcirculation is

6 the delivery of nutrients and oxygen and the removal of waste products and carbon dioxide [51-53]. Other functions include the regulation of blood pressure and body temperature [54]. Blood transports oxygen, carbon dioxide, nutrients, waste, heat, hormones, and agents of the immune system. The system is responsible for the flow of blood, nutrients, oxygen and other gases, and as well as hormones to and from cells. An average adult has 5 to 6 quarts (4.7 to 5.6 liters) of blood [55], which is made up of plasma, red blood cells, white blood cells and platelets. The system of blood vessels in the human body measure about 60,000 miles (96,560 kilometers)

[55].

Figure 1: Organizational structure of the microcirculation thought of as a tree-type network of arterioles that leads into an anastomosing network of capillaries [55]

2.1.2 Normal Vasculature

Blood flows throughout the body tissues in blood vessels. An extraordinary degree of branching of blood vessels exists within the human body. There are three major types of blood vessels: arteries, capillaries and veins. The microcirculation begins when small arteries of around 100 μm in diameter begin to branch into smaller arterioles, which have diameters of around 20-30 μm [52, 53].

Blood that passes through the arterioles will enter the capillaries (7-10 μm in diameter), where the majority of nutrient and gas exchange takes place. There is considerable branching taking place on the capillary level which serves to supply every tissue cell in the body. This is evidenced by the fact that all tissue cells are located within 60-80 μm of a capillary [52]. The blood is collected from capillary beds by venules, which may vary in size and character, but on average have an internal diameter roughly around 20 μm [53]. The organizational structure of the microcirculation may be thought of as a tree-type network of arterioles that leads into an anastomosing network of capillaries. This capillary bed is made up of a dense network of parallel-running vessel branches that eventually leads back into a network of venules of similar structure to that of arterioles [56].

2.2 Diseased Microcirculation

2.2.1 General Description

The early manifestations of diseases can be encountered in microcirculation.

The failure to cope with major changes in circulatory hemodynamics, the presence of abnormal blood components, a change in the control of micro vascular resistance, or abnormalities in the endothelial barrier function can all lead to microcirculatory diseases. Some of the diseases that damage the microcirculation itself or the blood includes infection, diabetes, hypertension, ischemia, sickle cell disease, etc. Some diseases lead to structural damage of essential components of the microcirculation such as hypertension and abnormal angiogenesis as in the case of cancer. Cancer is the second largest killer in the industrialized world, after cardiovascular disease, and is responsible for about 25% of all deaths in North America [57].

Consequently, qualitative and quantitative examination of in-vivo microcirculation and microvasculature in both normal and diseased tissues has been the subject of intense research in both clinical and laboratory settings [58].

The microcirculation plays an important role in cancer development and progression [42], and examining tumor microvasculature may provide diagnostic and prognostic information regarding the disease.

2.2.2 Angiogenesis

Angiogenesis, by definition, is the formation of new capillary blood vessels from pre-existing micro vessels [59]. Thus, in order for tumors to grow rapidly, angiogenesis must be initiated [60]. Tumors begin when a group of cells display uncontrolled cell growth [61]. They are described as benign when the growth is relatively slow and constrained to a specific location. Malignant tumors, on the other hand, grow much more rapidly and may undergo a process called metastasis [59,

60], whereby the tumor cells may disperse to another tissue or organ and establish secondary tumors. The term cancer generally refers to malignant tumors [52]. A tumor will begin supporting itself through avascular means, meaning the diffusion of nutrients and oxygen will support its growth. This continues until the tumor reaches a certain size, roughly 1 mm3 [62]. Any growth of the tumor after it has reached its maximum size through avascular means would require the recruitment of blood vessels to deliver the necessary nutrients and oxygen [63]. As cell growth continues, the microenvironment surrounding the tumor becomes critically hypoxic due to an exhaustion of local oxygen supply. This ‘stressed state’ stimulates a cascade of biological events that eventually form a dedicated tumor microvasculature (Fig. 1.2) [62]. In contrast to the self-limiting processes that occur during normal angiogenesis, tumor angiogenesis is wildly uncontrolled. Once a small avascular mass recruits a dedicated blood supply, a positive feedback loop is created, driven by progressive tumor growth and a chronic state of hypoxia and acidosis [64, 65]. This neovascularization is a process described as angiogenesis,

10 and has been identified as an essential step for tumor growth [63]. The tumor vasculature that arises, however, is much different than normal vasculature.

2.2.3 Tumor vascular

The rapid development of tumor vasculature by abnormal and poorly controlled angiogenesis results in vessel walls with pores ranging in size from 200 nm to 2 µm, with an average pore size reported to be approximately 400 nm [60,

163]. The leaky tumor vasculature, owing to the presence of these pores, allows macromolecules and nanoparticles to preferentially extravasate into the tumor in contrast to healthy blood vessels. Tumor vasculature is highly heterogeneous, where the vessels are irregularly constricted or dilated, can have uneven diameters, and excessive branching [66]. Tumor vascular networks consist of a disorderly tangle of tortuous and leakier than normal vessels with random orientation. Tumor vascular networks display loops, shunts, blind pouches, trifurcations, large avascular areas and lack of hierarchy (Figure 1.3a) [66-70, 71, and 72]. Tumor vascular networks differ significantly from normal tissue. Whereas normal vascular networks are well ordered with a hierarchical vessel arrangement, tumor vascular networks can be described as tangle of vessels [73] (Figure 1.2). They also show absence of differentiated arteries and veins. The molecular mechanisms that cause abnormal vascular characteristics are not well understood, but imbalance of pro- and anti-angiogenic factors is considered to be a key contributor [74]. Alongside the morphological abnormalities of an individual tumor, the distribution of regional

11 blood flow is equally heterogeneous in space and irregular in time, often demonstrating abnormal oscillations and even reverse flow patterns.

3.3 Techniques to measure Microcirculation Blood Flow

Modern computing power and recent advances in the field and technology of molecular genetics has led to an explosion of potential therapeutic targets and drugs for the early detection of cancer and its treatment. Generally, meaningful changes in tumor size are delayed up to 6 to 8 weeks after the initiation of therapy. An improved approach is needed to assess the behavior and response of a cancer vasculature at an earlier stage of treatment. Compared to normal tissue, many tumors have high micro vessel densities, large blood volumes, and unique and heterogeneous perfusion characteristics as discussed earlier. In an early trial of the anti-angiogenic drug bevacizumab for human colorectal cancer, the treatment was shown to decrease colorectal tumor blood perfusion, blood volume, and blood flow and tumor interstitial fluid pressure [31]. Currently there exist a number of clinical imaging technologies that can be used to assess features of the microvasculature as discussed below.

The basic principles of all medical imaging modalities involves the interaction of some form of energy (magnetic, radiation, optical, acoustic) with tissue and cellular structures within the body; whether it be magnetic resonance imaging (MRI), computed x-ray tomography (CT), positron emission tomography

(PET), optical coherence tomography (OCT), or ultrasound (US).

2.4 PRINCIPLES OF ULTRASOUND IMAGING

Ultrasound imaging is a diagnostic imaging tool for visualizing and assessing physiological structures with real time images. Ultrasound waves begin with the transducer, typically a piezoelectric material, where an electrical pulse is converted to an ultrasound pulse and vice versa [75]. The ultrasound pulse is comprised of a modulated sinusoidal carrier signal, typically at the resonant frequency of the transducer. As the ultrasound pulse travels through tissue, interactions with boundaries between tissues of different acoustic impedance will cause some of the energy of the pulse to reflect back towards the transducer.

Figure 2: Ultrasound imaging as a diagnostic imaging tool for visualizing and assessing physiological structures with real time images [75]

As shown in schematic diagram figure 3, due to the natural characteristics of piezoelectric, the reflected ultrasound energy that returns to the transducer is reconverted back into an electrical signal, which is processed and displayed as an image on the screen. The conversion of sound to electrical energy is called the piezoelectric effect. The image can be displayed in a number of modes: 1) amplitude

(A) 2) brightness (B) 3) motion (M).

Depending on the speed at which sound travels through the tissue, the returning echo will arrive at the transducer after some time delay (td), [41] which is proportional to the depth of the scattering boundary:

Figure 3: the natural characteristics of piezoelectric making the reflected ultrasound energy that returns to the transducer to reconvert back into an electrical signal which is processed and displayed as an image on the screen [41]

Ultrasound travels through a medium as a longitudinal vibration of the medium's particles, where the particles of the medium can be envisioned as connected by springs, effectively making the medium elastic. In a linear approximation, the propagation speed of the ultrasound wave (co) will be dictated by the medium properties, and is determined by

√

where, β is the bulk modulus and ρ is the density. The speed of sound in water is approximately 1485 m/s, and ranges between 1450 m/s to 1600 m/s depending on the tissue type. The ultrasound wavelength (λ) is then determined by the speed of sound (co) and particular frequency (f)

휆 = 𝑜/𝑓 ∙

The number of wavelengths in an ultrasound pulse dictates spatial pulse length. An ultrasound imaging pulse is typically two or three cycles long [76] and shaded by a Gaussian envelope. The ability to resolve fine details and objects is governed by the spatial resolution of our imaging system (Figure 4). The axial resolution of an ultrasound system refers to its ability to distinguish between two closely spaced objects in the beam direction or axis [76]. In order to achieve good axial resolution, the returning echoes from distinct reflectors should not overlap.

Thus, since the distance traveled between two reflectors by a pulse is twice the distance between those reflectors, the axial resolution (AR) is

where, H is number of cycles in the pulse x wavelength. The lateral resolution describes the ultrasound systems ability to distinguish between two closely spaced objects perpendicular to the beam direction, or orthogonal to the axial direction, in the imaging plane [76]. This lateral resolution is determined by the imaging wavelength, such that higher frequencies yield higher lateral resolution. The elevation resolution, or the slice thickness of the ultrasound beam, is dependent on the height or focusing of the transducer elements or orthogonal to the axial direction, out of the imaging plane.

Figure 4: Ultrasonic beam resolution [76]

An ultrasound pulse may experience many interactions due to the acoustic properties of tissue. The energy of the ultrasound beam may be scattered or absorbed. Objects within tissue that are roughly the same or smaller in size to the wavelength, and scatter ultrasound in all directions cause acoustic scattering. This type of scattering is termed non-specular reflection. Specular reflection, on the other hand, occurs at the smooth boundaries of regularly shaped objects and reflects ultrasound according to Snell’s Law [41].

2.4.1 Dynamic Contrast Enhanced Ultrasound (DCE-US)

In a typical single capillary, the flow velocity is about 0.3 mm/s, the vessel diameter about 5 μm, and the flow rate less than 1 ml per year [77]. These extreme characteristics make imaging and quantifying the microcirculation a significant challenge. Directly visualizing the microvasculature in situ is impossible, given the resolution limits of clinical imaging. Most conventional ultrasound methods are not sensitive to vessels smaller than 100 μm [78]. Dynamic contrast enhanced ultrasound (DCE-US) is an ultrasound-based method that relies on micron-sized bubbles of contrast agents to improve the backscatter of blood. Pharmacokinetic models are therefore used to relate the behavior of the contrast agent with physiological properties. DCE-US has enabled an assessment of micro vascular structures and hemodynamic characteristics.

Microbubbles, typically around 1-10 μm in diameter [79], are composed of a gas core surrounded by a shell layer. Shells can be composed of albumin, lipid, or polymer layers encompassing a gas such as nitrogen or a perfluorocarbon. The properties and materials of ultrasound contrast agents and their application are discussed in further detail in the following section. The high impedance difference due to the liquid-gas surface that separates the microbubble from blood causes a strong backscattered echo compared to red blood cells, allowing increased detection from blood. Contrast-enhanced ultrasound imaging, usually done at low acoustic power in order to minimize microbubble disruption, may employ another technique known as a ‘harmonic imaging mode’, whereby the fundamental frequency is blocked using a frequency filter set to retain the backscattered harmonic echoes generated by resonant modes of the microbubbles. Other techniques, referred to as multi-pulse techniques, have been introduced in a ‘pulse-inversion mode’ [79], which involves multiple pulses of different amplitude and phase. The combination of the echo responses due to these pulses results in the suppression of linear signals while retaining odd harmonics of the nonlinear signals. An example of such a technique is pulse-inversion [80]. Thus, a dynamic CE-US imaging exam allows assessment of blood flow on a microscopic and macroscopic level. The recorded data allows qualitative and quantitative assessments to be made by analyzing the temporal kinetics of the signal enhancement due to the microbubbles contrast agents [81-84].

2.4.2 DCE CT - Dynamic Contrast Enhanced Computed Tomography

Computed tomography (CT) is an image reconstruction technique that assimilates projection type data, acquired from multiple spatial orientations, into a high resolution cross sectional image [85]. Although the method can be applied to projection data from any modality, clinically, CT generally refers to imaging with x- rays. X-ray imaging is based on the absorption of high-energy photons by tissues and structures with variations in atomic number and density. The most common intravenous CT contrast agent is iodine, whose atomic number (N = 53) is twice that of calcium (N = 20). Iodine is routinely used to opacify and enhances the visualization of major vessels (e.g. surrounding the heart) during angiography [86], aid the detection of cancers in the brain and abdomen, and quantify flow and perfusion in tissue [87]. Pharmacokinetic modeling of CT contrast agents shares similarities with DCE-US perfusion techniques. Contrast enhanced CT utilize differences in lesion blood flow for detection and characterization. Due to their very slow flow rates, most clinically significant lesion blood flow is not detectable by ultrasound, using current Doppler techniques. However, microbubbles enable ultrasound to visualize this same information that CT is providing, with improved temporal and spatial resolutions. In addition, ultrasound contrast can be used for the characterization of lesions by providing additional vascular information not available with CT. Unlike contrast enhanced CT, ultrasound tracer concentration can capture the whole bolus with higher temporal and spatial resolutions, yielding new information about the lesion vascular morphology and blood flow dynamics. A major disadvantage of x-ray techniques is the potentially harmful effects of

19 radiation and acquisitions are restricted to a few snapshots of the bolus passage

(usually 30 sec, 60 sec, 3-10 minutes). For example, a typical 30 cm long chest CT

(64 slices x 0.6 mm thickness) delivers an effective radiation dose of approximately

6.0 mSv, three times the yearly dose from our natural environment [88, 89]. The sensitivity of ultrasound contrast has been shown to be similar to that of CT for metastases [90].

2.5 CONTRAST AGENTS

The first reported use of bubbles to enhance medical ultrasound was in 1968, when Gramiak and Shah noticed a sonographic enhancement in the aortic root following an intra-arterial injection of agitated saline [91]. This motivated research into smaller bubbles that could remain in the vasculature. A schematic structure of the biomedical microbubble is presented in Figure 5 [164].

Figure 5: Cartoon figure showing structure of a typical microbubble with different shell compositions. Microbubbles used for biomedical purposes are typically

between 0.5 and 10 μm diameter (the upper limit for passage through the lung capillaries) [165]

The gas core is a single chamber and comprises a large majority of the total particle volume. The shell acts as a barrier between the encapsulated gas and the surrounding aqueous medium. Different shell materials may be used, including lipid

(~3 nm thick), protein (15–20 nm thick) and polymer (100–200 nm thick). The lipid molecules are held together through physical force fields, such as hydrophobic and van der Waals interactions. The protein is cross-linked by covalent disulfide bonds.

The polymer chains are covalently cross-linked and/or entangled to form a bulk-like material.

Microbubbles are gas-liquid emulsions consisting of a gaseous core surrounded by a soft or hard shell. The gas core can be air or heavier gases such as perfluorocarbon (PFC), while the outer shell can consist of a variety of substances including albumin, polymers or a lipid bilayer [92-95]. The enhancement was attributed to the large discrepancy between the compressibility of the gas bubbles and the surrounding fluid. Microbubbles larger than RBCs would be trapped in the capillaries, and submicron- size microbubbles scatter ultrasound poorly and have insufficient stability. Like other contrast agents, microbubbles are injected intravenously, however due to their sizes of several microns (usually ranging between 1 and 4 μm; slightly smaller than red blood cells) confining them to the intravascular space (figure 6).

Figure 6 shows different ultrasound contrast agents and their distribution relative to the vascular endothelial cells. A: Schematic representation of different micro- and nanoparticles and their size ranges. Microbubbles are typically composed of a phospholipid shell encapsulating a gas, such as perfluorocarbon

(PFC) and total size ranges from 1-4 μm. B: Microbubbles due to their size are restricted to the intravascular space. Targeted microbubbles interact with target ligands on the endothelium. Smaller nanoparticle contrast agents are able to extravasate through the endothelium and enter the extravascular space, which opens up possibilities for targeting extra-luminal molecules.

Figure 6: Different ultrasound contrast agents and their distribution relative to the vascular endothelial cells [95]

2.5.1 Microbubble Material Preparation

Shell Material and Gas

The gas core comprises most of the particle volume and provides the mechanism for ultrasound backscatter and drug delivery. Gas bubbles of this size in aqueous media are inherently unstable owing to surface tension effects [97] and therefore require a stabilizing shell. The shell may be composed of surfactants, lipids, proteins, polymers, or a combination of these materials. Because the interior gas is a poor solvent for drug molecules, loading strategies must be employed within or onto the shell [96]. After the microbubble is prepared and administered in vivo, the primary role of the shell is to maintain microbubble integrity [166].

Protein Shells

The first albumin microbubble formulation to be approved by the US Food and Drug Administration (FDA) was Albunex (GE Healthcare). An Albunex suspension consists of roughly 7x108 microbubbles/mL with a size range from 1 to

15 μm diameter [98] and could pass the lung capillaries and provide contrast in the left ventricle of the heart. Albunex with an air bubble core, was made by sonication of albumin solution (so its shell consists of denatured albumin) [96] and is stable upon refrigeration for at least two years.

Surfactant Shells

Microbubbles stabilized by mixtures of the synthetic surfactants SPAN-40 and TWEEN-40 were formulated by Wheatley et al [99, 100]. These particular

23 microbubbles were not stable upon dilution, and therefore have limited biomedical utility.

Lipid Shells

Lipid-coated microbubbles are one microbubble formulations approved for clinical use in the US and abroad, including Definity (Lantheus Medical Imaging) and

Sonovue® (Bracco Diagnostics). Lipid shells have several advantages. Phospholipids spontaneously self-assemble into a highly oriented monolayer at the air-water interface, such that their hydrophobic acyl chains face the gas and their hydrophilic head groups face the water [96].

Polymer Shells

These types of microbubbles are stabilized by a thick shell comprising cross- linked or entangled polymeric species [96]. The bulk nature of the polymer shell makes it more resistant to area compression and expansion than its lipid and albumin counterparts, which reduces the echogenicity and drug delivery activity.

Gas core:

Early CEUS formulations were air filled and lasted in the circulation for less than 1 minute. Later, Insoluble gases such as sulfur hexafluoride and perfluorocarbons C3F8 (perflutren), C4F10 (perflubutane), C5F12 (perflenapent), and C6F14 (perflexane) were used that extended microbubble blood recirculation time from seconds to minutes, which was important for targeting efficacy. One of the

24 studies shows that, one C4F10 formulation, BR38, is reported to have circulation half-life in mice of nearly 10 minutes [101].

2.5.2 Detection of Microbubbles UCAs

Microbubbles are detectable by ultrasound because they have unique acoustic property to oscillate non-linearly when exposed to typical clinically used ultrasound pulses with frequencies ranging between 2 and 10 MHz that coincide with the resonance frequencies of most contrast microbubbles [102-104]. In contrast, surrounding tissue structures do not or only minimally show non-linear behavior and give rise to echoes that simply mirror the incident ultrasound pulse.

2.5.2.1 Non-linear behavior of Microbubbles

When ultrasound is exposed at very low mechanical index (MI 0.08-0.3), the microbubbles start to oscillate giving rise to a non-linear response contrasting the linear response of the myocardial tissue at high mechanical index. Ultrasound generates acoustic wave of positive and negative (sinusoidal) pressures. When this wave encounters microbubbles, they undergo compression at positive of ultrasound wave and expansion at negative of ultrasound wave. This results in an asymmetric- nonlinear bubble oscillation as shown in the figure pp. This asymmetry produces harmonics, which can be utilized to enhance the signals from the bubbles and effectively distinguish them from the surrounding tissue, thus improving the

25 imaging capabilities. Contrast specific ultrasound imaging modalities remove the linear tissue response and enhance the contrast microbubble response. There are different techniques developed such as the power modulation, pulse inversion or coherent contrast imaging to emphasis the contrast microbubbles acoustic signals and filter the tissue signals which substantially increases signal-to-background ratios which allows detection of small amounts of accumulating microbubbles [105].

High mechanical index imaging causes microbubble destruction known as destruction replenishment imaging or flash imaging (Fig 7) [165]. In this process bubbles within a plane are destroyed, allowing the ultrasound intensity as a function of time to be determined upon immediate replenishment of bubbles. By high mechanical index triggered imaging, myocardial perfusion can be assessed.

Low-mechanical index imaging is the most commonly used modality, often combined with a high-energy ultrasound flash causing microbubble destruction. By this technique real-time contrast echocardiography with simultaneous assessment of myocardial function and perfusion can be performed.

Figure 7: Physical mechanisms underlying the biological effects induced when microbubbles are excited by ultrasound energy [165]

The increase in the applications for UCA has stimulated research and development in UCA synthesis in both industry and academia [106-108]. Currently, more than five commercial UCAs are available for clinical use, and more than 20 potential UCAs as listed in the table 2 have been tested around the world. Targeted

UCAs are also commercially available [109-111]. Additionally, a number of different ways to synthesize UCAs using various shell materials and internal gas have been published [112-114].

We investigated a simple method to synthesize UCAs in our laboratory and then evaluated the properties of it. The synthesized UCAs were analyzed by high- resolution optical microscopy to confirm bubble formation. Bubble size distributions in aqueous solution were analyzed with an automated cell counter

(Invitrogen™ Countess™ II FL Automated Cell Counter). Light microscopy confirmed the particle size distribution. The responses to ultrasound imaging and the stability of the synthesized UCAs were quantitatively studied by ultrasound imaging over a two-week period.

2.5.2.2 Relevant Studies

UCAs have broad array of applications beyond imaging, including targeted high-intensity focused ultrasound (HIFU), drug delivery and drug delivery through the blood brain barrier (BBB) [115-119]. The nonlinear response of UCAs to the ultrasound field can cause localized micro streaming or jet streaming accompanied by cavitation, allowing for alterations in the permeability of the skin, cell membrane,

27 and BBB without significant damage to normal tissue [120-122]. This effect can be used to facilitate drug delivery to a localized area [123-126]. Lately, targeted drug delivery has been made more plausible by attaching ligands to the UCA shell [117,

118, and 127-31]. On the other hand, the use of UCAs during HIFU can reduce the energy required to damage cancerous tissue by increasing heat deposition or cavitation [132-136].

2.5.3 Principles for Evaluating the Microvasculature with Contrast Agents

Currently, there is much interest in the clinical application of ultrasound contrast agents, the use of which has the huge potential to expand the diagnostic capabilities of ultrasound investigations [137]. The characterization of these UCAs in terms of ultrasonic parameters, stability and bio distribution in the microvasculature is necessary prior to their clinical application. The detailed assessment of the microvasculature from correlation between the time-intensity curve and various experimental conditions necessitates the use of test flow phantoms suitable for the measurement of the flow parameters. Specifically, in the present study time-intensity curves were measured with three different types of flow phantoms. One is a single vessel flow phantom made of a single vessel, other is vascular flow phantom consisting of number of 15 vessels in a complex geometry and the last one is tumor vascular flow phantom in variety of vessel geometries such as curves, bifurcations, trifurcations, tumors, etc.

To effectively use contrast agents to quantify the microcirculation and extract useful information from a contrast time-intensity curve in any phantom model, an understanding of indicator dilution principles is required. Indicator dilution is based on applying a pharmacokinetic model to the test flow phantoms that accurately describes the transport of the tracer through the vasculature.

The techniques and materials used for developing these flow phantoms must satisfy the fundamental assumptions of tracer studies [38]: 1. The amount of tracer injected does not change the physiology of the organism (does not disturb the system that is being measured); 2. The mass of the injected tracer is conserved; 3.

The tracer is uniformly mixed in the carrier fluid; 4. The measured system is time invariant over the course of the study; 5. There is a known relationship between the signal that is measured and the tracer concentration.

3.6 Flow Phantoms models

The indicator-dilution theory is based on the law of mass conservation. After a dose of indicators is injected into the blood, the concentration of the agent is monitored as a function of time. The microbubbles are mixed in a few compartments in the circulatory system. Each of these compartments can be treated as a simple mixing chamber. A simple mathematical model can describe the mixing process inside the chamber and the response from each chamber can be characterized by a transfer function. Thus, by considering concentration of the indicator entering the mixing chamber as the input function, the output function defined as the

29 concentration leaving the chamber is simply the convolution of the input function and the transfer function [139]. With multiple mixing chambers in cascade connection, the same procedure can be repeated. The flow rate and the volume of the mixing chamber can then be derived if the transfer function of a particular mixing chamber can be estimated.

Note that the concentration is not directly measured in practice. Instead, the backscattered acoustic intensity is used to represent the concentration of the microbubbles [140]. Such an approximation may not be valid when the bubble concentration is high [141] and [142]. Figure 8 is a schematic diagram of generation of the time-intensity curve. Assuming a transducer focuses a sound beam in a vessel, as shown in the left panel, the lower right panel shows a corresponding M-mode image with the horizontal axis denoting time and the vertical axis representing depth. At each time instance, intensities of the backscattered signals within the vessel are averaged and the average value becomes a point in the time-intensity curve shown in the upper right panel. This process continues at all times until a complete time-intensity curve is generated.

Figure 8: Illustrating Flow in Single Vessel Flow phantom

3.6.1 Single-vessel flow phantom model:

Consider the simple model depicted in Fig 9. Consider a system with a single inflow and a single outflow orifice. The internal structure of the system is of no concern for the present. Recirculation does not occur; that is, once a unit of fluid leaves the system it does not reenter. Blood flow of rate Q enters and leaves a blood- mixing chamber of volume V via a single input vessel and a single output vessel.

Figure 9: System with a single inflow and a single outflow orifice [145]

When an indicator is injected suddenly into some portion of a vascular bed it does not all appear suddenly at a sampling site but it is dispersed with respect to time.

The time required for a given indicator particle to flow from entrance to exit through the system, by whatever path, is its transit time.

To illustrate this process, the following is considered: Let m1 units of indicator be injected at time zero into the entrance to the system, and measure the concentration of indicator at exit as a function of time, e (t). The amount of indicator, dm, leaving the system during a small time interval between time t and time t + dt is the concentration of indicator, c(t), multiplied by the volume of fluid leaving the system during this time interval, and this is the flow, Q, in units of volume/time, multiplied by the time interval, dt; that is, dm = c(t) Q dt. Because all indicator, m, must leave the system, equals the sum of the amounts leaving the system during all such time intervals, or

………………(2.1) ∫ ∫ where

……………….(.22)

∫

Measuring the area under the observed indicator concentration-time curve and dividing it into the known quantity of injected indicator determine the unknown flow, Q, through the system. We now introduce the distribution, or frequency, function, h (t), which describes the fraction of injected indicator leaving the system per unit time at time, t; that is,

……………(2.3)

Where Q c (t) is the rate at which indicator leaves the system at time t.

We must bear in mind that the area under a frequency function is unity and specifically

∫ ∫ …………..(2.4)

Combining equations (1) and (2),

…………..(2.5)

∫

Thus, to determine h(t) for fluid particles, each experimentally observed c(t) for indicator is divided by the area under the curve c(t) versus t.

We can now determine the total volume of the system simply by adding together all the elements of volume in equation 2.5. Formally, we integrate equation 2.4 to find

……….(2.6) ∫

Because h(t) is the frequency function of transit times,

……………(2.7) ∫ is by definition the mean of all transit times or the mean transit time, t. Therefore,

…………….(2.8) which states the fundamental fact that volume equals flow multiplied by mean transit time. In terms of the observed concentration of indicator, from equation (2.3) and equation (2.5),

∫ ……………….(2.9)

∫

Where, the ratio of integrals is nothing more than instructions for finding t.

Measurement of Flow and Volume by Constant Injection

When indicator is introduced continuously into the entrance to the system at constant rate, mi, in units of mass per time, if mixing at inflow is complete, the concentration of indicator admitted to the system is

………..(2.10)

The system is unaware of whether the indicator diluter is using sudden or constant injection. In the illustrated case, no indicator appears at exit until the third time interval when the element of volume characterized by the briefest transit times

34 will display the first indicator particles. In this element of volume the concentration of indicator is mi/Q during the third time interval. The fraction of outflowing particles with this transit time is h(t) dt. Therefore, the concentration of indicator in the total outflow at this time is

…………(2.11)

2.6.2 Multi-vessel Flow Model

To understand the multi-vessel model we must look for a moment at a real system, say a vascular bed. In this case, we hypothesize the effects of not following of our first assumptions, or Real Vascular System. However, it is not even essential that all fluid pass through a single channel. If fluid from each input channel bifurcates into every other input channel before leaving the system, it may still be possible to measure flow [143]. The criterion for satisfactory intermingling is simply that, for the case of constant injection, the limiting concentration of indicator in every output channel must be the same. In a study, a linear systems theory approach was applied to flow measurement that treats the complex architecture of the microvasculature as a black box with a single input and single output [144].

However, the tracer injected into the flow system will dilute into the fluid volume and be carried along the different transit paths from the input to the output of the system. After a certain period of time, the tracer will begin to emerge at the output in proportion to the distribution of possible transit times. Provided that a flow

35 system is linear and stationary, conservation of mass requires that the mass of tracer that enters the system must be equaled by the sum of the mass that exits and remains in the system.

Figure 10: Illustration of flow in a multi-vessel phantom [145]

In practice, a known quantity of indicator is introduced into a fluid flowing at unknown rate through a system of unknown volume. Fluid is sampled or monitored at one or more points downstream from the plane of introduction and the concentration of indicator, diluted by the parent fluid, is measured as a function of time.

2.6.3 Tumor Vessel Flow Model:

In this case, we hypothesize the effects of not following of our normal vascular assumptions, or real neoplastic system in a diseased condition. As cell growth continues, the microenvironment surrounding the tumor becomes critically hypoxic due to an exhaustion of local oxygen supply. This ‘stressed state’ stimulates a cascade of biological events that eventually form a dedicated tumor microvasculature (Fig. 11) [145]. In contrast to the self-limiting processes that occur during normal angiogenesis, tumor angiogenesis is wildly uncontrolled. However, it is not essential that all fluid pass through a single channel. Once a small avascular mass recruits a dedicated blood supply, a positive feedback loop is created driven by progressive tumor growth and a chronic state of hypoxia and acidosis [77, 145 and

146]. Fluid from each input channel bifurcates into every other input channel, passes through leaky tumor vasculature and coalesces in the extravascular space before leaving the system. It may still be possible to measure flow. The criterion for satisfactory intermingling is simply that, for the case of constant injection, the limiting concentration of indicator in every output channel must be the same.

Figure 11: Illustration of Flow in tissue-mimicking tumor vasculature flow phantom [145]

However, the tracer injected into the flow system will dilute into the fluid volume and be carried along the different tumorous transit paths from the input to the output of the system fig bb. Due to their size of several microns (pure intravascular contrast agents), contrast agents are able to enter the extravascular space of tumors and, thus, could theoretically be targeted against a larger number of disease specific markers beyond the targets expressed luminally on the vascular endothelial cells. Theoretically, tumor vasculature is thought to be leaky and defective, which will result in increased accumulation of tracer agents within the tumor [147, 148]. After a certain period of time, the tracer will begin to emerge at the output out of proportion to the distribution of possible transit times. Since a

38 flow system is nonlinear, the mass of tracer that enters the system must not be equaled by the sum of the mass that exits and remains in the system.

Also, early cancer detection and the imaging of tumor angiogenesis has been the most studied and promising target of ultrasound molecular imaging using microbubbles in cancer. The increasing interest in micro vascular imaging to study the blood flow has resulted in phantom development. Some phantoms have attempted to mimic a structural organ such as the prostate [149], while others have sought to mimic vasculature. Some vascular phantoms compatible with x-ray, ultrasound, and magnetic resonance were designed to mimic large vessels [150], such as the carotid artery [151]. Other studies have sought to create a vascular phantom using real vessels harvested from cadavers [152, 153]. Some of the major limitations of these designs are the size of the vessels and the absence of a leaky state, where small contrast agents may traverse between the mimicking vascular and tissue types. To the best of the knowledge, except for few commercially available; there is currently no phantom present within the literature that can provide information on tumor characteristics combined with the ability to measure flow at the micro vascular level. In this study we present three types of micro vascular phantoms (1) single vessel, (2) multi-vessel and (3) multi-vessel with artery bifurcations and structural abnormalities typical of diseased (tumor) vascular networks.

Our motivations for developing such kinds of phantoms were both to develop vascular models with complex geometries to mimic diseased vasculature and also to

39 introduce flow to enable contrast enhanced ultrasound flow quantification. Some studies have indicated the ability to mimic a complex structure akin to that of vessels in tumors by intertwining silicone pipes in a water tank to some extent, allowing phantoms to be produced with ‘realistic’ flow and tumor properties but did not exhibit tissue-mimicking material for realistic ultrasound acoustic properties

[154]. However, agar along with sephadex has been used to develop tumor and

TMM for multi-modal imaging applications, the phantoms did not extract the representations of different types of vessels with flow characteristics. These two features have been published separately (154, 155). Phantoms that are used in medical imaging research to substitute for real vasculature can be modeled on anatomical features, such as vessels and vascular trees, including structures of diseased vasculature that behave inappropriately, such as a tumor tissue. In truth, tissue-mimicking materials (TMMs) have most commonly been made with an agar or gelatin base [156-162]. A major benefit the formation of wall-less vessels is a far more realistic approximation of in vivo acoustic properties. Another benefit to modeling these features with wall-less agar vessels rather than using silicone tubing to mimic vessels is the phantom is reproducibility; the properties of the third phantom were more similar to those of the micro vascularization due to its dimensions while the parallelism with the capillaries reflect the complex and irregular structure of tumor micro vascularization with a known assumption for flow. The concept of wall-less vessels is explained in detailed in the next chapter of

Materials & Methods. Once the phantoms are developed we further study, correlation between the time-intensity curves obtained under various flow

40 conditions. Additionally, the study was confirmed by the integration of ultrasound and CT scan images.

CHAPTER III

MATERIALS & METHODS

3.1 Preparation of Ultrasound Contrast Agents (UCAs) Microbubbles

3.1. 1. Selection of Internal Gas: Octafluoropropane

Octafluoropropane gases are widely used as the internal gas in the synthesis of ultrasound contrast agents because these gases are relatively inert, nontoxic and generally have low solubility in water [167]. Octafluoropropane also chemically known as Perflutren (Alpolo, FC-31-10, city, UK), in particular, was chosen for this in vitro study because it can be easily liquefied and handled in a simple laboratory routine due to its relatively high boiling temperature of -36.7°C [167].

Octafluoropropane is chemically characterized as 1, 1, 1, 2, 2, 3, 3, 3- octafluoropropane. It has a molecular weight of 188, empirical formula of C3F8 and has the following structural formula: The standard cylinder-valve outlet connections

42 of a Perflutren gas cylinder was made into a custom gas control manifold using

Figure 12: Structural formula of Octafluoropropane [167]

various metal and brass fitting to draw gas from the cylinder into 10 mL glass vial.

The standard cylinder-valve outlet connections of a Perflutren gas cylinder was made into a custom gas control manifold using various metal and brass fitting to draw gas from the cylinder into 10 mL glass vial.

3.1.2. Selection of Outer Lipid Shell

1, 2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and 1, 2-dipalmitoyl-sn- glycero-3-phosphoethanolamine (DPPE)

a) DPPC has a molecular weight of 734, empirical formula of C40H80NO8P, and the following structural formula:

Figure 13: Structural formula of DPPC [167] b) DPPE has an approximate molecular weight of 5750 represented by empirical formula C265H527NO123PNa, and the following structural formula:

Figure 14: Structural formula of DPPE [167]

Figure 15: Colored molecules are DPPC head group (shown in blue), DPPE head group (shown in green), and lipid tails (shown in gray), and water (shown in pink) [169, 170]

The amount of lipid is a critical factor in the formation of shelled microbubbles. A comparative study of DPPE and DPPC shows that DPPE plays an important role in the membrane fusion mechanism and vesicle formation [169, 170] and DPPE molecules can form inter- and intra-molecular hydrogen bonds as shown in the above figure 15, where the amine group (hydrogen-donor) can interact strongly with water (hydrogen-acceptor). These strong intermolecular interactions affects membrane permeability, stability, and other biological properties normally associated with the functional operation of internal cell organelles. All these features make DPPC and DPPE very attractive in terms of membrane organization and functionalities—in particular, nonlinear behavior useful for contrast-enhanced ultrasound imaging.

3.1.3 Preparation of Stock Solution

The Stock solution was prepared by adding 10 mL of the Phosphate Buffer solution (PBS) (Sigma Aldrich, Saint Louis, MO, USA), 200mg of 1, 2-dipalmitoyl-sn- glycero-3-phosphocholine (DPPC) (Sigma Aldrich, Saint Louis, MO, USA), 50mg of 1,

2-dipalmitoyl-sn-glycero-3-phosphoethanolamine (DPPE) (Sigma Aldrich, Saint

Louis, MO, USA) and 1g of glucose (Sigma Aldrich, Saint Louis, MO, USA) in a beaker.

The molar mass ratio of DPPC to DPPE was approximately 4:1. The mixture was

45 heated at 90 °C for 20-30 minutes in a boiling water bath to dissolve the DPPC and

DPPE powders in glucose, pipette mixing every 5 minutes. After complete dissolution the solution was transferred to a 2 mL aluminum-sealed center rubber- hole, glass bottle that can be stored at 4 degree Celsius up to 6 months.

Figure 16: Preparation of Stock solution; The mixture with glucose, DPPE, DPPC and PBS in the mentioned ratio was heated at 90 °C for 20-30 minutes in a boiling water bath, then transferred to a 2 mL injection vials (aluminum-sealed rubber center glass bottles)

250 μl of the prepared microbubble stock solution was transferred to an aluminum sealed 2 mL glass vial and incubated at 40°C for approximately 15 minutes. 50 μL of glycerol (Sigma Aldrich, Saint Louis, MO, USA) solution was added to this pre- warmed solution. A manual cap crimping sealing machine was used to seal the

46 bottle by placing the crimping head above the aluminum cap and holding the crimping hand tight and pressing it. The pulling action while pressing the handle caused an airtight seal. Laboratory vacuum turret nozzle was connected to a hose.

The distal end of the hose was furnished with connector to fit 2 mL needle. The air in the glass bottle is then replaced with vacuum as shown in figure 17.

Figure 17: Customized technique to replace air with vacuum. Hose setting connected to a needle, which was injected into the glass bottle to replace air with vacuum

Approximately 20 μL of Octafluoropropane gas was withdrawn from the cylinder by injecting a hypodermic needle through the vial aluminum- rubber cap, into the solution.

Figure 18: Customized setting of Octafluoropropane gas cylinder outlet to inject of Octafluoropropane gas into glass bottle through hypodermic needle

The vial was shaken vigorously for 30 seconds at 4,300 rpm with amalgamator

(Lantheus Medical Imaging Inc., MA, USA) at room temperature thus producing Gas- filled lipid shell microbubble contrast agents as shown in figure 18.

Figure 19: Preparation of contrast Agent’s microbubbles: Stock solution in the glass bottle on the left side was amalgamated for 30 s to get gas-filled lipid shell microbubble contrast agents under the milky-white foamy layer in the glass bottle on the right side

The synthesized UCAs were observed and analyzed by light microscope (Olympus,

Hamburg, Germany) and a counting chamber (Invitrogen Countess, USA) to confirm bubble formation.

3.1.4 Observation of Microbubbles under Microscope

Microbubbles contrast agents were analyzed using high-end optical microscope under 40X magnification.

3.1.5 Analysis of Microbubbles using Invitrogen Counting Chamber

1 mL of UCAs was diluted in de-ionized water to make the suspension. 20 μL of cell suspension was transferred to a small micro tube having 20ul of 0.4% trypan blue stain. Trypan blue was added to estimate the number of active bubbles in the microbubble suspension. It was mixed gently by pipetting up and down. 20 μL of the sample mixture was added to the chamber ports on one side of the Countess™ cell counting chamber slide. The two chambers of the slide were labeled “A” and “B” for easy tracking of your samples. The Countess™ cell counting chamber slide was inserted, sample side ‘A’ first into the slide inlet on the instrument, making sure that the sample side A is inserted completely into the instrument. UCA microbubbles were counted and size distribution was studied.

3.2 Preparation of Tissue-mimicking vascular flow phantoms

All phantoms were produced in three basic steps. First, a vascular mold was formed inside an acrylic box. Next molten agar was poured into the box and allowed to set. Finally internal mold materials were removed, resulting in a tissue mimicking material. Details of individual phantom types are provided below.

3.2.1 Single vessel flow Phantom

A) Single vessel flow Phantom acrylic box construction

Each phantom consisted of a 10 cm × 7 cm × 4 cm rectangular box of acrylic material as shown in figure 20. 11-mm-diameter cylindrical holes were made on the sides of the wall with the advanced laser-cutting machine. Holes were made to the correct diameter of 11 mm and depth 0.6 mm. A tap was used to cut threads inside the holes. It was held in line with the hole and turned clockwise to create threads and anti-clockwise to remove the thread bits. Pipe connectors (Cole–Parmer) of size

1/8 x 1/8 NPT were glued in place with silicone adhesive (Huntsman Advanced

Materials, Basel, and Switzerland). Reticulated foam (pore size 0.5- 1 mm) was glued with silicon adhesive to the inside wall and around the pipe connectors in the box.

The reticulated foam provided a fixed structure into which the agar could flow when molten and, once set, prevented the agar gel from coming away from the connectors and the box when flow was applied.

Figure 20: Flow Phantom acrylic box construction of size 10 cm × 7 cm × 4 cm

B) Preparation of Single vessel mold

A wooden rod acted as mold for a single vessel, with diameters between 1 and 2 mm, typical of rat femoral and common carotid arteries [171, 172]. The rod was inserted through both of the pipe connectors. The connectors were aligned to ensure that the rod was straight.

C) Preparation of Tissue-mimicking mixture

20 g or volume ratio 10% of Agar (Sigma Aldrich, Saint Louis, MO, USA) was added to 250 ml degassed, deionized and distilled water in a beaker. A magnetic stirrer was used while the mixture was heated in order to prevent the agar from burning at the bottom of beaker. Once the water bath heater had reached 90°C, continuous stirring was continued for 15 minutes in order for the mixture to reach equilibrium temperature. At the end of this period, the agar had dissolved and the

51 mixture appeared homogenous. After this, the heater was switched off, and the TMM was left to cool while still being stirred until the TMM temperature had reached

50°C.

The TMM was then poured into the phantom mold, covering the metal rod.

The box was completely filled with TMM to form a relatively gas free upper phantom boundary allowing good coupling between the phantom and the imaging transducer. The TMM was left to set and after the agar was completely cured, the box was covered with a plastic sheet to prevent the TMM from drying out.

The rod was carefully removed, and C-flex tubing (Cole–Parmer) was then attached to the external ends of the connectors to act as feed-in and feed-out pipes. The top and side views of the flow phantom prepared are shown in Figure 21.

Figure 21: Process of preparation of Single vessel flow phantom

D) Single Vessel Flow Phantom Experimental Setup

The single vessel phantom model was validated using a setup designed to provide stable and reproducible data under different flow conditions [93, 111]. The schematic diagram of the experimental set-up is illustrated in Figure 22.

The feed-in pipe of the tissue mimicking flow phantom was connected to a peristaltic pump (Watson Marlow, Falmouth, United Kingdom) generating a pulsatile flow with different flow rates. The reservoir containing 500 ml solution circulating in the closed system consisted of de-ionized water and UCA, which underwent constant stirring in the reservoir tank to obtain an even distribution of

CA in the solution.

The ultrasound linear probe (LG308-16A) was fixed by a tripod holder at longitudinal plane from the vessel phantom lumen. The transducer was fixed at a distance of 2 cm from the vessel lumen.

Figure 22: Single Vessel Flow Phantom Experimental Setup

E) Data Acquisition

After the peristaltic pump flow rate was set to a proper value and turned on, it was allowed to run degassed water for approximately 20 seconds before data acquisition so as to ensure the flow has been directed through the phantom and that a steady flow was developed. The pump was then turned off and the ultrasound probe (Fukuda Denshi, LG308-16A) was placed at a desired position and gently lowered to the surface of the phantom, taking care not to apply excess force and distort the TMM. The cross-section of the single vessel (with diameter of 0.912mm) was imaged using B-mode imaging. The feed-in and feed-out pipes were placed in the reservoir. A Fukuda Denshi Ultrasound scanner (Tokyo, Japan) was used to collect ultrasound data from the phantom. The imaging and experimental parameters are summarized in Table 3 and 4. The real-time videos were recorded to the PC through a Video Grabber card (Monoprice USB video & Audio Grabber,

China) and saved for offline analysis.

Mechanical Index 0.9

Depth 2 cm

Frame Rate 17 fps

Probe Frequency 14 MHz

Dynamic range 75 dB

Flow-rate mL/min 10 – 50 mL/min

Table I: The B-mode Ultrasound imaging parameters

Flow-rate mL/min Imaging Plane

10 Longitudinal

10 Lateral

20 Longitudinal

20 Lateral

30 Longitudinal

30 Lateral

40 Longitudinal

40 Lateral

50 Longitudinal

50 Lateral

Table II: The B-mode ultrasound imaging experimental parameters

3.2.2 Multi-vessel Flow phantom

A) Multiple-vessel flow Phantom construction

Figure 24 illustrates the phantom and measurement setup. A rectangular acrylic box of similar dimensions and tapping as used in single vessel phantom acted as the container for the multi-vessel phantom. Pipe connectors (Cole–Parmer) of size 1/8 x 1/8 NPT were glued in place with silicone adhesive (Huntsman Advanced

Materials, Basel, and Switzerland). Reticulated foam (pore size 0.5-1 mm) was glued with silicon adhesive to the inside wall and around the pipe connectors in the box.

B) Preparation of Multiple-vessel mold

Twenty consolidated processed wires of Cross sectional area of 0.3255 mm2 acted as vessel mold for the multiple-vessel phantom, with diameter 0.6438 mm.

The wires were inserted through both of the pipe connectors. The wires were folded into arched shape to mimic the shape of the typical capillary bed as shown in the following figure 23.

Figure 23: The wires folded into arch shape to mimic the shape of the typical

capillary bed. Each wire is of diameter 0.6438 mm

C) Preparation of Tissue-mimicking mixture

20 g representing a 10% volume ratio of Agar was added to 250 ml degassed, deionized and distilled water in a beaker. The mixture was heated with the magnetic stirrer stirring inside to prevent the agar from burning at the bottom of beaker.

Once the water bath heater had reached 90°C, the mixture was left to heat at this temperature under continuous stirring for 15 minutes. At the end of this period, the agar had dissolved and the mixture was homogenous. After this, the heater was switched off, and the TMM was left to cool while still being stirred until the TMM temperature had reached 50°C.

The TMM was then poured into the phantom mold, covering the metal rod.

The box was completely filled with TMM to form a strong upper vessel wall to allow the transducer to obtain images. The TMM was left to. After the agar was completely cured, the box was covered with a plastic sheet preventing the TMM from drying out.

The wires were retracted from the box to form the hollow wall-less multiple vessels as shown in the figure, and C-flex tubing was attached to the external ends of the connectors to act as feed-in and feed-out pipes. The multi-vessel flow phantom prepared is shown in Figure 24.

D) Multiple- vessel Experimental Setup

This section for multi-vessel phantom is similar to single-vessel phantom experimental set up.

Figure 24: Multi-Vessel Flow Phantom Experimental Setup

E) Data Acquisition

During experiment, after the peristaltic pump flow rate was set to a proper value and turned on, it was allowed to run degassed water for around 20 second before acquiring actual data so as to ensure the flow has been directed through the flow phantom and a steady flow was developed. The pump was then turned off.

After this the ultrasound transducer probe was placed at a desired position and lowered as close as possible to the surface of the phantom without applying excessive pressure to the TMM. The cross-section of the single vessel (with diameter of 0.912mm) was imaged using B-mode imaging. The feed-in and feed-out pipes were placed in the reservoir. Once the set up was ready, A Fukuda Denshi

Ultrasound scanner (Tokyo, Japan) equipped with linear probe (LG308-16A) was used to collect ultrasound data from the phantom. The imaging and experimental parameters are summarized in Table 4. The real-time videos were recorded to the

PC through a Video Grabber USB and saved for offline analysis.

The cleaning procedure was performed after each experiment in order to remove all

MBs in the closed system. During the cleaning procedure, the plastic tubes were flushed with de-ionized water. Cleaning procedures were performed every time before a new batch of CA was added to the system and a new data is collected.

3.2.3 Tumor-vasculature flow phantom

A) Tumor-vasculature flow Phantom construction

Figure 24 shows the phantom and measurement setup. A rectangular acrylic box was used for other two types of vascular phantom acted as the container for the multi-vessel phantom.

B) Preparation of Neoplastic Vasculature mold

The neoplastic vasculature was constructed to characterize the neoplastic blood flow properties with random tortuosity added to the wall-less vessels.

Different vessel configurations were created. First, a single vessel representing

“healthy” this develops tortuosity abnormalities along its centerline. This vessel network was designed to have two different bifurcations. At each bifurcation, the daughter vessel diverges in opposite directions. Prior to bifurcation, the daughter vessels resulting from that bifurcation trace equivalent paths to ensure smoothness at these junctions. By varying the angles at which daughter vessels diverge, we simulated vascular changes due to the onset of disease. The angles at which vessels diverge have been linked to the presence of liver disease [173], with the fibrous tissue in diseased livers causing wider divergence angles between hepatic vessels. A more tortuous vessel was further created by adding multiple independent vessels combined with the bifurcated vessels. This type of vasculature simulated morphological abnormalities relevant to tumor angiogenesis which mimic the onset of disease [174].

C) Preparation of Tissue-mimicking mixture

Once the water bath heater had reached 90°C, the mixture was left to heat at this

61 temperature under continuous stirring for 15 minutes. At the end of this period, the agar had dissolved and the mixture was homogenous. After this, the heater was switched off, and the TMM was left to cool while still being stirred until the TMM temperature had reached 50°C.

D) Preparation of Tumor mimicking material

Mimicking of tumors requires the determination of an appropriate choice of tumor mimicking material in terms of increased complexity of vascular flow. Porous

Polyurethane (PU) was selected due to its advantageous physical properties that are mainly open cell, permeable to air, breathable and water absorbent. Its most important attribute is, however, its elasticity; or, in other words, its reversible deformability - an attribute whose degree can be set during the production process according to its intended use as tumor mass of cells. The inclusions to mimic the cancer in different sizes were made using PU soft foam. The foam was modified as desired by cutting in a circular shape of approximate diameters of 10 mm, out of the foam sheet as shown in figure 25. In each tumor mold, a tiny hole for inserting the wires was drilled. These tumor mold inclusions were randomly inserted in certain regions on the wires. Such modifications served to alter the transmission pattern of ultrasonic waves for imaging in a manner different from the multi-vessel flow pattern and can be used to mimic the ultrasound characteristics of desired parts or conditions found in the diseased state of the human vasculature.

Figure 25: The foam was modified to a shape as shown in the figure by cutting in a circular shape of approximate diameter of 10 mm

E) Preparation of Tumor vasculature flow phantom

The PU tumor inclusions were inserted through the bifurcated and independent wires and placed within the box. The TMM was poured into the phantom mold, covering the tumor inclusions and the wires. The box was completely filled with TMM to form a strong upper vessel wall to allow the transducer to obtain images. The TMM was left to set. After the agar was completely cured, the box was covered with a plastic sheet preventing the TMM from drying out. Inclusions that mimic the properties of tumors were hence looked like strung onto different vessels placed into the phantom container at specific locations and were embedded in TMMs. The wires were retracted from the box to form the hollow wall-less multiple vessels as shown in the figure while the tumors remained embedded in the TMM. C-flex tubing (Cole–Parmer) was attached to the external

63 ends of the connectors to act as feed-in and feed-out pipes. The side and top views of the tumor vessel flow phantom in a 3D solid work design are shown in Figure 26.

Figure 26: Tumor vessel flow phantom in a 3D solid work design: The upper phantom is the side view and lower phantom is the top view of the tumor vessel flow phantom in a 3D solid work design showing consolidated wires and tumors embedded in agar TMM to simulate the morphological abnormalities relevant to tumor angiogenesis which mimic the onset of disease. The wires are then retracted and tumors are left with wall-less vessels embedded in the agar tissue

F) Tumor vasculature experimental set up

This section is similar to multi-vessel flow phantom experimental set up.

Figure 27: Tumor vasculature Flow phantom experimental set up

G) Data Acquisition

This phantom represented simulations of a healthy vessel, which has developed high spatial-frequency tortuosity abnormalities, which are typical of cancerous vasculature. During experiment, the peristaltic pump flow rate was set to

65 a given value and then turned on. It was allowed to run degassed water for around

20 second before acquiring actual data so as to ensure the flow has been directed through the flow phantom and a steady flow was developed. The pump was then turned off. After this the ultrasound transducer probe was placed at a desired position and lowered as close as possible to the surface of the phantom without too much pressing into the TMM. The cross-section of the single vessel (with diameter of 0.912mm) was imaged using B-mode imaging. The feed-in and feed-out pipes were placed in the reservoir. Once the set up was ready, a Fukuda Denshi

Ultrasound scanner (Tokyo, Japan) equipped with linear probe (LG308-16A) was used to collect ultrasound data from the phantom. The real-time videos were recorded to the PC through a Video Grabber USB and saved for offline analysis.

A cleaning procedure was performed after each experiment in order to remove all

MBs in the closed system. During the cleaning procedure, the plastic tubes were washed with de-ionized water. Cleaning procedures were performed every time before a new batch of CA was added to the system and a new data is collected.

3.3 Computed Tomography (CT): Comparative study of Vessel diameter measurement

To verify ultrasound-derived vessel diameter measurements, multi-detector

CT was also used to image the agar-based wall-less single-vessel, multi-vessel, and tumor vessel flow phantoms. In this experiment, the ultrasound experimental set up

66 used to evaluate flow parameters was not implemented, instead only the individual phantoms were scanned without any flow of contrast microbubbles within the flow phantoms.

Imaging data were acquired using a 16-slice CT system (Philips, Brilliance

Big Bore, USA) which has a spatial resolution of 16 lp/cm in the x-, y-, and z-axes.

The Brilliance CT Big Bore Radiology technology configuration, which incorporates the 85 cm large, bore and 60 cm true scan field-of-view. The flow phantom was placed in the iso-center of the scanner. Data were acquired by using a RapidView 4D reconstruction CT diagnostic protocol. Parameters included a 60 cm true scan field of view, 300 effective mAs (effective tube current–time product is tube current–time product divided by pitch), detector collimation of 2 x 0.6 mm, 16 x 0.75 mm, 16 x 1.5 mm, 8 x 3.0 mm, 4 x 4.5 mm, and a gantry rotation time of 0.44 second. CT images, which were reconstructed using extended display field of view technology with image matrix 512x512 which offered extrapolated reconstruction for visualization of anatomy out to 70 cm, which may be useful in radiation oncology for avoidance in treatment planning. Due to reconstructive distortion data outside of 60 cm was not considered to be of diagnostic quality and thus omitted. With this process, 15 static images of the phantoms were created at each scan level.

CHAPTER IV

RESULTS & DISCUSSION

4.1 RESULTS

4.1.1 Ultrasound Contrast agents:

Prior to application, the microbubble solution obtained by amalgamation of the sealed vial was allowed to settle down for approximately 2 minutes. 1.5 mL of contrast agent was withdrawn from beneath the foamy surface as shown in the figure 19. The fluid below the foam layer was used as the US contrast agent. In an initial assessment, the synthesized UCAs were analyzed by using high-resolution optical microscopy to confirm bubble formation. The diameter of the microbubbles was measured under a high-end optical microscope and ranged from 5 to 20

μm. Figure 1 shows a microscopic image of prepared lipid-based contrast agent. It is noted that the bubbles may appear to be artificially large in the image because they were compressed when taking the picture. A cell counter and plotted against

68 number of microbubbles as shown in figure 2 measured the size distribution of the contrast agent. The median diameter was 18 μm and the minimum size of the microbubble was 5 μm.

Figure 28: Two-dimensional microscopic photo of the UCAs. The shape of the UCAs was observed under optical microscope at 40X magnification.

Figure 29: Size distributions of UCAs. The size distribution of UCAs was measured with a cell counter and plotted against number of microbubbles.

Figure 30: Study of the microbubble size on a cell counter.

4.1.2 Detection by Ultrasound

During preliminary examinations with microbubble contrast, individual

“dots” of ultrasound reflection can be visualized. To confirm whether these signals represent individual microbubbles, very dilute suspensions of ultrasound contrast agents or individual microbubbles were prepared and studied by ultrasound imaging. Ultrasound of dilute microbubble dispersions (free floating) demonstrated distinct white foci; concentration of these sites was consistent with signals from individual microbubbles over a period of time. The characteristic diffusion of UCA under ultrasound can be seen in Figure 31. The stability of the synthesized UCAs was measured by ultrasound imaging over a 2 hours period. The characteristics of each UCA microbubbles under ultrasound can be seen in Figure 7. As expected, the presence of a small amount of UCA increases the reflection so that the average brightness on ultrasound imaging. It was seen that very dilute dispersions of microbubbles do present, as individual white dots of ultrasound scatterers that were consistent with the ultrasound imaging were individual microbubbles.

Additionally, the average brightness decreases exponentially with time due to the breakdown of microbubbles in the degassed water. The main reason for the different maximum brightness may be the relatively small density of microbubbles in the degassed water.

As UCAs microbubbles are generally not intended to be used after a long period of time, the long-term stability of the agent was evaluated to verify the imaging capabilities of the synthesized UCAs. Figure 31 shows the durability of the

71 average brightness over time (0, 1, 3, 5, 7, 9 min, 1 hr. and 2hrs) after the injection of the UCA microbubbles via hypodermic needle as shown in figure. The echogenicity of microbubbles shell show significant reductions after 2 hours, suggesting that they should be used in experiments on the day of amalgamation within 1 hour of synthesis (see Figures 7 and 8).

Figure 31: The characteristic diffusion of UCA microbubbles by ultrasound in degassed water

4.1.3 Flow phantom experiments

Three different flow phantoms were tested. B- Mode ultrasound acquisitions were obtained in two imaging planes as shown in table 4 and 5 of Materials &

Methods section. With every bolus injections and five different flow-rates, around

70 real-time B-mode ultrasound video clips were recorded and stored for off-line analysis. As the contrast agent is entering the phantom, the concentration of the microbubbles is plotted as a function of time. At each time instance, intensities of the backscattered signals from the microbubbles within the vessel are averaged and the average value becomes a point in the time-intensity curve shown in the upper right panel. This process continues at all times until a complete time-intensity curve is generated. For the off-line analysis of the B-mode data, the intensities from the manually chosen regions-of-interest (ROI) were measured. Because of the stable experimental setting, the ROI we chose was on the similar positions during all different flow rates for single-vessel phantoms. While different ROIs were chosen for multi-vessel and tumor vessel phantoms depending on the proper visualization of multiple vessels and tumor arteries in the phantoms respectively. Time-intensity curves figure 32 (b) was generated for all three types of phantom real-time video clips in ROIs as shown if figure 32 (a).

The curve can be divided into three regions. The first region on the curve shows the intensity plateau of approximately 10 s and it corresponds to a case where there are no microbubbles, usually before the injection of contrast microbubbles. The second region is from 10 s to 30 s and it relates to injection of contrast with a complete mixing and dilution of microbubbles. Here, the time- intensity curve increases to the maximum due to the wash-in of the contrast followed by a third region which relates to the decrease of the curve due to the gradual wash-out of the contrast.

Figure 32 (a): ROIs drawn from still B-mode images of a single-vessel flow

phantom

Figure 32 (b): Time intensity curve extracted from ROIs

To achieve more quantitative description of the curve shape, three parameters were studied which are of critical interest for all application areas.

Peak of Enhancement (PE): It is the time when contrast enters the ROI and the signal intensity rises above the baseline intensity (Ibase).

Initial time of Enhancement (ET): This is the time of steepest slope representing the highest enhancement rate during the contrast wash-in. It is mainly characterized by tissue vascularization.

Time to Peak (TTP): It is the point in time where the contrast washes in and the signal enhancement actually starts till it reaches the first peak of intensity.

Tmax: the volume of the interstitial space the time period between the first peak of intensity and the maximum enhancement mainly characterizes it.

Washout rate: It is the point of time where the curve gradually starts to decrease.

After this diffusion rate of microbubbles drops completely.

The time-intensity curves were further analyzed in MATLAB to calculate the slope. This parameter refers to the Up-slope of the signal-intensity curve during the early phase of contrast enhancement as shown in figure 32 (b). The curve region is considered crucial for the actual quantification of the flow. Three points on the curve selected are averaged and the resulting slope is calculated as shown in figure

33.

Figure 33: Calculation of slope in the region of initial time of enhancement (ET)

4.1.3.1 Single Vessel Flow Phantom:

The plots of time intensity curves for ROI imaged according to the parameters discussed in table 4 are shown in appendix section. The intercepts on the curve close to the first peak intensity regions are not considered. The product of the flow rate and the slope corresponds to the volume of a single vessel flow phantom. The results are shown in the last column with an average of 23.84 mL.

Table III: Summary of the derived slopes of the time-intensity curves with different flow-rates and imaging planes within a single-vessel flow phantom is shown below

Flow Rate Probe Slope (s) Average Mean

(FR) (mL/min) Position S1 S2 S3 Slope Slope

(s) (s)

10 Longitudinal 0.59 0.6 0.51 0.56

10 Lateral 0.45 0.45 0.505

20 Longitudinal 0.34 0.5 0.54 0.46

20 Lateral 0.64 0.64 0.550

30 Longitudinal 0.64 0.65 0.52 0.60

30 Lateral 0.77 0.77 0.705

40 Longitudinal 0.90 0.82 0.85 0.85

40 Lateral 0.61 0.61 0.730

50 Longitudinal 0.96 0.77 0.92 0.88

50 Lateral 1.09 1.09 0.985

The slopes associated with the single-vessel phantom are plotted as a function of the flow rate is shown in figure 34. The slopes were derived from least- square fitted exponential time-intensity curves based on the imaging measurements. The correlation coefficient between the mean slope measurements and the least squares fitted line is all higher than 0.95, indicating a good linear relationship between the mean slope and the flow-rates. For all data, the mean

78 proportional difference of the slope measurements and flow-rate was +0.695 ± 0.18

(mean ± SD).

Single-vessel Slope Linear (Single-vessel Slope ) 1.2

1 y = 0.0114x + 0.353 R² = 0.91168

0.8

0.6

Slope (s) Slope 0.4

0.2

0 0 10 20 30 40 50 60 Flow-rate (mL/min)

Figure 34: Scatter diagram shows correlation between flow-rate measurements and mean slope measurements at 5 different flow-rates in two imaging planes (n= 5). Regression analysis showed good linear correlation between mean slope measurements and flow-rates (r= .95, y= 0.0114x + 0.353).

4.1.3.2 Multi-Vessel Flow Phantom:

The multi-vessel flow phantom was validated using an experimental set up as discussed in the method section and parameters shown in table 4. Time-intensity curves figures 32 (a and b) were generated for multi-vessel phantom in ROIs as shown if figure 32 (a). The plots of time intensity curves for ROI imaged according to

79 the parameters discussed in table 4 are shown in appendix section. The intercepts on the curve close to the first peak intensity regions are not considered.

Table IV: Summary of the derived slopes of the time-intensity curves with different flow-rates and imaging planes within a single-vessel flow phantom

Flow Rate Imaging Slope (s) Average Mean

(FR) plane S1 S2 S3 S4 Slope Slope

mL/min (s) (s)

10 Longitudinal 0.3 0.15 0.16 0.15 0.19

10 Lateral 0.29 0.19 0.25 0.41 0.28 0.235

20 Longitudinal 0.17 1.10 1.00 0.64 0.36

20 Lateral 1.08 0.51 0.46 0.14 0.36 0.36

30 Longitudinal 0.44 0.53 0.28 0.47 0.43

30 Lateral 0.21 0.09 0.17 0.18 0.16 0.295

40 Longitudinal 0.57 0.19 0.37 0.46 0.41

40 Lateral 0.45 0.39 0.39 0.38 0.40 0.405

50 Longitudinal 0.44 0.40 0.31 0.28 0.73

50 Lateral 0.51 0.42 0.25 0.28 0.55 0.64

The calculated values of early slope associated with the time-intensity curves within the multi-vessel phantom are plotted as a function of the flow rate is shown in figure 35. The slope values were derived from least-square fitted exponential

80 time-intensity curves based on the selected ROIs. The correlation coefficient between the mean slope measurements and the least squares fitted line is 0.86, indicating a good correlation between the mean slope and the flow-rates.

For all data, the mean proportional difference of the slope measurements and flow-rate was + 0.387 ± 0.15 (mean ± SD).

Multi-vessel Slope Linear (Multi-vessel Slope) 0.8

0.7

0.6 y = 0.0086x + 0.1305 R² = 0.7565 0.5

0.4

Slope (s) Slope 0.3

0.2

0.1

0 0 10 20 30 40 50 60 Flow-rate (mL/min)

Figure 35: Scatter diagram shows correlation between flow-rate measurements and mean slope measurements at 5 different flow-rates in two imaging planes (n= 5). Regression analysis showed good linear correlation between mean slope measurements and flow-rates (r= .86, y= 0.0086x + 0.1305).

4.2.2.1 Slopes vs. Flow-rate for Single vessel and multi-vessel flow phantoms

The correlation coefficient between the mean slope measurements and the least squares fitted line is 0.90, indicating a good linear correlation between the single- vessel and Multi-vessel flow phantoms.

Single-vessel Slope Multi-vessel Slope 1.2 0.7

1 0.6

0.5

0.8 0.4 0.6 0.3 Slope (s) Slope 0.4 0.2

0.2 0.1

0 0 0 10 20 30 40 50 60 Flow-rate (mL/min)

Figure 36: Mean slope measurements derived from Time intensity curves of single- vessel and Multi-vessel in two imaging planes at 5 different flow-rates. (n=5, mean ± SD, were P≤0.05)

4.1.4 Measurement of Intensity (Maximum Intensity, Imax) at Different flow- rates

Table V: The maximum intensity vs. Flow-rates in single vessel Phantom.

Flow rate Imaging Maximum Intensity Mean Intensity

mL/min Plane Imax (AUC) AUC

10 Longitudinal 38

10 Lateral 29 33.5

20 Longitudinal 22.5

20 Lateral 27 24.75

30 Longitudinal 38.5

30 Lateral 22 30.25

40 Longitudinal 34

40 Lateral 15 24.5

50 Longitudinal 36

50 Lateral 18 27

Mean Intensity Expon. (Mean Intensity )

15 y = 31.733e-0.004x 10 R² = 0.2699

5 Maximum Intensity Imax Intensity Maximum 0 0 10 20 30 40 50 60 Flow-rate (mL/min)

Figure 37: Maximum intensity vs. Flow-rates in single vessel Phantom. Regression analysis showed good linear correlation between mean slope measurements and flow-rates (r= .52, A= 31.733, β =0.004) while r=0.52 and P=0.79

The table 8 The signal intensity versus pulsing interval plots were fitted to an exponential function y=A(1−e−βt), where A is the plateau video intensity reflecting the micro vascular cross-sectional area, and β reflects the rate of rise of signal intensity and, hence, microbubble velocity. To normalize intensity values they were subjected to linear regression analysis. Closer correlations were found between flow-rates and the signal intensity values with r=0.52 and P≥0.05.

After 10 2 s, the individual time-intensity curves in figure 37 with different flow-rates showed an increase of the signal intensity to a maximum, followed by a decrease of the curve, due to wash-out of the contrast. Along with the increase in the

84 flow-rate, there was an increase of the signal intensity. Further increase of the flow- rate did not lead to any increase in the peak signal intensity.

Table VI: The maximum intensity vs. Flow-rates in a multi-vessel Phantom

Flow rate Imaging Mean Intensity Average

mL/min Plane Imax (AUC) Intensity

AUC

10 Longitudinal 21

10 Lateral 25 23

20 Longitudinal 24

20 Lateral 28 36

30 Longitudinal 34

30 Lateral 17 31

40 Longitudinal 26

40 Lateral 22.5 21.5

50 Longitudinal 19.8

50 Lateral 16.5 18.5

Mean Intensity Expon. (Mean Intensity )

35 30 25 20 15 y = 33.55e-0.01x 10 R² = 0.3022

5 Maximum Intensity Imax Intensity Maximum 0 0 10 20 30 40 50 60 Flow-rate (mL/min)

Figure 38: Maximum intensity vs. Flow-rates in Multi-vessel Phantom. Regression analysis showed good linear correlation between mean slope measurements and flow-rates (r= .52, A= 33.55, β =0.01) while r=-0.51 and P=0.62

4.1.5 Ultrasound based velocity measurements

For flow velocity estimation, the single-vessel phantom experimental setup as shown in Figure 3 and 4 were considered. The video clips of the microbubbles injection recorded for the first experiment were analyzed in MATLAB to obtain the time constant (t) of the fluid microbubbles flowing at different flow-rates specific velocity. To calculate time (t), two ROIs were selected. First region corresponds to the time of initial time of enhancement of the microbubble wash-in and the second region relates to the time curve gradually started to descend to baseline due to microbubble washout. To estimate the time-delay, two points T1 and T2 were

86 selected on the curve, which displayed time delay between the times of initial enhancement of the microbubble for both the ROIs. By subtracting T1 from T2, time- delay was calculated as (t). This was repeated for images at different flow-rates 10,

20, 30, 40 and 50 mL/min. For distance calculation, the actual physical distance between the two ROIs was measured to estimate the distance as shown in the figure.

The ultrasound based velocity measurements from the time-intensity curves across a longitudinal view of ROIs of the vessel in the flow phantom at flow rates of 10, 20,

30, 40 and 50 mL/min various parameters were measured.

Table VII: The summary of the ultrasound based velocity measurements derived from the distance between the two ROIs and the time difference (T2-T1) between the times of delay of initial time of enhancement on the time-intensity curves with different in a longitudinal view within a single-vessel flow phantom.

Flow rate Distance Time Flow Velocity

mL/ min (mm) (t) (sec) (mm/s)

10 5.8 5 1.16

20 5.83 6 0.96

30 5.24 2 2.62

40 6 5 1.20

50 6.02 3 2

The velocity profile obtained from a phantom is shown in the figure. The peak velocity was located at the center of the vessel, decreasing to a minimum at the vessel walls. There was an increase of the velocity along with the increase in the flow-rate. Further increase of the flow-rate did not lead to any increase in the velocity.

Flow Velocity Linear (Flow Velocity)

2.5 y = 0.0192x + 1.012 2 R² = 0.1878

1.5

0.5 Flow Velocity (mm/s) Velocity Flow 0 0 10 20 30 40 50 60 Flow-rate (mL/min)

Figure 39: Ultrasound based velocity measurements vs. Flow-rate in Single-vessel Phantom. Regression analysis showed no statistical difference between the flow velocity measurements and flow-rates (y= .0192x + 1.012) while correlation coefficient =0.65 and P≤0.05

Figure 40: When performed in conjunction with a mammogram, the SonoCiné ultrasound can reveal cancers that may otherwise be missed

4.1.6 Tumor vasculature flow phantoms:

Figure 40 shows invasive breast cancer scanned with contrast-enhanced ultrasound imaging, which was not visible to either a mammogram or sonogram.

Mammography finds most breast cancers, but in some subset of women who have dense breasts, it only finds about half of the cancers. Contrast-enhanced Ultrasound has been a very good tool at seeing cancers when you know where they are, but it had not been used for finding breast cancers in normal breasts.

Enhancement refers to a process by which lesions revealed on an ultrasound image increases in contrast at a specific rate over a given short-time interval, which indicates increased vascularity to the area. A neoplasm will tend to have an increased vascularity when compared to normal breast tissue. However, contrast

89 enhancement is not specific to malignant tumor. Many benign breast tissues can exhibit variable degrees of contrast enhancement as well. Contrast enhancement associated with cancerous tissue is differentiated from benign-type enhancement through the use of Dynamic contrast-enhanced ultrasound technique.

The ultrasonography was initially performed in B mode to identify the focal lesion. The area to be imaged was identified before the injection according to 3 rules: (1) the whole tumor should be seen on the image, if possible; (2) the sectional plane should contain the solid part (wall, septa, and papillae) of the tumor; and (3) the most vascularized area should be selected by ultrasound imaging. Then a dose of

1mL contrast microbubbles diluted in 500 mL degassed water was injected as a rapid bolus through the phantom. This was not followed by saline flush. The first set of experiments was performed on tumor region 1, to study the relationship between in-flow and out-flow vs. flow rates. The fluid mixture in the phantom was allowed to flow in the tumor to measure the in-flow. The in-flow was measured in longitudinal direction, at five flow-rates 10, 20, 30, 40 and 50 mL/min. Now the orientation of the probe was changed to lateral direction. The fluid mixture in the phantom was now allowed to flow out of the tumor to measure the out-flow. The out-flow was also measured in two directions, longitudinal and lateral at five flow-rates 10, 20,

30, 40 and 50 mL/min as shown in table xx. The same set up was maintained for two more set of experiments that were performed on tumor region 2 and tumor region 3 as shown table 5. The sequence of recording the raw data of the contrast in- flow and out-flow lasted at least 15 seconds or until the uptake disappeared completely when the pump was turned off.

Theoretically, cancerous tissue will show a more than 70% increase in signal intensity over baseline also known as initial enhancement curve, within the first 60-

90 seconds because of large vessels in the tumor. This marked increase in signal intensity is followed by a wash out phase, which is the result of increased vascular permeability. On a time vs. signal-intensity curve, the percentage of maximal signal increase will tend to correlate very well to the density of the micro-vessel count. It is more common to encounter a higher ratio of micro-vessels in the tumor periphery rather than in the tumor center when the tumor is malignant and not benign. Hence, as a part of preliminary study of the tumor vessel phantoms, we chose to analyze the in-flow and out-flow occurring at the tumor periphery under five different flow- rates.

The region with the greatest vascularization density was targeted as the ROI as shown in the figure. The signal intensity vs. time of the contrast in-flow and out- flow to the tumor was plotted graphically in both longitudinal and lateral imaging planes.

Tumor 1

Table VIII: Summary of Results obtained from the experimental parameters as discussed in table 5 is shown in the table

Flow Rate Imaging Inflow Average Outflow Average

(FR) Plane (mL) In-flow mL Out-flow

mL/min (mL) (mL)

10 Longitudinal 0.03 0.14

10 Lateral 0.03 0.03 0.51 0.32

20 Longitudinal 0.06 0.46

20 Lateral 0.03 0.04 0.57 0.51

30 Longitudinal 0.02 0.07

30 Lateral 0.22 0.12 0.5 0.15

40 Longitudinal 0.01 0.19

40 Lateral 0.01 0.01 0.09 0.34

50 Longitudinal 0.11 0.08

50 Lateral 0.01 0.06 0.75 0.12

Tumor 1 Inflow at Different Flow rates 0.16

0.14

0.12

0.1

0.08 Slope Inflow Slope 0.06

0.04

0.02

0 1 2 3 4 5 -0.02 Flow-rate mL/min

Figure 41: Up-slope values of in-flow vs. flow-rate in tumor region 1

Tumor 1 Outflow at Different Flow rates 0.7 0.6

0.5

0.4 0.3 Slope Slope Outflow 0.2 0.1 0 1 2 3 4 5 Flow-rate mL/min

Figure 42: Up-slope values of out-flow vs. flow-rate in tumor region 1

Tumor 1 Inflow & Outflow VS Flowrate 0.6 0.14

0.5 0.12

0.1 0.4

0.08 0.3

Slope 0.06 Slope Outflow 0.2 Slope Inflow 0.04

0.1 0.02

0 0 1 2 3 4 5 Flow rates

Figure 43: Up-slope values of in-flow and out-flow vs. flow-rate in tumor region 1

The parameters obtained from tumor region 1 after contrast injection showed almost same arrival time and the signal intensities showed an undefined pattern.

The results were consistent for the other tumor regions (2 and 3) also.

Tumor 2

The region with the greatest vascularization density in second tumor region, was targeted as the ROI as similar to tumor region 1. The signal intensity vs. time of the contrast in-flow and out-flow to the tumor was plotted graphically in both longitudinal and lateral imaging planes.

Table IX: Results obtained from the parameters discussed in table 5 are shown in the table for tumor region 2

Flow Rate Probe Inflow Average Outflow Average

(FR) Position In-flow Out-

flow

10 Longitudinal 0.52 0.14

10 Lateral 0.05 0.28 0.51 0.32

20 Longitudinal 0.25 0.46

20 Lateral 0.7 0.47 0.57 0.51

30 Longitudinal 0.01 0.07

30 Lateral 0.08 0.04 0.5 0.28

40 Longitudinal 0.05 0.19

40 Lateral 0.64 0.34 0.09 0.14

50 Longitudinal 1.13 0.08

50 Lateral 0.12 0.62 0.75 0.41

Tumor 3

Table X: Summary of Results obtained from the experimental parameters as discussed in table 5 for tumor region 3

Flow Rate Imaging Inflow Average Outflow Average

(FR) (mL/min) Plane (mL) In- Out-

flow(mL) flow(mL)

10 Longitudinal 0.03 0.06

10 Lateral 0.09 0.06 0.13 0.09

20 Longitudinal 0.06 0.19

20 Lateral 0.09 0.07 0.08 0.13

30 Longitudinal 0.15 0.08

30 Lateral 0.18 0.16 0.06 0.07

40 Longitudinal 0.14 0.13

40 Lateral 0.06 0.10 0.08 0.10

50 Longitudinal 0.06 0.88

50 Lateral 0.08 0.07 0.15 1.03

4.1.7 Computed Tomography (CT): Comparative study of Vessel diameter measurement

4.1.7.1 Data Analysis

The standard deviation of relative diameters was calculated from CT data and ultrasound images for visible vessels in each phantom. The differences in relative diameter changes between CT and ultrasound measurements were first computed. Relative diameter changes in millimeters were calculated as

[(DUltrasound (std) – DCT)/DCT]

(Where DUltrasound is maximum diameter of US measurements and DCT is relative diameter of CT scan measurements) from the CT data, were compared with the measurements obtained with the ultrasound reference standard diameter as shown in figure.

The minimum and maximum ultrasound diameter measurements ranged from 0.03 to 1.09 mm of the vessels in three types of phantoms as shown in figure

(44 to 51), whereas the corresponding minimum and maximum CT diameter measurements ranged from 0.93 to 4.49 mm. Compared to ultrasound reference standard measurement 0.04 mm, the CT diameter measurements for single-vessel diameter changes ranged from 0.93 to 2.73 mm, multi-vessel diameter changes ranged from 1.43 to 2.08 mm and tumor-vessel diameter changes ranged from 0.93 to 2.73 mm, with overall standard deviation of SD = 0.123.

Table XI: Summary of standard deviation of Relative Diameter Measurements with Difference in Relative Diameter Change between Optical and CT Measurements

Phantoms Ultrasound CT scan (mm) Relative Relative

diameter (mm) Diameter change change (mm) CT (mm) D2 D1

Std min max (Min-max)/min D1-D2/D1

Single-vessel 0.04 0.93 2.53 0.63 0.93

Single-vessel 0.04 1.18 2.73 0.45 0.911

Multi-vessel 0.03 1.43 2.61 0.45 0.93

Multi-vessel 0.04 1.21 3.08 0.60 0.93

Tumor- 1.09 1.49 4.49 0.66 0.65

vessel

Sample Standard Deviation, s 0.12337017467767 Variance (Sample Standard), s2 0.0152202 Population Standard Deviation, σ 0.11034563879012 Variance (Population Standard), σ2 0.01217616 Total Numbers, N 5 Sum: 4.351 Mean (Average): 0.8702

Figure 44: CT image of Single-vessel diameter measurement in phantom 1

Figure 45: CT image of Single-vessel diameter measurement in phantom 2

Figure 46: CT image of multi-vessel diameter measurement in phantom 1

100

Figure 47: CT image of multi-vessel diameter measurement in phantom 2

Figure 48: CT image of tumor-vessel diameter measurement in tumor-vessel phantom

101

Figure 49: US image of Single-vessel diameter measurement in phantom 1 and 2

Figure 50: US image of Multi-vessel diameter measurement in phantom 1 and 2

102

Figure 51: US image of tumor-vessel diameter measurement in tumor-vessel phantom

4.2 Discussion

Currently, the most popular microbubble shell design for a blood pool contrast agent is one clinically approved and prepared based on lipid shell design.

The sole protein-based approved agent Optison/FS069, with the formal generic name ‘‘Perflutren protein microspheres’’, contains human albumin shell and perfluoropropane core. Preparation of these microbubble agents is often achieved by high shear gas dispersion in the aqueous mixtures. In case of one of the contrast bubble materials, Definity and MRX-115 (perflutren lipid microspheres), aqueous phase with lipid mixture is packaged in a vial with perfluoropropane gas headspace,

103 stopper sealed and stored in the refrigerator. The sealed vial is subjected to high- speed shaking for 45 s for microbubble generation immediately. General design of the microbubble contrast agent is simple: a gas core that is surrounded by a stabilizer shell, with the whole structure dispersed in an aqueous medium

(degassed water) and injected into the phantoms. Microbubble contrast agents, in the form of intravenous injectable, are now routinely used as markers of blood flow.

During initial work in the creation of UCA microbubbles, it was determined through analysis with both optical microscopy and a cell counter that dilution rates need to be controlled according to the density of microbubbles. In this study the dilution rate of microbubbles was set to a constant concentration of 1 mL: 500 mL water.

Also, during initial work in the imaging of UCA microbubbles with ultrasound, it was determined that the microbubbles were not completely mixed when the constant flow was used. Thus, the absolute flow quantification is not accurate unless the effective mixing volume can be found. The differences in characteristics of the time-intensity curves obtained based on the concentration of microbubbles, may require different signal-processing techniques for the mixing of microbubbles in the two types of phantoms. The derived Up-slope values were observed to be related to volume, velocity and peak image intensity. Hence, it was determined that the slope alone is not sufficient for quantitative flow analysis, even if it can be accurately obtained. In other words, the time-intensity technique can only be applied for relative flow analysis if no additional information on the volume or the flow rate is available. For example, it was assumed that the volume in both the phantoms is fixed and, thus, that the time constant is inversely proportional to

104 the volume flow rate distance by velocity. The estimation of the volume was reasonably accurate for the both phantom for a volume up to 50 mm3. Such information is expected to be critical in clinical applications of the time-intensity- based quantitative flow analysis techniques. Alternatively, if the volumetric flow rate can be obtained by other techniques (e.g., conventional Doppler techniques), then the effective volume can be calculated. The derived volume may be further directly related to perfusion of a particular organ.

In the flow phantoms, the signal intensity increased in flow rate from 10 mL/min to 30 mL/min; further increase of flow-rate did not lead to any increase of the signal intensity. The results showed that the intensity increased by approximately 3 X as a function of dose increase from 10 to 30 mL/min. Further increase of the dose did not have influence on the intensity; in fact, a slight decrease of the intensity was observed. A possible explanation for these phenomena is microbubble saturation. This effect occurs when UCA microbubbles are too close together and multipath reverberations minimize backscatter signal.

A significant increase in ultrasound intensity was observed as flow-rate was increased from 10 mL/min to 30 mL/min. The flow-rates beyond 30mL/min did not lead to further increase of the intensity. Using low flow-rates, the intensity was of particular interest, describing the indicator volume in the microcirculation. The intensity shows a high variation, but no systematic change by increasing flow rates in the range from 30 to 50 mL/min. These results were in correspondence with the dye-dilution theory, where the intensity was directly related to the contrast volume

105 because the amount of vessels in the flow phantom was not changed during the experiment.

In the results with tumor vessel phantoms, measurement of both the in-flow and the out-flow of the ROI was found to be problematic, and may prove to be practically difficult in clinical situations using standard linear imaging probes. In other words, it may be difficult to place both the artery and the vein of an organ in the same image plane. Thus, 3-D imaging may be beneficial in generating the input/output time intensities. Another complication is that many organs have multiple input vessels and multiple output vessels (e.g., liver and brain). Thus, the single input/single output model used in this paper also needs to be modified.

106

CHAPTER V

Conclusions & Recommendations

5.1 CONCLUSIONS

Through past research it has been discovered that conventional flow phantoms are not easily reproducible and prohibitively difficult or impossible alternative to construct. Although many synthetic materials have been used, vascular flow phantoms have yet to be researched as a possible system to create realistic three- dimensional flow and vascular structure to promote research for evaluations of cancer treatments and research in pre-clinical settings.

In this study three types of vascular flow phantoms were developed and studied for the quantification of blood flow. After successful fabrication of the agar- tissue mimic material within the concept of wall-less vessels, the time-intensity

107 curves were determined and average slope; mean intensity and average velocity of the flow was derived. Average pore size, scaffold porosity and water up taking capacity were analyzed and found to be comparative to porous scaffold used previously in research that promote cellular adhesion, proliferation and differentiation for tissue engineering application. In addition, the flow-rates were tested to investigate the slope of the intensities. The correlation coefficient between the mean slope measurements and the least squares fitted line was all higher than

0.95, indicating a good linear relationship between the mean slope and the flow- rates. For all single-vessel flow phantom data, the mean proportional difference of the slope measurements and flow-rate was +0.695 ± 0.18 (mean ± SD).

It was found that the correlation coefficient between the mean slope measurements and the least squares fitted line is 0.90, indicating a good linear correlation between the single-vessel and Multi-vessel flow phantoms. However, the parameters obtained from tumor region 1, 2 and 3 did not show any significant flow pattern and correlation after contrast injection of UCAs.

Finally, the measured intensity at the tumor regions is used to represent concentration in this study. Although such an assumption has been used in many previous studies, some studies have also demonstrated the nonlinear relationship between the bubble concentration and the measured intensity. Thus, the initial concentration of the bolus injection needs to be carefully controlled. Otherwise, the linear system model will not be valid. More information can be obtained when intensities at both the input and the output are measured. For further studies, time

108 intensities acquired at the input vessel and the output vessel may also be correlated with the perfusion measurements acquired directly from the mixing chamber.

Again, alternative modeling of the problem is required because such a flow system has multiple input vessels and multiple output vessels. Developing new time-an intensity-based technique is the focus of current research.

5.2 RECOMMENDATIONS

While the phantom generation technique presented herein is a novel means to more accurately construct microvasculature flow phantoms for ultrasound imaging studies, there are several improvements of the technique worth noting.

First and foremost, it must be implemented in a water bath, as the tube needs a smooth acoustically transparent medium through which to move. This then makes it necessary to add speckle noise as a post-processing step if one is interested in analyzing vessels in a more realistic imaging context. Since the process of building up vessel networks presented in this manuscript is via simple vessel mold retraction in a wall-less agar, the signal to noise ratio (SNR) between the vessels and the background noise in the final composite vessel images is spatially variant as a function of how many vessel signals are added together at a given location. The noise power increases uniformly throughout the image volume of the composite vessel phantom as each individual vessel phantom is added to the composite image; the signal power within the vessels does not always increase at all locations,

109 however. In some locations of a composite image multiple vessels can be present, such as in the region before a bifurcation, and thus when they are added the final

“vessel signal” increases N times (where N is the number of vessels present at a location). After vessels bifurcate and trace unique trajectories through the image space, their signals do not add any longer and thus are reduced relative to the increased noise power. While this was not accounted for in these preliminary proof- of-concept imaging studies, one could compensate for this by characterizing the noise of the imaging system and normalizing the noise to the vessel signals in a post- processing step after the composite vessel networks had been constructed, as the centerlines of each vessel within the network are known.

An additional recommendation to improve the phantom generation technique is if one is interested in achieving non-trivial velocity streamlines within a vessel phantom generated by the method presented herein; additional degrees of freedom must be added to their experimental apparatus. For instance, if one wanted to simulate a change in flow direction at a bifurcation they would need to rotate the transducer relative to the tube at the appropriate location along the vessel’s centerline (or equivalently: rotate the tube beneath the transducer). This would become increasingly difficult in situations where a vessel’s trajectory varied in both the X and Y dimensions. As the transducer would need to undergo two independent rotations. While not impossible, it is likely that this type of imaging procedure would be prohibitively difficult. Thus, we propose this phantom generation method as primarily of interest for those interested in assessing complex vessel morphology.

110

BIBLIOGRAPHY

[1] Lipowsky H. H., Zweifach B. W., “Network analysis of microcirculation of cat mesentery,” Microvasc. Res. 7(1), 73–83 (1974).10.1016/0026-2862 (74)90038-7

[2] Kety S. S., Schmidt C. F., “The nitrous oxide method for the quantitative determination of cerebral blood flow in man: theory, procedure and normal values,”

J. Clin. Invest. 27(4), 476 483 (1948).10.1172/JCI101994

[3] Chen Z., Milner T. E., Dave D., Nelson J. S., “Optical Doppler tomographic imaging of fluid flow velocity in highly scattering media,” Opt. Lett. 22(1), 64–66

(1997).10.1364/OL.22.000064

[4] Izatt J. A., Kulkarni M. D., Yazdanfar S., Barton J. K., Welch A. J., “In vivo bidirectional color Doppler flow imaging of picoliter blood volumes using optical coherence tomography,” Opt. Lett. 22(18), 1439–1441

(1997).10.1364/OL.22.001439

[5] Microfluidics based phantoms of superficial vascular network Long Luu,1 Patrick

A. Roman,3 Scott A. Mathews,2 and Jessica C. Ramella-Roman1

[6] Molls M, Vaupel P (Eds). Blood Perfusion and Microenvironment of Human

Tumors: Implications for Clinical Radiooncology. Springer, NY, USA (1998).

111

[7] Imaging innovations for cancer therapy response monitoring Sadeghi-Naini,

Falou, and Hudson et al.

[8] Grunwald J. E., DuPont J., Riva C. E., “Retinal haemodynamics in patients with early diabetes mellitus,” Br. J. Ophthalmol. 80(4), 327–331

(1996).10.1136/bjo.80.4.327

[9] Carmeliet P., Jain R. K., “Angiogenesis in cancer and other diseases,” Nature

407(6801), 249–257 (2000).10.1038/35025220

[10] Dauzat M., Laroche J. P., Deklunder G., Ayoub J., Quére I., Lopez F. M., Janbon C.,

“Diagnosis of acute lower limb deep venous thrombosis with ultrasound: trends and controversies,” J. Clin. Ultrasound 25(7), 343–358 (1997).10.1002/(SICI)1097-

0096(199709)25:7<343::AID-JCU1>3.0.CO;2-A

[11] Hatab M. R., Giller C. A., Clarke G. D., “Evaluation of cerebral arterial flow with transcranial Doppler ultrasound: theoretical development and phantom studies,”

Ultrasound Med. Biol. 23(7), 1025–1031 (1997).10.1016/S0301-5629(97)00030-6

[12] Zakharov P., Völker A. C., Wyss M. T., Haiss F., Calcinaghi N., Zunzunegui C., Buck

A., Scheffold F., Weber B., “Dynamic laser speckle imaging of cerebral blood flow,”

Opt. Express 17(16), 13904–13917 (2009).10.1364/OE.17.013904

[13] Dunn A. K., “Laser speckle contrast imaging of cerebral blood flow,” Ann.

Biomed. Eng. 40(2), 367–377 (2012).10.1007/s10439-011-0469-0

112

[14] Ponticorvo A., Dunn A. K., “Simultaneous imaging of oxygen tension and blood flow in animals using a digital micromirror device,” Opt. Express 18(8), 8160–8170

(2010).10.1364/OE.18.008160

[15] Yao J., Maslov K. I., Shi Y., Taber L. A., Wang L. V., “In vivo photoacoustic imaging of transverse blood flow by using Doppler broadening of bandwidth,” Opt. Lett.

35(9), 1419–1421 (2010).10.1364/OL.35.001419

[16] Riva C., Ross B., Benedek G. B., “Laser Doppler measurements of blood flow in capillary tubes and retinal arteries,” Invest. Ophthalmol. 11(11), 936–944 (1972).

[17] Feke G. T., Rivat C. E., “Laser Doppler measurements of blood velocity in human retinal vessels,” J. Opt. Soc. Am. 68(4), 526–531 (1978).10.1364/JOSA.68.000526]

[18] Duncan D. D., Lemaillet P., Ibrahim M., Nguyen Q. D., Hiller M., Ramella-Roman J.

C., “Absolute blood velocity measured with a modified fundus camera,” J. Biomed.

Opt. 15(5), 056014 (2010).10.1117/1.3494565

[19] Singh A. S., Kolbitsch C., Schmoll T., Leitgeb R. A., “Stable absolute flow estimation with Doppler OCT based on virtual circumpapillary scans,” Biomed. Opt.

Express 1(4), 1047–1058 (2010).10.1364/BOE.1.001047

[20] Hamilton WF, Moore JW, Kinsman MJ, Spurling RG. Studies on the circulation.

IV. Further analysis of the injection method, and of changes in hemodynamics under physiological and pathological conditions. Am J Physiol. 1932:99: 534–551.

[21] Kinsman JM, Moore JW, Hamilton WF. Studies on the circulation. I. Injection method: physical and mathematical considerations. Am J Physiol. 1929;89:322–330.

113

[22] Estimation of intra-operator variability in perfusion parameter measurements using DCE-US Ingrid Leguerney, Jessie Thalmensi, Mohamed Chebil, Sarah

Parisot, Pierre Peronneau, Alain Roche, and Nathalie Lassau

[23] Time-intensity-based volumetric flow measurements: an in vitro study Pai-Chi

Li, a, , Chih-Kuang Yeha, Sheng-Wuei Wanga

[24] Volumetric tumor measurement using three-dimensional ultrasound: in vitro phantom study on measurement accuracy under various scanning conditions

Seong Ho Park*, Byung Ihn Choi*, , , Joon Koo Han*, Chang Jin Yoon†, Joon Woo

Lee*, Sam Soo Kim‡, Heon Han§

[25] Pinter SZ, Lacefield JC. Objective selection of high-frequency power Doppler wall filter cutoff velocity for regions of interest containing multiple small vessels.

IEEE Trans Med Imaging. 2010;29:1124–1139.

[26] Wang Y, Dan HJ, Fan JH, Wen SB. Evaluation of the correlation between colour power Doppler flow imaging and vascular endothelial growth factor in breast cancer. J Int Med Res. 2010;38:1077–1083.

[27] Alcázar JL, Jurado M, López-García G. Tumor vascularization in cervical cancer by 3-dimensional power Doppler angiography: correlation with tumor characteristics. Int J Gynecol Cancer. 2010;20:393–397.

[28] Palmowski M, Huppert J, Hauff P, Reinhardt M, Schreiner K, Socher MA,

Hallscheidt P, Kauffmann GW, Semmler W, Kiessling F. Vessel fractions in tumor xenografts depicted by flow- or contrast-sensitive three-dimensional high-

114 frequency Doppler ultrasound respond differently to antiangiogenic treatment.

Cancer Res. 2008;68:7042–7049.

[29] Wei K, Jayaweera AR, Firoozan S, Linka A, Skyba DM, Kaul S. Quantification of myocardial blood flow with ultrasound-induced destruction of microbubbles administered as a constant venous infusion. Circulation. 1998;97(5):473–483.

[30] Pollard RE, Larson RF. Dynamic contrast-enhanced computed tomography for the quantification of tumor response to vasoactive agents in a rat tumor model: preliminary results. Contrast Media Mol Imaging. 2010

[31] Metoki R, Moriyasu F, Kamiyama N, Sugimoto K, Iijima H, Xu HX, Aoki T, Miyata

Y, Yamamoto K, Kudo K, Shimizu M, Yamada M. Quantification of hepatic parenchymal blood flow by contrast ultrasonography with flash-replenishment imaging. Ultrasound Med Biol. 2006;32:1459–1466.

[32] Chen NG, Fowlkes JB, Carson PL, LeCarpentier GL. Rapid 3D imaging of contrast flow: demonstration of a dual beam technique. Ultrasound Med Biol. 2007;33:915–

923. [33] Shiraishi J, Sugimoto K, Moriyasu F, Kamiyama N, Doi K. Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography. Med Phys. 2008;35:1734–1746.

[34] Feingold S, Gessner R, Guracar IM, Dayton PA. Quantitative volumetric perfusion mapping of the microvasculature using contrast ultrasound. Invest Radiol.

2010;45:669–674. [35] Sugimoto K, Shiraishi J, Moriyasu F, Ichimura S, Metoki R,

Doi K. Analysis of intrahepatic vascular morphological changes of chronic liver

115 disease for assessment of liver fibrosis 17 stages by micro-flow imaging with contrast-enhanced ultrasound: preliminary experience. Eur Radiol. 2010;20:2749–

2757.

[36] Molinari F, Mantovani A, Deandrea M, Limone P, Garberoglio R, Suri JS.

Characterization of single thyroid nodules by contrast-enhanced 3-D ultrasound.

Ultrasound Med Biol. 2010;36:1616–1625.

[37] Huang SF, Chang RF, Moon WK, Lee YH, Chen DR, Suri JS. Analysis of tumor vascularity using three-dimensional power Doppler ultrasound images. IEEE Trans

Med Imaging. 2008;27:320–330. [38] Jain RK. Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science. 2005;307:58–62.

[39] Bullitt E, Ewend MG, Aylward S, Lin W, Gerig G, Joshi S, Jung I, Muller K, Smith

JK. Abnormal vessel tortuosity as a marker of treatment response of malignant gliomas: preliminary report. Technol Cancer Res Treat. 2004;3:577–584.

[40] Bullitt E, Lin NU, Smith JK, Zeng D, Winer EP, Carey LA, Lin W, Ewend MG. Blood vessel morphologic changes depicted with MR angiography during treatment of brain metastases: a feasibility study. Radiology. 2007;245:824–830.

[41] Design and characterization of a multimodality phantom for contrast enhanced ultrasound and magnetic resonance imaging Ian pang, University of Toronto, 2011

[42] Imaging biomarkers of angiogenesis and the microvascular environment in cerebral tumors G Thompson, MBChB, MRCS, S J Mills, PhD, FRCR, D J Coope, PhD,

MRCS, J P B O’connor, PhD, FRCR, and A Jackson, FRCR, FRCP

116

[43] Pogue BW, Patterson MS (2006) Review of tissue simulating phantoms for optical spectroscopy, imaging and dosimetry. J Biomed Opt 11:

041102.10.1117/1.2335429 PubMed: 16965130

[44] Development and Application of Stable Phantoms for the Evaluation of

Photoacoustic Imaging Instruments Sarah E. Bohndiek, Sandhya Bodapati,

Dominique Van De Sompel, Sri-Rajasekhar Kothapalli, and Sanjiv S. Gambhir*

[45] Development of a vessel-mimicking material for use in anatomically realistic

Doppler flow phantoms King DM1, Moran CM, McNamara JD, Fagan AJ, Browne JE.

[46] G. Cloutier, G. Soulez, S. Qanadli, P. Teppaz, L. Allard, Z. Qin, F. Cloutier and L.

Durand, "A multimodality vascular imaging phantom with fiducial markers visible in

DSA, CTA, MRA, and ultrasound," MEDICAL PHYSICS}, vol. 31}, pp. 1424-1433},

JUN}, 2004}.

[47] r. Frayne, l. Gowman, d. Rickey, d. Holdsworth, p. Picot, m. Drangova, k. Chu, c.

Caldwell, a. Fenster and b. Rutt, "a geometrically accurate vascular phantom for comparative-studies of x-ray, ultrasound, and magnetic-resonance vascular imaging

- construction and geometrical verification," medical physics}, vol. 20}, pp. 415-

425}, mar-apr}, 1993}

[48] c. Kerber and c. Heilman, "flow dynamics in the human carotid-artery .1.

Preliminary-observations using a transparent elastic model," american journal of neuroradiology}, vol. 13}, pp. 173-180}, jan-feb}, 1992}.

117

[49] w. Dabrowski, j. Dunmorebuyze, r. Rankin, d. Holdsworth and a. Fenster, "a real vessel phantom for imaging experimentation," medical physics}, vol. 24}, pp. 687-

693}, may}, 1997}

[50] Characterization of biomechanical properties of agar based tissue mimicking phantoms for ultrasound stiffness imaging techniques. Kavitha

Manickama, , Ramasubba Reddy Machireddya, Suresh Seshadrib

[51] Vascularization Strategies for Tissue Engineering Michael Lovett,

Ph.D.,1 Kyongbum Lee, Ph.D.,2 Aurelie Edwards, Ph.D.,2 and David L. Kaplan, Ph.D. 1,,2

[52] S. I. Fox, S. I. Fox. Human Physiology. Dubuque, Iowa: Wm. C. Brown, 1996.

[53] F. Martini, Fundamentals of Anatomy and Physiology. Upper Saddle River, N.J.:

Prentice Hall, 1998.

[54] Heat Stress and Baroreflex Regulation of Blood Pressure Craig g. Crandall1,2

[55] Circulatory System: Facts, Function & Diseases by Kim Ann Zimmermann, Live

Science Contributor

[56] R. F. Tuma, W. N. Durán, K. Ley and ScienceDirect (Online service).

Microcirculation. Boston: Elsevier/Academic Press, 2008.

[57] Cardiovascular Disease in the Developing World and Its Cost-Effective

Management Thomas A. Gaziano, MD, MSc

[58] Lewis, D.H. Microcirculation in Europe: is itsfitture aspron~ising as itspast? in

Sixth World Congress for Microcirculation. 1996. Munich, Germany.

118

[59] Tumor angiogenesis: past, present and the near future Robert S. Kerbel

[60] Angiogenesis in cancer and other diseases Peter Carmeliet1 & Rakesh K. Jain2

[61] Blood vessel network remodelling during tumor growth M.Weltera ,

H.Riegera,∗

[62] E. E. Voest and P. A. DAmore. Tumor Angiogenesis and Microcirculation. New

York: Dekker, 2001.

[63] J. Folkman, "Role of angiogenesis in tumor growth and metastasis," Semin.

Oncol., vol. 29, pp. 15-18, dec, 2002.

[64] j. H. Naish, l. E. Kershaw, d. L. Buckley, a. Jackson, j. C. Waterton and g. J. M.

Parker, "modeling of contrast agent kinetics in the lung using t-1-weighted dynamic contrastenhanced mri," magnetic resonance in medicine}, vol. 61}, pp. 1507-1514}, jun}, 2009}.

[65] p. Tofts and a. Kermode, "measurement of the blood-brain-barrier permeability and leakage space using dynamic mr imaging .1. Fundamental-concepts," magnetic resonance in medicine}, vol. 17}, pp. 357- 367}, feb}, 1991}

[66] F. Foster, P. Burns, D. Simpson, S. Wilson, D. Christopher and D. Goertz,

"Ultrasound for the visualization and quantification of tumor microcirculation,"

CANCER AND METASTASIS REVIEWS}, vol. 19}, pp. 131-138}, JUN}, 2000}.

119

[68] C. Greis, "Ultrasound contrast agents as markers of vascularity and microcirculation," Clinical hemorheology and microcirculation}, vol. 43}, pp. 1-9},

2009}.

[69] D. Simpson, C. Chin and P. Burns, "Pulse inversion Doppler: A new method for detecting nonlinear echoes from microbubble contrast agents," IEEE transactions on ultrasonic ferroelectrics and frequency control}, vol. 46}, pp. 372-382}, MAR},

1999}.

[70] K. Wei, A. R. Jayaweera, S. Firoozan, A. Linka, D. M. Skyba and S. Kaul,

"Quantification of myocardial blood flow with ultrasound-induced destruction of microbubbles administered as a constant venous infusion," Circulation, vol. 97, pp.

473-483, Feb 10, 1998.

[71] D. L. Buckley, "Uncertainty in the analysis of tracer kinetics using dynamic contrastenhanced T1-weighted MRI," Magn. Reson. Med., vol. 47, pp. 601-606, Mar,

2002.

[72] E. Henderson, J. Sykes, D. Drost, H. J. Weinmann, B. K. Rutt and T. Y. Lee,

"Simultaneous MRI measurement of blood flow, blood volume, and capillary permeability in mammary tumors using two different contrast agents," J. Magn.

Reson. Imaging, vol. 12, pp. 991-1003, Dec, 2000.

[73] R. K. Jain, "Taming vessels to treat cancer," SCIENTIFIC AMERICAN}, vol. 298}, pp. 56-63}, JAN}, 2008}

120

[74] R. Jain, "Normalization of tumor vasculature: An emerging concept in antiangiogenic therapy," SCIENCE}, vol. 307}, pp. 58-62}, JAN 7}, 2005}.

[75] Development and application of piezoelectric materials for ultrasound generation and detection, Amir Manbachi and Richard S C Cobbold

[76] J. T. Bushberg. The Essential Physics of Medical Imaging. Philadelphia:

Lippincott, 2002.

[77] Quantification of blood flow using ultrasound contrast agents by: John Monte

Hudson

[78] F. Foster, P. Burns, D. Simpson, S. Wilson, D. Christopher and D. Goertz,

"Ultrasound for the visualization and quantification of tumor microcirculation,"

Cancer and Metastasis Reviews}, vol. 19}, pp. 131-138}, Jun}, 2000}.

[79] C. Greis, "Ultrasound Contrast agents as markers of Vascularity and

Microcirculation," Clinical Hemorheology and Microcirculation}, vol. 43}, pp. 1-9},

2009}.

[80] D. Simpson, C. Chin and P. Burns, "Pulse inversion Doppler: A new method for detecting nonlinear echoes from microbubble contrast agents," IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control}, vol. 46}, pp. 372-382}, MAR},

1999}.

[81] K. Wei, A. R. Jayaweera, S. Firoozan, A. Linka, D. M. Skyba and S. Kaul,

"Quantification of myocardial blood flow with ultrasound-induced destruction of

121 microbubbles administered as a constant venous infusion," Circulation, vol. 97, pp.

473-483, Feb 10, 1998.

[82] J. Eyding, W. Wilkening, M. Reckhardt, G. Schmid, S. Meves, H. Ermert, H.

Przuntek and T. Postert, "Contrast burst depletion imaging (CODIM): a new imaging procedure and analysis method for semiquantitative ultrasonic perfusion imaging,"

Stroke, vol. 34, pp. 77-83, Jan, 2003. 62

[83] O. Lucidarme, Y. Kono, J. Corbeil, S. H. Choi and R. F. Mattrey, "Validation of ultrasound contrast destruction imaging for flow quantification," Ultrasound Med.

Biol., vol. 29, pp. 1697- 1704, Dec, 2003.

[84] W. Wilkening, T. Postert, J. Federlein, Y. Kono, R. Mattrey and H. Ermert,

"Ultrasonic assessment of perfusion conditions in the brain and in the liver," in.

[85] KA Miles, JD Eastwood, and M Konig, editors. Multidetector Computed

Tomography in Cerbrovascular Disease: CT Perfusion Imaging. Informa Healthcare,

UK, 2007.

[86] TJ Ryan. The coronary angiogram and its seminal contributions to cardiovascular medicine over five decades. Circulation, 106(6):752–756, Aug 2002.

[87] TY Lee. Functional ct: Physiological models. Trends in Biotechnology, 20(8):S3–

S10, 2002.

[88] TY Lee and RK Chhem. Impact of new technologies on dose reduction in ct. Eur J

Radiol, 76(1):28–35, Jul 2010.

122

[89] YV Sivintsev. Natural background radiation. Atomic Energy, 64(1):55–67, Jul

1988.

[90] Quaia, E et al, Comparison of contrastenhanced ultrasonography versus baseline ultrasound and contrast-enhanced computed tomography in metastatic disease of the liver: diagnostic performance and confidence. Eur Radiol, 2006. 16(7): p. 1599-609.

[91] R Gramiak and P Shah. Echocardiography of the aortic root. Investigative

Radiology, Jan 1968.

[92] Deshpande N, Needles A, Willmann JK. Molecular ultrasound imaging: current status and future directions. Clin Radiol 2010;65:567-81.

[93] Kircher MF, Willmann JK. Molecular Body Imaging: MR Imaging, CT, and US.

Part I. Principles. Radiology 2012;263:633-43.

[94] Kiessling F, Huppert J, Palmowski M. Functional and molecular ultrasound imaging: concepts and contrast agents. Curr Med Chem 2009;16:627-42.

[95] Gessner R, Dayton PA. Advances in molecular imaging with ultrasound. Mol

Imaging 2010;9:117-27.

[96] Microbubble Compositions, Properties and Biomedical Applications Shashank

Sirsi and Mark Borden

[97] Epstein PS, Plesset MS. J Chem Phys. 1950;18:1505–1509.

123

[98] Christiansen C, Kryvi H, Sontum PC, Skotland T. Biotechnology and Applied

Biochemistry. 1994; 19:307–320.

[99] Singhal S, Moser CC, Wheatley MA. Langmuir. 1993;9:2426–2429.

[100] Wang WH, Moser CC, Wheatley MA. Journal of Physical

Chemistry. 1996;100:13815–13821.

[101] BR38, a New Ultrasound Blood Pool Agent Michel Schneider, PhD, Brijesh

Anantharam, MD, Marcel Arditi, PhD,* Daniela Bokor, MD

[102] Frinking PJ, Bouakaz A, Kirkhorn J, et al. Ultrasound contrast imaging: current and new potential methods. Ultrasound Med Biol 2000;26:965-75.

[103] Forsberg F, Shi WT, Goldberg BB. Subharmonic imaging of contrast agents

Ultrasonics 2000;38:93-8.

[104] Schneider M, Anantharam B, Arditi M, et al. BR38, a new ultrasound blood pool agent. Invest Radiol 2011;46:486-94.

[105] Burns PN, Wilson SR, Simpson DH. Pulse inversion imaging of liver blood flow: improved method for characterizing focal masses with microbubble contrast. Invest

Radiol 2000;35:58-71.

[106] Cavalieri, F.; Ashokkumar, M.; Grieser, F.; Caruso, F. Ultrasonic synthesis of stable, functional lysozyme microbubbles. Langmuir 2008, 24, 10078–10083.

124

[107] Miller, D.L.; Gies, R.A. Gas-body-based contrast agent enhances vascular bioeffectsof 1.09MHz ultrasound on mouse intestine. Ultrasound Med. Biol. 1998,

24, 1201–1208.

[108] Simon, R.H.; Ho, S.Y.; Lange, S.C.; Uphoff, D.F.; D'Arrigo, J.S. Application of lipid- coated microbubble ultrasonic contrast to tumor therapy. Ultrasound Med. Biol.

1993, 19, 123–125.

[109] Klibanov, A.L. Preparation of targeted microbubbles: Ultrasound contrast agents for molecular imaging. Med. Biol. Eng. Comput. 2009, 47, 875–882.

[110] Sirsi, S.S.; Feshitan, J.; Kwan, J.; Homma, S.; Borden, M.A.Effect of microbubble size on fundamental mode high frequency ultrasound imaging in mice. Ultrasound

Med. Biol. 2010, 36, 935–948.

[111] Quaia, E. Microbubble ultrasound contrast agents: An update. Eur. Radiol.

2007, 17, 1995–2008.

[112] Stride, E.; Saari, N. Microbubble ultrasound contrast agents: A review. Proc.

Inst. Mech. Eng. 2003, 217, 429–447.

[113] Suzuki, R.; Takizawa, T.; Negishi, Y.; Hagisawa, K.; Tanaka, K.; Sawamura, K.;

Utoguchi, N.; Nishiolka, T.; Maruyama, K. Gene delivery by combination of novel liposomal bubbles with perfluoropropane and ultrasound. J. Controlled Release

2007, 117, 130–136.

125

[114] Santin, M.D.; King, D.A.; Foiret, J.; Haak, A.; Obrien, W.D.; Bridal, S.L.

Encapsulated contrast microbubble radial oscilation associated with postexcitation pressure peak. J. Acoust. Soc. Am. 2010, 127, 1156–1164.

[115] Goldberg, B.B.; Raichen, J.S.; Forsberg, F. Ultrasound Contrast Agent: Basic

Principles and Clinical Application, 2nd ed.; CRC Press: London, UK, 2001.

[116] Mesiwala, A.H. High-intensity focused ultrasound selectively disrupts the blood brain barrier in vivo. Ultrasound Med. Biol. 2002, 28, 389–400.

[117] Hernot, S.; Klibanov, A.L. Microbubble in ultrasound-triggered drug and gene delivery. Adv. Drug Deliv. Rev. 2008, 60, 1153–1166.

[118] Jonathan, R.; Lindner, M.D. Targeted ultrasound contrast agents: Diagnostic and therapeutic potential. IEEE Ultrason. Symp. 2001, 2, 1695–1703.

[119] Mitragotri, S. Healing sound: The use of ultrasound in drug delivery and other therapeutic applications. Nat. Rev. Drug Discov. 2005, 4, 255–260.

[120] Synthesis of Laboratory Ultrasound Contrast Agents Jingam Park 1 , Donghee

Park 1 , Unchul Shin 1 , Sanghyub Moon 1 , Chihyun Kim 1 , Han Sung Kim 1 , Hyunjin

Park 2 , Kiju Choi 3 , Bong-Kwang Jung 4 , Jaemin Oh 5 and Jongbum Seo 1,*

[121] McDannold, N.; Vykhodtseva, N.; Hynynen, K. Targeted disruption of the blood-brain barrier with focused ultrasound: Association with cavitation activity.

Phys. Med. Biol. 2006, 51, 793–807.

126

[122] Hynynen, K.; McDannold, N.; Vykhodtseva, N.; Jolesz, F.A. Noninvasive MR imaging-guided focal opening of the blood-brain barrier in rabbits. Radiology 2001,

220, 640–646.

[123] Liu, Y.; Miyoshi, H.; Nakamura, M. Encapsulated ultrasound microbubbles:

Therapeutic application in drug/gene delivery. J. Controlled Release 2006, 114, 89–

99.

[124] Ferrara, K.W.; Borden, M.A.; Zhang, H. Lipid-shelled vehicles: Engineering for ultrasound molecular imaging and drug delivery. Acc. Chem. Res. 2009, 42, 881–892.

[125] Skyba, D.M.; Price, R.J.; Linka, A.Z.; Skalak, T.C.; Kaul, S. Direct in vivo visualization of intravascular destruction of microbubbles by ultrasound and its local effects on tissue. Circulation 1998, 98, 290–293.

[126] Jong, N.; Frinking, P.J.; Bouakaz, A.; Cate, F.J. Detection procedures of ultrasound contrast agents. Ultrasonic 2000, 38, 87–92.

[127] Ferrara, K.W.; Pollard, R.; Borden, M. Ultrasound microbubble contrast agents:

Fundamentals and application to gene and drug delivery. Annu. Rev. Biomed. Eng.

2007, 9, 415–447.

[128] Cavalieri, F.; Zhou, M.; Ashokkumar, M. The design of multifunctional microbubbles for ultrasound image-guided cancer therapy. Curr. Top. Med. Chem.

2010, 10, 1198–1210.

127

[129] Rapoport, N.; Gao, Z.; Kennedy, A. Multifunctional nanoparticles for combining ultrasonic tumor imaging and targeted chemotherapy. J. Natl. Cancer Inst. 2007, 99,

1095–1106.

[130] Lindner, J.R.; Song, J.; Christiansen, J.; Klibanov, A.L.; Xu, F.; Ley, K. Ultrasound assessment of inflammation and renal tissue injury with microbubbles targeted to

P-Selectin. Circulation 2001, 104, 2107–2112.

[131] Schumann, P.A,; Christiansen, J.P.; Quigley, R.M.; McCreery, T.P.; Sweizer, R.H.;

Unger, E.X.; Lindner, J.R.; Matsunaga, T.O. Targeted-microbubble binding selectively to GPIIbIIIa receptors of platelet thrombi. Invest. Radiol. 2002, 37, 587–593.

[132] Gilles, B.; Saletes, I.; Bera, J.C. Cavitation generated by amplitude modulated

HIFU: Investigation on the inertial cavitation threshold. Am. Inst. Phys. 2007, 911,

171–177.

[133] Ay, T.; Havaux, X.; van Camp, G.; Campanelli, B.; Gisellu, G.; Pasquet, A.; Denef,

J.F.; Melin, J.A.; Vanoverschelde, J.L. Destruction of contrast microbubbles by ultrasound: Effects on myocardial function, coronary perfusion pressure, and microvascular integrity. Circulation 2001, 104, 461–466.

[134] Son,S.; Lee, S. Microfocusing using the thermal actuation of microbubbles.

Microfluid. Nanofluid. 2009, 6, 77–84.

[135] Lavon, I.; Kost, J. Ultrasound and transdermal drug delivery. Drug Discovery

Today 2004, 9, 670–676.

128

[136] Nathalie Lassau, Linda Chami, Baya Benatsou, Pierre Peronneau, and Alain

Roche. Dynamic contrast-enhanced ultrasonography (dce-us) with quantification of tumor perfusion: a new diagnostic tool to evaluate the early effects of antiangiogenic treatment. European Radiology Supplements, 17(S6):89–98, Dec 2007.

[137] A.J. Hindle, A.C. Perkins. A perfusion phantom for the evaluation of ultrasound contrast agent Ultrasound Med Biol, 20 (1994), pp. 309–314

[139] X. Chen, K. Schwarz, D. Phillips, S. Steinmetz, R. Schlief. A mathematical model for the assessment of hemodynamic parameters using quantitative contrast echocardiography

[140] K.Q. Schwarz, X. Chen, G.P. Bezante, D. Phillips, R. Schlief The Doppler kinetics of microbubble echo contrast Ultrasound Med Biol, 22 (1996), pp. 453–462

[141] B. Wilson, K.K. Shung, B. Hete, H. Levene, J.L. Barnhart A feasibility study on quantitating myocardial perfusion with Albunex®, an ultrasonic contrast agent

Ultrasound Med Biol, 19 (1993), pp. 181–191

[142] L. Claassen, G. Seidel, C. Algermissen Quantification of flow rates using harmonic grey-scale imaging and an ultrasonic contrast agent: An in vitro and in vivo study Ultrasound Med Biol, 27 (2001), pp. 83–88

[143] ZIERLER, K. L.: Circulation times and the theory of indicator-dilution methods for determining blood flow and volume. In: Handbook of PhysiCirculation Research,

Volume X, March 19G2 ology: A Critical, Comprehensive Presentation of

129

Physiological Knowledge and Concepts. Section 2, Volume I: Circulation. American

Physiological Society, Chapter B-10. (In press.)

[144] ZIERLER, K. L.: A simplified explanation of the theory of indicator-dilution for measurement of fluid flow and volume and other distributive phenomena. Bull.

Johns Hopkins Hosp. 103: 199, 1958.

[145] J Folkman. Angiogenesis. Annu Rev Med, 57:1–18, 2006

[146] G Bergers and LE Benjamin. Tumorigenesis and the angiogenic switch. Nat

Rev Cancer, 3(6):401–410, Jun 2003.

[147] Maeda H, Sawa T, Konno T. Mechanism of tumor-targeted delivery of macromolecular drugs, including the EPR effect in solid tumor and clinical overview of the prototype polymeric drug SMANCS. J Control Release 2001;74:47-61.

[148] Ultrasound for molecular imaging and therapy in cancer Osamu F. Kaneko,

Jürgen K. Willmann

[149] J. S. Huber, Q. Peng and W. W. Moses, "Multi-Modality Phantom Development,"

IEEE Transactions on Nuclear Science}, vol. 56}, pp. 2722-2727}, OCT}, 2009}.

[150] G. Cloutier, G. Soulez, S. Qanadli, P. Teppaz, L. Allard, Z. Qin, F. Cloutier and L.

Durand, "A multimodality vascular imaging phantom with fiducial markers visible in

DSA, CTA, MRA, and ultrasound," MEDICAL PHYSICS}, vol. 31}, pp. 1424-1433},

JUN}, 2004}.

130

[151] R. Frayne, l. Gowman, D. Rickey, D. Holdsworth, P. Picot, M. Drangova, M. Chu,

C. Caldwell, A. Fenster and B. Rutt, "A geometrically accurate vascular phantom for comparative-studies of x-ray, ultrasound, and magnetic-resonance vascular imaging

- construction and geometrical verification," medical physics}, vol. 20}, pp. 415-

425}, mar-apr}, 1993}.

[152] C. Kerber and C. Heilman, "Flow dynamics in the human carotid-artery .1.

Preliminary-observations using a transparent elastic model," American Journal of

Neuroradiology}, vol. 13}, pp. 173-180}, jan-feb}, 1992}.

[153] W. Dabrowski, J. DunmoreBuyze, R. Rankin, D. Holdsworth and A. Fenster, "A real vessel phantom for imaging experimentation," MEDICAL PHYSICS}, vol. 24}, pp.

687-693}, MAY}, 1997}.

[154] Estimation of intra-operator variability in perfusion parameter measurements using DCE-US Marianne Gauthier, Ingrid Leguerney, Jessie Thalmensi, Mohamed

Chebil, Sarah Parisot, Pierre Peronneau, Alain Roche, Nathalie Lassau

[155] Multimodal phantom of liver tissue. Chmarra MK1, Hansen R, Mårvik R, Langø

[156] Blechinger J C, Madsen E L and Frank G R 1988 Tissue-mimicking gelatin-agar gels for use in magnetic resonance imaging phantoms Med. Phys. 15 629–36

[157] De Korte C L, Cespedes E I, Van Der Steen A F W, Norder B and TeHijenhuis K

1997 Elastic and acoustic properties of vessel mimicking material for elasticity imaging Ultrason. Imaging 19 112–26

131

[158] Madsen E L, Zagzebski J A, Banjavie R A and Jutila R E 1978 Tissue mimicking materials for ultrasound phantoms Med. Phys. 5 391–4

[159] Mitchell M D, Kundel H L, Axel L and Joseph P M 1986 Agarose as a tissue equivalent phantom material for NMR imaging Magn. Reson. Imaging 4 263–6

[160] Rickey D W, Picot PA, Christopher D A and Fenster A 1995 A wall-less vessel phantom for Doppler ultrasound studies Ultrasound Med. Biol. 21 1163–76

[162] Ryan L K and Foster F S 1997 Tissue equivalent vessel phantoms for intravascular ultrasound Ultrasound Med. Biol. 23 261–73

[163] Yuan F, Dellian M, Fukumura D et al.: Vascular permeability in a human tumor xenograft: molecular size dependence and cutoff size. Cancer Res. 55, 3752–3756

(1995)

[164] Microbubble Compositions, Properties and Biomedical Applications. Shashank

Sirsi, MarkBorden Bubble Sci Eng Technol. Author manuscript; available in PMC

2010 June 22.

[165] Combining Microbubbles and Ultrasound for Drug Delivery to Brain Tumors:

Current Progress and Overview Hao-Li Liu1, Ching-Hsiang Fan2, Chien-Yu Ting2,

Chih-Kuang Yeh2, [166] Microbubble-induced increase in ablation of liver tumors by high-intensity focused ultrasound Kazuyuki Hanajiria, Toshiyuki Maruyamaa Yukio

Kanekob, Hiroshi Mitsuia,

[167] O'Neil, M.J. (ed.). The Merck Index - An Encyclopedia of Chemicals, Drugs, and

Biologicals. Whitehouse Station, NJ: Merck and Co., Inc., 2006., p. 1236

132

[168] Molecular Simulation Study of Structural and Dynamic Properties of Mixed

DPPC/DPPE Bilayers Sukit Leekumjorn and Amadeu K. Sum.

[169] Birner, R., M. Bürgermeister, R. Schneiter, and G. Daum. 2001. Roles of phosphatidylethanolamine and of its several biosynthetic pathways in

Saccharomyces cerevisiae. Mol. Biol. Cell. 12:997–1007.

[170] Jahn, R., and H. Grubmüller. 2002. Membrane fusion. Curr. Opin. Cell Biol.

14:488–495.

[171] Rickard R.F., Wilson J., Hudson D.A. Characterization of a rodent model for the study of arterial micro anastomoses with size discrepancy (small-to-large) Lab

Anim. 2009; 43:350–356.

[172] Nam K.H., Bok T.H., Jin C., Paeng D.G. Asymmetric radial expansion and contraction of rat carotid artery observed using a high-resolution ultrasound imaging system Ultrasonics. 2014;54:233–240.

[173] Sugimoto K, Shiraishi J, Moriyasu F, Ichimura S, Metoki R, Doi K. Analysis of intrahepatic vascular morphological changes of chronic liver disease for assessment of liver fibrosis 17 stages by micro-flow imaging with contrast-enhanced ultrasound: preliminary experience. Eur Radiol. 2010; 20:2749–2757.

[174] Bullitt E, Ewend MG, Aylward S, Lin W, Gerig G, Joshi S, Jung I, Muller K, Smith

JK. Abnormal vessel tortuosity as a marker of treatment response of malignant gliomas: preliminary report. Technol Cancer Res Treat. 2004; 3:577–584.

133

APPENDIX

(A) Graphical Results of Single-vessel Flow phantom

Imaging plane: Longitudinal, Flow-rate: 10mL/min, B-mode image: ROI

Imaging plane: Longitudinal, Flow-rate: 10mL/min, time-intensity curve

134

Imaging plane: Longitudinal, Flow-rate: 10mL/min, Slope

Imaging plane: Lateral, Flow-rate: 10mL/min, B-mode image: ROI

135

Imaging plane: Lateral, Flow-rate: 10mL/min, time-intensity curve

Imaging plane: Lateral, Flow-rate: 10mL/min, Slope

136

Imaging plane: Longitudinal, Flow-rate: 20mL/min, B-mode image: ROI

Imaging plane: longitudinal, Flow-rate: 20mL/min, time-intensity curve

137

Imaging plane: Longitudinal, Flow-rate: 20mL/min, Slope

Imaging plane: Lateral, Flow-rate: 20mL/min, B-mode image: ROI

138

Imaging plane: Lateral, Flow-rate: 20mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 20mL/min, Slope

139

Imaging plane: Longitudinal, Flow-rate: 30mL/min, B-mode image: ROI

Imaging plane: Longitudinal, Flow-rate: 30mL/min, B-mode image: time-intensity

curve

140

Imaging plane: Longitudinal, Flow-rate: 30mL/min, Slope

Imaging plane: Lateral, Flow-rate: 30mL/min, B-mode image: ROI

141

Imaging plane: Lateral, Flow-rate: 30mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 30mL/min, Slope

142

Imaging plane: Longitudinal, Flow-rate: 40mL/min, B-mode image: ROI

Imaging plane: Longitudinal, Flow-rate: 40mL/min, B-mode image: time-intensity

curve

143

Imaging plane: Longitudinal, Flow-rate: 40mL/min, Slope

Imaging plane: Lateral, Flow-rate: 40mL/min, B-mode image: ROI

144

Imaging plane: Lateral, Flow-rate: 40mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 40mL/min, Slope

145

Imaging plane: Longitudinal, Flow-rate: 50mL/min, B-mode image: ROI

Imaging plane Longitudinal, Flow-rate: 50mL/min, B-mode image: time-intensity

curve

146

Imaging plane: Longitudinal, Flow-rate: 50mL/min, Slope

Imaging plane: Lateral, Flow-rate: 50mL/min, B-mode image: ROI

147

Imaging plane: Lateral, Flow-rate: 50mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 50mL/min, Slope

148

(B) Graphical Results of Multi-vessel Flow phantom

Imaging plane: Longitudinal, Flow-rate: 10mL/min, B-mode image: ROI

Imaging plane: Longitudinal, Flow-rate: 10mL/min, B-mode image: time-intensity

curve

149

Imaging plane: Longitudinal, Flow-rate: 10mL/min, Slope

Imaging plane: Lateral, Flow-rate: 10L/min, B-mode image: ROI

150

Imaging plane: Lateral, Flow-rate: 10mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 10mL/min, Slope

151

Imaging plane: longitudinal, Flow-rate: 20mL/min, B-mode image: ROI

Imaging plane: longitudinal, Flow-rate: 20mL/min, B-mode image: time-intensity

curve

152

Imaging plane: longitudinal, Flow-rate: 20mL/min, Slope

Imaging plane: Lateral, Flow-rate: 20mL/min, B-mode image: ROI

153

Imaging plane: Lateral, Flow-rate: 20mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 20mL/min, Slope

154

Imaging plane: Longitudinal, Flow-rate: 30mL/min, B-mode image: ROI

Imaging plane: Longitudinal, Flow-rate: 30mL/min, B-mode image: time-intensity

curve

155

Imaging plane: Longitudinal, Flow-rate: 30mL/min, Slope

Imaging plane: Lateral, Flow-rate: 30mL/min, B-mode image: ROI

156

Imaging plane: Lateral, Flow-rate: 30mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 30mL/min, Slope

157

Imaging plane: longitudinal, Flow-rate: 40mL/min, B-mode image: ROI

Imaging plane: longitudinal, Flow-rate: 40mL/min, B-mode image: time-intensity

curve

158

Imaging plane: longitudinal, Flow-rate: 40mL/min, Slope

Imaging plane: Lateral, Flow-rate: 40mL/min, B-mode image: ROI

159

Imaging plane: Lateral, Flow-rate: 40mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 40mL/min, Slope

160

Imaging plane: longitudinal, Flow-rate: 50mL/min, B-mode image: ROI

Imaging plane: longitudinal, Flow-rate: 50mL/min, B-mode image: time-intensity

curve

161

Imaging plane: longitudinal, Flow-rate: 50mL/min, Slope

Imaging plane: Lateral, Flow-rate: 50mL/min, B-mode image: ROI

162

Imaging plane: Lateral, Flow-rate: 50mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 50mL/min, Slope

163

(A) Graphical Results of Tumor- vessel Flow phantom Tumor region 1

Imaging plane: longitudinal, In-Flow-rate: 10mL/min, B-mode image: ROI

Imaging plane: longitudinal, In-Flow-rate: 10mL/min, B-mode image: time-intensity

curve

164

Imaging plane: longitudinal, In-Flow-rate: 10mL/min, Slope

Imaging plane: Lateral, In-Flow-rate: 10mL/min, B-mode image: ROI

165

Imaging plane: Lateral, In-Flow-rate: 10mL/min, B-mode image: time-intensity

curve

Imaging plane: Lateral, Flow-rate: 10mL/min, Slope

166

Imaging plane: longitudinal, In-Flow-rate: 20mL/min, B-mode image: ROI

Imaging plane: longitudinal, In-Flow-rate: 20mL/min, B-mode image: time-intensity

curve

167

Imaging plane: longitudinal, In-Flow-rate: 20mL/min, Slope

Imaging plane: Lateral, Flow-rate: 20mL/min, B-mode image: ROI

168

Imaging plane: Lateral, Flow-rate: 20mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 20mL/min, Slope

169

Imaging plane: Longitudinal, Flow-rate: 30mL/min, B-mode image: ROI

Imaging plane: Longitudinal, In-Flow-rate: 30mL/min, B-mode image: time-

intensity curve

170

Imaging plane: Longitudinal, In-Flow-rate: 30mL/min, Slope

Imaging plane: Lateral, In-Flow-rate: 30mL/min, B-mode image: ROI

171

Imaging plane: Lateral, Flow-rate: 30mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 30mL/min, Slope

172

Imaging plane: Longitudinal, In-Flow-rate: 40mL/min, B-mode image: ROI

Imaging plane: Longitudinal, In-Flow-rate: 40mL/min, B-mode image: time-

intensity curve

173

Imaging plane: Longitudinal, In-Flow-rate: 40mL/min, Slope

Imaging plane: Lateral, in-Flow-rate: 40mL/min, B-mode image: ROI

174

Imaging plane: Lateral, In-Flow-rate: 40mL/min, B-mode image: time-intensity

curve

Imaging plane: Lateral, In-Flow-rate: 40mL/min, Slope

175

Imaging plane: Longitudinal, In-Flow-rate: 50mL/min, B-mode image: ROI

Imaging plane: Longitudinal, In-Flow-rate: 50mL/min, B-mode image: time-

intensity curve

176

Imaging plane: Longitudinal, In-Flow-rate: 50mL/min, Slope

Imaging plane: Lateral, In-Flow-rate: 50mL/min, B-mode image: ROI

177

Imaging plane: Lateral, In-Flow-rate: 50mL/min, B-mode image: time-intensity

curve

Imaging plane: Lateral, Flow-rate: 50mL/min, Slope

178

Imaging plane: Lateral, Out-Flow-rate: 10mL/min, B-mode image: ROI

Imaging plane: Lateral, Out-Flow-rate: 10mL/min, B-mode image: time-intensity

curve

179

Imaging plane: Lateral, Out-Flow-rate: 10mL/min, Slope

Imaging plane: Lateral, Flow-rate: 10mL/min, B-mode image: ROI

180

Imaging plane: Lateral, Flow-rate: 10mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 10mL/min, Slope

181

Imaging plane: Lateral, Out-Flow-rate: 20mL/min, B-mode image: ROI

Imaging plane: Lateral, Out-Flow-rate: 20mL/min, B-mode image: time-intensity

curve

182

Imaging plane: Lateral, Out-Flow-rate: 10mL/min, Slope

Imaging plane: Lateral, Flow-rate: 20mL/min, B-mode image: ROI

183

Imaging plane: Lateral, Flow-rate: 20mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 10mL/min, Slope

184

Imaging plane: Longitudinal, Out-Flow-rate: 30mL/min, B-mode image: ROI

Imaging plane: Longitudinal, Out-Flow-rate: 30mL/min, B-mode image: time-

intensity curve

185

Imaging plane: Longitudinal, Out-Flow-rate: 30mL/min, Slope

Imaging plane: Lateral, Flow-rate: 30mL/min, B-mode image: ROI

186

Imaging plane: Lateral, Flow-rate: 30mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 30mL/min, Slope

187

Imaging plane: Longitudinal, Out-Flow-rate: 40mL/min, B-mode image: ROI

Imaging plane: Longitudinal, Out-Flow-rate: 40mL/min, B-mode image: time-

intensity curve

188

Imaging plane: Longitudinal, Out-Flow-rate: 40mL/min, Slope

Imaging plane: Lateral, Flow-rate: 40mL/min, B-mode image: ROI

189

Imaging plane: Lateral, Flow-rate: 40mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 40mL/min, Slope

190

Imaging plane: Longitudinal, Out-Flow-rate: 50mL/min, B-mode image: ROI

Imaging plane: Longitudinal, Out-Flow-rate: 20mL/min, B-mode image: time-

intensity curve

191

Imaging plane: Longitudinal, Out-Flow-rate: 10mL/min, Slope

Imaging plane: Lateral, Flow-rate: 50mL/min, B-mode image: ROI

192

Imaging plane: Lateral, Flow-rate: 50mL/min, B-mode image: time-intensity curve

Imaging plane: Lateral, Flow-rate: 50mL/min, Slope

193