Modality Phantom for Contrast Enhanced Ultrasound and Magnetic Resonance Imaging

DESIGN AND CHARACTERIZATION OF A MULTI- MODALITY PHANTOM FOR CONTRAST ENHANCED ULTRASOUND AND MAGNETIC RESONANCE IMAGING

Ian Pang

A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Medical Biophysics University of Toronto

Abstract

Design and Characterization of a Multi-Modality Phantom for Contrast Enhanced Ultrasound and Magnetic Resonance Imaging

Ian Pang

Master of Science

Graduate Department of Medical Biophysics

University of Toronto, 2011

Multi-modality imaging is a possible solution for overcoming individual modality limitations. With the use of modality specific contrast agents, contrast-enhanced multi- modality imaging may provide a more comprehensive evaluation of tumour characteristics.

This may be possible by combining ultrasound and magnetic resonance imaging, whose contrast agents behave differently within the microvasculature. A novel, microvascular, and leaky phantom is presented that permits ultrasound contrast agents to remain entirely within the mimicking vascular compartment while the magnetic resonance contrast agents may freely diffuse between the mimicking vasculature and tissue compartments. The results show that the phantom is a useful tool for investigating the combination of contrast-enhanced ultrasound and magnetic resonance imaging. This work motivates further combined contrast-enhanced imaging studies, with future work to incorporate additional modalities.

Acknowledgements

First, I would like to acknowledge the members of my supervisory committee for supporting and guiding me throughout this journey of academia. I felt encouraged to pursue many of my own ideas and projects, which ultimately led to this thesis. For his guidance, and always reminding me of the importance of effective time management, I would like to express my gratitude to my supervisor Dr. Rajiv Chopra. I would also like to thank Dr. Donald Plewes, whose advice and suggestions always managed to stir my imagination. Finally, I wish to thank

Dr. David Goertz, for always reminding me to answer what I'm asking.

For all the friendships, encouragements, assistance, and conversations, I sincerely thank all the members of C7, past and present. To the students, your company has been refreshing and entertaining. Between all the experiments and the quest for results, there were some truly wonderful coffee breaks, outdoor lunches, musical outbursts, pranks, and broken records. To the machinists of SRI, thank you for your patience and guidance on all things manufacturing and design. Some of the best times of my Master's were the countless hours I spent in the machine shop listening to classic rock alongside you.

Finally, I wish to thank my family for their continuous support and patience during the years spent on this thesis. Many of your homemade meals kept me going through the late nights and weekends. You've always believed in me, and for that I am eternally grateful.

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Content

Abstract ...... ii

Acknowledgements ...... iii

Content ...... iv

List of Figures ...... vi

List of Tables ...... ix

1. Introduction ...... 1 1.1. Contrast Imaging...... 1

1.1.1. Contrast-Enhanced Ultrasound Imaging ...... 2

1.1.2. Dynamic Contrast-Enhanced Magnetic Resonance Imaging...... 4

1.2. Kinetics of Contrast Agents ...... 5

1.2.1. Compartmental Modeling ...... 6

1.2.2. The Gamma Variate Function ...... 8

1.3. Multi-Modality Imaging ...... 10

1.3.1 Combined Ultrasound and Magnetic Resonance Imaging ...... 11

1.4. Purpose and Objectives ...... 13 1.5. Appendix – Background Knowledge ...... 14

1.5.1. The Microcirculation ...... 14

1.5.2. Cancer and Angiogenesis...... 15

1.5.3. Tumour Vasculature ...... 16

1.5.4. Principles of Ultrasound ...... 17

1.5.5. Principles of Magnetic Resonance ...... 19

1.5.6. T1-Weighted Imaging ...... 23 1.5.7. Measuring T1 ...... 23

2. Design and Characterization of a Multi-Modality Phantom ...... 25 2.1 Introduction ...... 25

2.2 Materials and Methods ...... 27

2.2.1 Design of a Multi-Modality Phantom ...... 27

2.2.2 Fabrication of a Multi-Modality Phantom ...... 29

2.2.3 Characterization of Multi-Modality Phantom ...... 31

2.2.4 Contrast-Enhanced Ultrasound Imaging ...... 33

2.2.5 Dynamic Contrast-Enhanced Magnetic Resonance Imaging...... 34

2.2.6 Data Analysis ...... 35 2.3 Results ...... 38

2.3.1 Characterization of Multi-Modality Phantom ...... 38

2.3.2 Contrast Kinetic Curves ...... 40

2.3.3 Gamma Variate Fit ...... 42

2.4 Discussion ...... 45

2.5 Conclusions ...... 50

3. Conclusions and Future Works ...... 51 3.1 Summary of Thesis Contributions ...... 51

3.2 Future Works ...... 52

3.2.1 Optimizing Design and Fabrication ...... 52

3.2.2 Investigations Using Computed Tomography ...... 54

3.2.3 Applications With Specific Imaging Techniques ...... 55

4. Bibliography ...... 61 5. Appendix ...... 61 5.1 Procedure for Phantom Fabrication ...... 70

5.2. Schematic Drawings for Phantom Parts ...... 78

List of Figures

Figure 1.1 Schematic representation of a compartmentalized system accessible by diffusible contrast media...... 6

Figure 1.2 Normal blood vessels (A) and tumour blood vessels (B) imaged from mouse dorsal skin chambers, where the blood vessels are contrast enhanced using FITC-dextran and imaged using multiphoton laser-scanning microscopy. The tumour cells were from a LS174T human colon cancer xenograft. Both images are 550 μm across. [69] ...... 16

Figure 1.3 Schematic representation of the axial, lateral, and elevational contributions to an ultrasound beam [70]...... 19

Figure 2.1 Overall phantom design with labelled parts...... 28

Figure 2.2 Cross section of dialysis tube wall. The wall is 30 μm thick and is characterized by a multi-layer ordering of pore sizes (Baxter®)...... 29

Figure 2.3 A completed phantom unit, where the spacing between dialysis tubes is 600 μm. B) The phantom unit before the imaging section is filled with gel, highlighting the parallel arrangement of the dialysis tubes...... 31

Figure 2.4 A schematic diagram of the experimental setup...... 32

Figure 2.5 A high SNR axial MR image (A) acquired for generating a mask (B) to separate out the compartment signals. The resulting binary mask was only applied for MR data...... 37

Figure 2.6 Plot of mean driving pressure across the phantom as a function of mean flow rate. The number in brackets denotes the spacing between tubes in µm (n = 5). Standard deviations were two orders of magnitude smaller than their respective means and thus were not included in the plot...... 38

the enhancement. Note that the MR image contrast has been adjusted in this figure to highlight enhancement outside of the dialysis tubes...... 39

Figure 2.8 Contrast kinetic curves resulting from the phantom with 600 μm spacing between tubes. The curves represent signal from within the imaging chamber of the phantom, where the US Tube signal represents an intravascular signal components, and the MR Tube and MR Gel signals represent the signal within the dialysis tubing and agar gel respectively...... 41

Figure 2.9 Contrast kinetic curves resulting from the phantom with 900 μm spacing between tubes. The MR Inflow and US Tube signals represent intravascular signal components, while the MR Tube and MR Gel represent the signal within the dialysis tubing and agar gel respectively...... 42

Figure 2.10 The MR Inflow and US Tube signals for the 600 µm phantom, which represent signal that has not leaked into the extravascular space...... 43

Figure 2.11 Fitting a gamma variate function (GVF) to the MR Input signal (A) and the US Tube signal (B), both of which represent an intravascular signal component. The data is from a phantom with 600 μm spacing between tubes...... 44

Figure 3.1 A prototype phantom with two inlets and outlets. A) The red arrows point at an inlet/outlet pair that leads to a ring of dialysis tubes that surrounds a 5 x 5 tube matrix. The inlet/outlet for the 5 x 5 matrix of tubes is denoted by the black arrows. The total number of tubes present is 49. B) A schematic representation of the phantom cross section, highlighting the outer ring of tubes (red) that have a separate flow inlet/outlet than the inner matrix of tubes (black)...... 53

Figure 3.2 A) Beads of water forming as water is passed through the tubing. The beads form as water is forced through the pores of the dialysis tubing. B) A phantom after it was parylene coated, showing no beads forming as water is passed through the tubing...... 54

Figure 3.3 A preliminary experiment was conducted using an old phantom to test the feasibility of perfusion imaging with CT. Enhancement was observed (A); however, there was no apparent sign of leakage back into the tubes as the peak signal intensity appeared to reach a plateau level (B). Future CT experiments would require a change in the experimental setup to account for the weight and dosage of the CT contrast agent...... 55

Figure 3.4 A) A single tube phantom. B) A schematic example of contrast agent diffusion from the single tube due to a steady infusion through the phantom. The closer a region is to the tube, the higher the concentration of agent...... 57

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Figure 3.5 Schematic diagram demonstrating a possible solution for creating an evenly mixed chamber using water within the imaging chamber. Air is passed through the air channel turning a fan, which in turn rotates a mixer within the chamber. This proposed mechanism avoids the use of a mechanical stirrer for creating an evenly mixed chamber...... 58

Figure 3.6 Pixel-by-pixel parametric map of the time to peak (min) parameter for the 600 µm spacing phantom data. Peak signal intensities arrive much more quickly within the tube compartment than the gel. Diffusing contrast agent along the outer boundary of the tubes will accumulate and thus have much longer time to peak values. Parameter parametric maps allow visualization of the different compartment spaces due to different signal characteristics on a per pixel basis. ... 60

Figure 5.1 A) Manufactured phantom parts. B) Tweezers and accupuncture needle used during fabrication process...... 70

Figure 5.2 Threading of the fibres through the mesh, where A) depicts the first row of a 5 x 5 arrangement and B) depicts a finished 5 x 5 arrangement with silicon (Step 8)...... 72

Figure 5.3 A) Spacers separating the different rows of tubes. B) Silicon applied to the nylon mesh, which holds the tubes to the mesh...... 73

Figure 5.4 A phantom with an end piece placed over the tube bundle. Note the jig used to support the phantom upright...... 74

Figure 5.5 A mylar sheet sealing the imaging chamber. The phantom is now ready to be filled with a tissue mimicking material...... 76

Figure 5.6 Phantom wall...... 78

Figure 5.7 Phantom end piece...... 79

Figure 5.8 Ring...... 79

Figure 5.9 Phantom ring holder...... 80

Figure 5.10 Schematic of an assembled phantom...... 80

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List of Tables

Table 1 Fitting parameters from the gamma variate fit for the intravascular compartmental signals...... 43

Chapter 1

1. Introduction

1.1. Contrast Imaging

Dynamic contrast-enhanced imaging has been developed as a tool to study tumour vasculature. The abnormal pathophysiology and microvascular structure gives rise to temporal and spatial variations in signal enhancement that differ from normal surrounding tissue, which can be used to provide information on tumour characteristics. Some of the physiological characteristics of interest include blood flow, blood velocity, vessel permeability, and microvascular vessel density. Some studies have investigated the prognostic value of these parameters [1] and their ability to monitor response to a therapy based on their changes [2, 3].

Contrast-enhanced imaging studies typically begin with the intravenous administration of a contrast agent bolus through a peripheral vein. Bolus injection methodology is of considerable importance, and must be done in a consistent manner [4]. As the bolus passes through the circulatory system, the vascular characteristics described by enhancement will be due to the

physical properties of the contrast agent. Generally, a contrast agent is considered intravascular or extravascular. Intravascular contrast agents are assumed to never leave the vascular system due to their large size in comparison to the spaces between vessel wall endothelial cells.

Extravascular contrast agents, by comparison, are small enough such that they traverse vessel walls through the endothelial cell junctions into the space outside the vasculature. This process is often mediated by diffusion.

Imaging technologies have played an important role in understanding angiogenic mechanisms and functional implications [5]. Both magnetic resonance and ultrasound imaging modalities can apply dynamic contrast enhanced imaging techniques for the purpose of characterising tumours. Several techniques have been developed for both imaging modalities, and there is considerable interest in applications that combine both modalities to provide a more informed and complete tumour characterization.

1.1.1. Contrast-Enhanced Ultrasound Imaging

Some of the conventional ultrasound imaging methods are not sensitive to vessels smaller than 100 μm [6]. With the introduction of microbubble contrast agents, the field of contrast enhanced ultrasound (CE-US) imaging has enabled an assessment of microvascular structures and hemodynamic characteristics. Microbubbles, typically around 1-10 μm in diameter [7], 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. As well, microbubbles are true intravascular agents due to their size.

Ultrasound microbubbles present many advantages for contrast ultrasound over conventional ultrasound. 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. As well, microbubbles will oscillate in an ultrasound field, and emit a specific signal that contains nonlinear components. This signal can be filtered in order to distinguish the echoes returning from blood versus those returning from tissue. Finally, at high insonation powers, the microbubble can be disrupted whereby the shell ruptures and the gas escapes, effectively removing contrast signal.

The detection of ultrasound microbubbles can be accomplished with any ultrasound imaging technique since microbubbles act as acoustic scatterers. However, the preferential detection of microbubble nonlinear/harmonic components requires a specialized detection scheme. Contrast-enhanced ultrasound imaging, usually done at low acoustic power in order to minimize microbubble disruption, may employ a ‘harmonic imaging mode’, whereby the fundamental frequency is blocked using a frequency filter set to retain the backscattered harmonic echoes. Other techniques, referred to as multi-pulse techniques, have been introduced in a ‘pulse-cancellation mode’ [7], 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 the nonlinear signals. An example of such a technique is pulse-inversion [8]. 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 [9-12].

1.1.2. Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Contrast agents for magnetic resonance typically alter the relaxation times in order to enhance contrast between different tissue types. The most prevalent type of contrast agents for magnetic resonance are Gd-chelate based. Gadolinium is a Lanthanide element that is paramagnetic in its trivalent state (GdIII). This metal ion has an S ground state structure that couples a large magnetic moment with a long electron spin relaxation time, making it efficient for nuclear spin relaxation when interacting with nuclei [13]. However, as a free metal ion it is poorly tolerated in vivo as it is toxic. In order to make them clinically viable, GdIII is bound to a ligand. Other properties required of contrast agents along with low toxicity include good water solubility and rapid excretion after administration [13].

Gd-chelate based contrast agents, typically around 500 da in size, are able to freely diffuse from the vasculature into tissue, and vice versa. This movement of the contrast agent between vascular and extra-vascular tissue compartments requires a mathematical model to describe the pharmacokinetics of the contrast agent and its regional distribution, which is governed by parameters such as blood flow, blood volume, the endothelial permeability and surface area, as well as the size of the surrounding extracellular extravascular space [14-17].

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) typically involves

T1-weighted sequences. For a spoiled gradient recalled echo pulse sequence, a short TR and TE along with a moderate flip angle are used. Low concentrations of Gd-chelate based MR contrast agents will primarily cause a shortening in T1, which is the cause of signal enhancement. This enhancement seen by T1-weighted imaging schemes is due to several factors, such as the native

T1 of the tissue, the dose of the contrast agent, the parameters chosen for the given imaging

scheme, and the different methods the contrast agent may behave within the physiology [3].

The affected T1 relaxation time can be linearly related to contrast agent concentration [18] by

1 1 = + 푟 ∙ [퐶퐴] 푇1 푇10 1 where, T10 is the pre-injection T1 of tissue, r1 is the relaxivity [mmol-1s-1], and CA is the contrast agent concentration [mmol]. Relaxivity is dependent on the chemical properties of the contrast agent and is a parameter that describes the agents’ ability to increase the relaxation rates of the surrounding water protons.

1.2. Kinetics of Contrast Agents

Kinetic studies are of increasing importance when studying the functional components of biological systems. Typically, a bolus of contrast agent (tracer) is injected in the bloodstream and its concentration versus time curve is measured downstream. Functional information can be extracted from system kinetics data, taken from the spatial and temporal components of that particular system. In many circumstances, a mathematical model is utilized to interpret kinetic data in order to extract useful characteristic parameters regarding the system. Contrast agents are used as externally delivered substances which act as probes for characterizing the system. If they are diffusible, they may pass through the capillary walls into the extravascular extracellular space (EES), which is the interstitial space excluding regions of blood plasma and cells.

1.2.1. Compartmental Modeling

Compartmental models are useful for modeling diffusible contrast agents, i.e. those that can pass out of capillaries and into the extravascular space. These models represent a system as distinct compartments which are connected via pathways representing material transfers. A compartment is considered well-mixed and kinetically homogeneous [19]. Well-mixed means any two samples within the compartment from a given time point would have the same concentration of substance, and kinetically homogeneous means any particle in a compartment has equal probability of taking the pathways leaving the compartment.

Figure 1.1 Schematic representation of a compartmentalized system accessible by diffusible contrast media.

A two-compartment model is often used for analysing MR contrast kinetic data that uses

Gd(III)-based contrast agents (Fig. 1.1). The contrast agent diffuses from the blood plasma compartment into the extracellular space of the tissue at a rate determined by blood flow to the

tissue, permeability of the vessel walls, and the surface area of the perfusing vessels [20]. Here,

MR contrast agents are considered to not cross cell membranes; thus, the space outside the vessel is termed the extravascular extracellular space (EES), and its volume distribution per unit volume of tissue is ve. If the vascular volume is a small fraction of the total tissue volume, then the contrast agent concentration in the vessels (Cp) are assumed to not influence the total tissue concentration (Ct), and 퐶푡 = 푣푒 ∙ 퐶푒, where Ce is the concentration the agent in the EES. Here, the rate equation is given as

푑퐶푒 푣 = 퐾푡푟푎푛푠 (퐶 − 퐶 ) 푒 푑푡 푝 푒 where Ktrans is the transfer constant between vp and ve. The solution to the rate equation is

−퐾푡푟푎푛푠 푡−푡 ′ 푡 푡푟푎푛푠 ′ 푣푒 퐶푡 푡 = 퐾 ∙ 퐶푝 푡 ∙ 푒 푑푡′ 0

Depending on the physiological state of capillary permeability and blood flow, there can be different interpretations as to what Ktrans represents. A collaborative review article led by Tofts

[14] provides a summary on the different Ktrans interpretations. Along with the limitation of needing a priori knowledge regarding the vessel/flow characteristics for interpreting Ktrans, this model assumed a bi-exponential washout profile for the blood plasma contrast agent concentration Cp [14, 21]. This method is often called the Tofts model within literature.

Due to the limitations described above, the model was modified to include the concentration of contrast agent in the blood plasma. This has led to the generalized kinetic model, which has become a popular tracer kinetic standard. The generalized kinetic model refers to the total tissue tracer (퐶푡 = 푣푝 ∙ 퐶푝 + 푣푒 ∙ 퐶푒) and thus

−퐾푡푟푎푛푠 푡−푡′ 푡 푡푟푎푛푠 ′ 푣푒 퐶푡 푡 = 푣푝 ∙ 퐶푝 (푡) + 퐾 ∙ 퐶푝 푡 ∙ 푒 푑푡′ 0

A limitation of this model is in the early phases of tissue enhancement, where contrast agent transport is occurring largely in one direction. In this case, the interstitial volume can be ignored and the equation is simplified to [22]

푡 푡푟푎푛푠 ′ 퐶푡 푡 = 푣푝 ∙ 퐶푝 (푡) + 퐾 ∙ 퐶푝 푡 ∙ 푑푡′ 0

The generalized kinetic model applies a defined Cp, or arterial input function (AIF), which is usually measured for each individual patient.

1.2.2. The Gamma Variate Function

The earliest contrast agents were termed indicators, which were easily detectable substances that could be injected into the vascular system. Some early investigations looked at quantifying blood volume and cardiac output [23, 24]. Indicator dilution curves were of interest since they allowed application of the central volume principle, or the Stewart-Hamilton relationship [23, 25], which is

퐵푉 푀푇푇 = 퐵퐹 where MTT is the mean transit time through the volume defined by the blood volume (BV), and

BF is the blood flow [26, 27]. It was soon apparent that an analytical expression for indicator dilution curves would greatly facilitate theoretical analyses of arterial indicator dilution curves, as well as enable characterization between normal versus abnormal curves. It was shown heuristically that indicator dilution curves could be described by the mathematical properties of a general class of random variables termed "gamma variates" [28]. The gamma variate function

is the probability density function of the Erlang distribution, which is a special case of the gamma distribution. It was eventually shown that the gamma variate could be derived by modeling flow in a blood vessel as a series of mixing chambers [29]. The complete expression for the gamma variate function is

퐴 퐶 푡 = ∙ (푡 − 퐴푇)훼 ∙ 푒−(푡−퐴푇)/훽 훽훼+1 ∙ 훤(훼 + 1) where α and β are distribution parameters, A is the area under the curve, t is the transit time since the time of injection, AT is the bolus arrival time, and Γ(α + 1) is the gamma function. It is convenient to represent the bracketed term as a constant, termed the scale factor K. Thus, we can rewrite the gamma variate equation into the formula [27]

0 (푡 < 푡표)

퐶 푡 = −(푡−푡표 ) 훼 훽 퐾 ∙ (푡 − 푡표) ∙ 푒 (푡 ≥ 푡표 ) where to represents the bolus arrival time, K is a scale factor, and α and β describe the shape of the bolus. The formula can be fitted using a Levenberg-Marquardt approach for nonlinear least-squares fitting and optimization. A user defined to can be used to render the optimization more robust, leaving K, α and β as the fitted parameters.

The gamma variate function has found applications for fitting the indicator dilution curve to the first pass of an intravascular contrast agent bolus. It has been used to describe ultrasound contrast dilution curves in piglet brains [30], as well as the increase in blood volume and perfusion is rabbit skeletal muscle due to adenoviral endothelial growth factor (AdVEGF) gene transfer [31]. Recent studies have looked at comparing the gamma variate function against other fitting functions for indicator dilution models applied to US contrast enhanced time intensity curves from liver metastases and the ovine corpora lutea [32]. Within the field of MRI,

the gamma variate function can be used to fit the concentration versus time curve and exclude recirculation effects from dynamic susceptibility contrast enhanced MRI studies, which usually focus on the brain [33, 34]. These studies seek to measure cerebral blood volume and cerebral blood flow, and assume that the MR contrast agent remains purely intravascular within the brain vasculature due the blood brain barrier.

1.3. Multi-Modality Imaging

The combination of multiple imaging modalities and the information they provide is of interest for enhancing the overall diagnostic information. This is because no one single imaging modality may provide information on all aspects of structure and function [35]. Clinically, this information is gathered in separate imaging sessions, often performed under different patient orientations. As a result, direct quantitative comparison of the images is usually impossible, and often the comparisons are made visually and qualitatively. While this can be sufficient for many routine clinical applications, there are situations where it would be desirable to perform more quantitative region-of-interest or pixel-based analysis by combination of the data to determine an underlying tissue property. One approach to accomplish this is through image registration. While the practical advantages of this approach include the use of existing equipment, there are clear limitations. The acquisition of sequential scans from different imaging modalities makes it impossible to correlate parameters measured and their changes throughout a study. Additional limitations include the difficulty with achieving perfect registration, and from the patient's perspective, having to schedule independent imaging exams.

Recently, hybrid imaging systems have emerged to combine the advantages of individual imaging modalities into a more fluid, even simultaneous acquisition setup.

Integration of multiple imaging modalities can follow either of two routes: placing the modalities side by side and being able to move the patient smoothly and quickly from one modality to the other or a fully integrated system where the multiple imaging modalities are used simultaneously within a single imaging exam.

An example of multi-modal imaging systems in clinical use is PET with CT [36-38].

PET provides functional information while CT provides structural information; thus, an integrated system seemed obvious, such as for localizing functional abnormalities prior to radiation or surgical treatment [35]. Other modality combinations include ultrasound and near infrared tomography [39, 40], ultrasound and magnetic resonance [41-43], magnetic resonance and x-ray [44, 45], and magnetic resonance and PET [46, 47].

1.3.1 Combined Ultrasound and Magnetic Resonance Imaging

Multi-modality US and MR imaging has been investigated previously by a number of investigators. Studies have looked at integration of US and MR through simultaneous data acquisition [41-43, 48, 49] or co-registration of images [50-52]. Applications that have been explored include real time motion compensation [41, 43], tissue biopsy [48, 53], and monitoring of ablations [49]. Another application of combined US and MR is for contrast imaging. Several studies have compared and evaluated contrast-enhanced tumour perfusion measurements made with US and MR. Yankeelov et al. found a correlation when assessing tumour perfusion with US and MR between the measured parameters α∙β and Ktrans, but no significant correlation

was found between either α and Ktrans or β and Ktrans [54]. Niermann et al. evaluated contrast enhanced US (CE-US) in comparison with DCE-MRI and FDG-PET in its ability to characterize tumour perfusion within mice before and after a variety of treatments (radiation therapy, antiangiogenic chemotherapy, and combined chemoradiation) [55]. It was found that CE-US estimated parameters (perfusion, blood volume, blood velocity) were significantly reduced with each treatment method, where the greatest decrease was observed for the combined chemoradiation group. DCE-MRI and PDG-PET also saw a decrease in their measured parameters but they were statistically insignificant. Harrer et al. evaluated US and MR perfusion techniques in assessing brain tumours, and found by analyzing their respective time-intensity curves a significant difference in peak intensity, the positive slope gradient, and the area under the curve when comparing healthy to tumour brain tissue [56]. The time to peak parameter did not show significant difference, yet had previously been shown to be useful in a CE-US and

DCE-MRI stroke study [57]. Thus, the author’s state that the most appropriate and reliable parameter has yet to be determined. In this manner many comparative perfusion studies look promising, yet no definite conclusions are made.

In order to evaluate the contrast signal characteristics between different imaging modalities and compare different perfusion techniques, a standardized microvascular phantom with well-controlled parameters could prove valuable. Developing a physical model for ultrasound and magnetic resonance contrast imaging may give insight on complementary imaging strategies for combining the spatial and temporal information gathered to infer upon the vascular state of the physiologic system.

1.4. Purpose and Objectives

Phantoms provide a good platform due to easy reproducibility of experiments, parameter control, and the ability to include fiducial markers for precise registration and image fusion. A single standardized flow phantom compatible across all modalities would be ideal as a platform for comparative multi-modality microvascular studies. The purpose of this thesis is to describe the fabrication and characterization of a leaky microvascular phantom and to compare the contrast enhancement measured with both magnetic resonance and ultrasound imaging.

1.5. Appendix – Background Knowledge

1.5.1. The Microcirculation

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. The primary function of the microcirculation is the delivery of nutrients and oxygen and the removal of waste products and carbon dioxide [58, 59]. Other functions include the regulation of blood pressure and body temperature [58, 59].

The microcirculation begins when small arteries with around 100 μm diameters begin to branch into smaller arterioles, which have diameters around 20-30 μm [58, 59]. 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 [58]. 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 [59]. 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 [60].

1.5.2. Cancer and Angiogenesis

In Canada, it is estimated that during the year 2010 there will be approximately 76,200 cancer related deaths [61]. As well, every week there will be approximately 3,340 new cases of cancer diagnosed [61]. Thus, cancer constitutes a major health issue that affects the lives of many Canadians. The microcirculation plays an important role in cancer development and progression, and examining tumour microvasculature may provide diagnostic and prognostic information regarding the disease.

Tumours begin when a group of cells display uncontrolled cell growth. They are described as benign when the growth is relatively slow and constrained to a specific location.

Malignant tumours, on the other hand, grow much more rapidly and may undergo a process called metastasis, whereby the tumour cells may disperse to another tissue or organ and establish secondary tumours. The term cancer generally refers to malignant tumours [58].

A tumour will begin supporting itself through avascular means, meaning the diffusion of nutrients and oxygen will support its growth. This continues until the tumour reaches a certain size, roughly 1 mm3 [62]. Any growth of the tumour after it has reached its maximum size through avascular means would require the recruitment of blood vessels to deliver the necessary nutrients and oxygen. This neovascularisation is a process described as angiogenesis, and has been identified as an essential step for tumour growth [63]. Angiogenesis, by definition, is the formation of new capillary blood vessels from pre-existing microvessels [64].

Thus, in order for tumours to grow rapidly, angiogenesis must be initiated [65]. The tumour vasculature that arises, however, is much different than normal vasculature.

1.5.3. Tumour Vasculature

Tumour vasculature is highly heterogeneous, where the vessels are irregularly constricted or dilated, can have uneven diameters, and excessive branching [66]. Consequently, tumour blood flow is chaotic and variable. As well, the walls of the vessels in tumours have widened inter-endothelial junctions and abnormally shaped endothelial cells, defects which make tumour vessels leaky [66].

Tumour vascular networks differ significantly from normal tissue. Whereas normal vascular networks are well ordered with a hierarchical vessel arrangement, tumour vascular networks can be described as a ‚tangle of vessels‛ [67] (Figure 1.2). 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 [68].

1.5.4. Principles of Ultrasound

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.

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. Due to the natural characteristics of piezoelectrics, the reflected ultrasound energy that returns to the transducer is reconverted back into an electrical signal. Depending on the speed at which sound travels through the tissue, the returning echo will arrive at the transducer after some time delay (td), which is proportional to the depth of the scattering boundary:

2 ∙ 푑푒푝푡푕 푡푑 = 푐푠표푢푛푑 where csound is the speed of sound within the tissue of interest.

Ultrasound travels through a medium as a mechanical and longitudinal vibration of the medium's particles, where the particles of the medium can be thought as connected by springs, effectively making the medium elastic. The propagation speed of the ultrasound wave

(co) will be dictated by the medium properties, and is determined by

훽 푐 = 표 휌

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 speed of sound (co) can be related to frequency (f) and wavelength (λ) by

푐표 = 푓 ∙ 휆

The number of wavelengths in an ultrasound pulse dictates spatial pulse length. An ultrasound imaging pulse is typically three cycles long [70].

Our ability to resolve fine details and objects is governed by the spatial resolution of our imaging system (Figure 1.3). The axial resolution of an ultrasound system refers to its ability to distinguish between two closely spaced objects in the beam direction [70]. 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

1 퐴푅 = ∙ 푆푝푎푡푖푎푙 푃푢푙푠푒 퐿푒푛푔푡푕 2

The lateral resolution describes the ultrasound systems ability to distinguish between two closely spaced objects perpendicular to the beam direction, and is determined by the beam width at a specific depth [70]. Beam width changes with depth, making lateral resolution depth dependent. The elevation resolution, or the slice thickness of the ultrasound beam, is dependent on the height of the transducer elements.

Figure 1.3 Schematic representation of the axial, lateral, and elevational contributions to an ultrasound beam [70].

There are many interactions that an ultrasound pulse may experience due to the acoustic properties of tissue. The energy of the ultrasound beam may be scattered or absorbed.

Acoustic scattering is caused by objects within tissue that are roughly the same or smaller in size to the wavelength, and scatter ultrasound in all directions. 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. These losses in energy are referred to as attenuation.

1.5.5. Principles of Magnetic Resonance

The nucleus of an atom exhibits magnetic characteristics which are influenced by the spin and charge distributions. When the total number of protons is not equal to the number of neutrons in a nucleus, there is a magnetic moment created due to the nuclear spin. Hydrogen

has a large magnetic moment and is abundant throughout the human body, primarily as part of water molecules, and thus is the principle element for magnetic resonance imaging.

Magnetic resonance imaging begins with the alignment of paramagnetic nuclei within a high magnetic field, usually 1.5T or 3T in strength. Protons in this field will align with the magnetic field in either a parallel or anti-parallel orientation, where the sum of their magnetic moments will produce a net magnetic moment in the direction of the magnetic field. In addition, the magnetic field will cause the spinning protons to precess at an angular frequency

(ωo) that is proportional to the magnetic field strength (Bo). This relationship is described by the

Larmor equation

휔표 = 훾 ∙ 퐵표 where Υ is the gyromagnetic ratio, a constant for each element. For hydrogen, Υ is equal to 42.58

MHz/T.

The equilibrium of the net magnetic moment can be disturbed by application of a radiofrequency pulse tuned to the Larmor frequency, whose magnetic component is termed B1.

The displacement of the longitudinal magnetization vector to generate transverse magnetization is described by the flip angle. A 90-degree flip angle will place the entire longitudinal magnetization in the transverse plane. The time it takes for the return of the longitudinal magnetization back to equilibrium is the T1 relaxation time, or the spin-lattice relaxation time, since the excited protons release their energy to the tissue (molecular lattice). The radiofrequency pulse will also align all the protons to the same phase. Local micromagnetic inhomogeneities due to the individual magnetic fields of each proton can cause a spin-spin interaction, whereby the aligned protons will precess at different frequencies. The time it takes

for transverse magnetization to decay to zero is due to the loss of phase coherence and is termed the T2 relaxation time, or the spin-spin relaxation time. T1 is typically longer than T2. The spins may also get out of phase due to the externally applied magnetic field. There will always be slight variations in the homogeneity of the applied magnetic field which will cause protons in different locations to precess differently due to slightly different magnetic field strengths.

The decay of transverse magnetization described by both spin-spin interactions and the external magnetic field is called T2*. T2* will always be less than T2. The time interval between applications of radiofrequency pulses is called the repetition time, or TR. Many pulses will be applied in order to form an MR image.

For MR imaging, the radiofrequency pulse used to tip the net magnetic moment is applied in the presence of a magnetic field gradient (Gz), and is modulated by a frequency envelope such as a sinc or Gaussian waveform. The magnetic field gradient will vary the magnetic field strength along an axis, thus causing only the protons with resonant frequencies within the frequency bandwidth of the radiofrequency pulse to be excited. These excited protons will emit a signal which is called the free induction decay (FID). One problem is that the FID dephases very rapidly and may disappear before it can be measured. Time is required to apply spatial encoding gradients to spatially encode the signal. Thus, the FID is intentionally dephased and rephrased (or recalled) at a later time, the echo time (TE). The use of Gz allows for slice selection in MR imaging, and the slice thickness can be controlled with either the gradient strength or by altering the radiofrequency pulse bandwidth. Once a slice has been localized, the MR signal must be localized in two other perpendicular directions to create an image of the slice; this is spatial encoding. After application of Gz, all the protons within a slice

are precessing at the same frequency. Applying another gradient, the phase encoding gradient

(Gy) in plane of this slice for some time will induce a phase shift, whereby some protons will experience a higher net magnetic field and will precess faster, and some will experience a lower magnetic field and precess slower. Once the Gy gradient is turned off, all the protons will experience the same field strength and precess at the same frequency, however there will be a phase shift along the y-axis. This is phase encoding. Applying another gradient perpendicular to Gz and Gy (Gx) during the reception of the recalled signal alters the frequency along this axis

(frequency encoding). Applying Gx during the reception of the signal (readout) provides positional information along that axis, and fills a line in ‘k-space’ corresponding to a specific Gy.

This happens for every TR interval, and for every TR a different Gy strength is applied to induce a different phase shift value. In summary, a unique frequency will represent an x-position while a unique phase will represent a y-position in k-space. K-space is the space in which the recorded data is written, and a Fourier transform is performed on k-space to get from frequency information to spatial information.

In order to emphasize the differences in spin characteristics within tissue, a pulse sequence is used to make the emitted signals dependent on T1, T2, T2*, or spin density. A pulse sequence is a sequence of repeatedly applied radiofrequency pulses that occur during an MR study [71]. If the pulse sequence emphasizes T1 characteristics, we would say that it is T1- weighted. The same applies for T2, T2*, and spin density.

1.5.6. T1-Weighted Imaging

T1-weighted imaging implies contrast is produced primarily from different T1 characteristics in tissue, and where T2 contributions are considered negligible. One method of achieving T1-weighted imaging is by using a spoiled gradient recalled echo pulse sequence

(SPGR). A characteristic of gradient recalled echo sequences is there is residual transverse magnetization at the end of each TR, which will be affected by the next few RF pulse cycles until it reaches steady state [71]. The spoiled gradient recalled echo pulse sequence eliminates

(‚spoils‛) the residual transverse magnetization, thereby reducing the T2* weighting and increasing the T1 weighting. Signals measured using an SPGR sequence can be converted to T1 using the SPGR equation,

−푇푅 푀표 ∙ (1 − 푒 푇1 ) ∙ 푠푖푛휃 푀푥푦 = −푇푅 (1 − 푐표푠휃 ∙ 푒 푇1 ) where Mxy is the measured signal intensity, Mo is the equilibrium longitudinal magnetization,

TR is the repetition time, and θ is the flip angle. Mo can be determined with a known T1, such as the native T1 (T10), which is the T1 pre-contrast injection. The SPGR equation can also be re- written to solve for T1

−푇푅 푇1 = 푀 − 푠푖푛휃 ∙ 푀 푙푛 푥푦 표 푀푥푦 ∙ 푐표푠휃 − 푀표 ∙ 푠푖푛휃

1.5.7. Measuring T1

Measuring native T1 (T10) can be quite challenging. An inversion recovery spin echo

(IR-SE) pulse sequence can be used to make a reliable assessment of T1, where several

measurements are made using a series of different inversion times (TI), and the resultant corresponding signal intensities are fitted to the equation

푇퐼 − 푆퐼(푇퐼) = 푎푏푠 푆퐼푖푛푓 ∙ (1 − 푘) ∙ 푒 푇1

where SI(TI) indicates the signal intensity at a specific TI, SIinf is the signal intensity from the spin system in thermal equilibrium, and k corresponds to the cosine of excitation angle of the inversion pulse [72]. It has been shown that an inversion recovery fast spin echo (IR-FSE) can also be used with good efficiency to calculate the native T1 relaxation time [73]. Several other methods have been investigated for quickly measuring T1, such as Look-Locker [74, 75] and variable flip angle methods [73, 76].

Chapter 2

2. Design and Characterization of a Multi-Modality Phantom

2.1 Introduction

The field of multi-modality imaging is emerging as a viable technique for providing improved differential diagnosis between tissue types. This is made possible due to the complementary nature of the different imaging characteristics of each modality, and their respective contrast agents. The increasing interest in multi-modality imaging has resulted in multi-modality phantom development. Some phantoms have attempted to mimic a structural organ such as the prostate [77], while others have sought to mimic vasculature. Some vascular phantoms compatible with x-ray, ultrasound, and magnetic resonance were designed to mimic large vessels [78], such as the carotid artery [79]. Other studies have sought to create a vascular phantom using real vessels harvested from cadavers [80, 81]. 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. These can be viewed as a major limiting factor for a multi-modality contrast phantom since many of the clinically approved contrast agents for computed tomography (CT) and MR can provide information on tumour characteristics at the microvascular level. To the best of the author’s knowledge, there is currently no microvascular multi-modality phantom present within the literature.

In this study we present a multi-modality microvascular phantom to assist in the development of contrast imaging techniques. The advantages of a phantom include low cost, reproducibility, and well-controlled characteristics. These advantages translate into an experimental setup that may be used for evaluating contrast kinetic pharmacokinetic models, comparing contrast imaging techniques, and motivating further development of multi-modality contrast studies. Previous microvascular phantoms for US involve a controlled flow setup where imaging was performed over acoustically transparent tubes with sub-millimetre diameters. Dialysis cartridge tubing has also been used as a microvascular mimic for contrast imaging studies. Details of microvascular phantoms using whole dialyzer cartridges for developing and testing US contrast imaging techniques have previously been published [82,

83]; however, the dialyzer cartridge's plastic shell might complicate results due to severe attenuation. Other studies sought to overcome this limitation by removing an area of the plastic shell to create an acoustic window [84], and in another design replacing it with a latex foil to contain any leakage that might occur through the dialysis tubing pores [85]. Cutting away part of the shell may provide a suitable imaging window, but is a risky procedure that may damage the fragile dialysis tubing. More recently, a microvascular flow phantom was constructed by attaching four sub-millimetre tubes to needles [86].

A previous MR study has demonstrated the clearance of Gd-chelate based contrast agents from dialyzers [87]; however, few dialyzer based MR phantoms for contrast kinetics imaging have been presented thus far in the literature. Heilmann et al. have studied contrast agent permeability using a hollow fibre module to mimic tissue perfusion [88], while a previous study used similarly sized fibres as a phantom to study diffusion mediated relaxation [89].

The purpose of this work was to design, construct, and characterize a microvascular phantom whose properties would provide a suitable platform for investigating the combination of multi-modality contrast imaging. Preliminary imaging evaluation of the phantom was performed using US and MR to evaluate the nature of contrast enhancement with each modality.

2.2 Materials and Methods

2.2.1 Design of a Multi-Modality Phantom

The overall goal of developing a leaky microvascular phantom for contrast imaging requires that contrast agents behave as observed in vivo, with ultrasound microbubbles remaining in the vasculature, and MR Gd-chelates diffusing freely between the intravascular and extravascular compartments. With these requirements in mind, several criteria for the microvascular phantom were established. First, the tubing and flow rates used should approximate those of microvessels. This arrangement of tubes would serve as the intravascular component of tissue. Second, a tissue mimicking material encompassing the tubes should facilitate the diffusion of contrast agents out from the tubes into this material, which represents

the extravascular compartment of tissue. Finally, the tubing should only be permeable to MR contrast agents and not US contrast agents. During the design process, we also considered the constraints imposed by the imaging devices. This includes non-magnetic materials for MR and ultrasound transparent materials for US. Figure 2.1 depicts a schematic of the phantom design.

Figure 2.1 Overall phantom design with labelled parts.

The basic design of the microvascular phantom consists of a chamber of agar gel, through which small porous tubes pass through parallel to each other. The tubes approximate the diameter of small arteries/large arterioles [58, 59, 90]. Parallel alignment and well-controlled spacing of the tubes was achieved by passing the tubes through two pieces of nylon mesh located at either end of the phantom. Using this approach, the centre-to-centre spacing was dictated by the pore spacing of the mesh. Dialysis tubing (Diapes PES-150, Baxter, ID = 200 µm, wall thickness = 30 µm) was used due to its permeability, size, and availability. The pore sizes

of this tubing is between 89 and 972 nm [91] (Figure 2.2), enabling low-molecular weight tracers for MR to diffuse out, while retaining ultrasound microbubbles within the lumen.

Figure 2.2 Cross section of dialysis tube wall. The wall is 30 μm thick and is characterized by a multi- layer ordering of pore sizes (Baxter®).

2.2.2 Fabrication of a Multi-Modality Phantom

The entire phantom is made from plastics to maintain MR compatibility. As well, the top and bottom surfaces of the imaging chamber were formed with 12.7 µm thick Mylar film which acted as an acoustic window for ultrasound imaging of the tubes. The length of tubing in the chamber was approximately 25 mm, to provide a region large enough for imaging. The tubes were arranged in a 5x5 grid in order to provide a well-controlled and repeatable geometry. The centre-to-centre spacing used between the tubes was 600 and 900 µm. Future iterations could customize the spacing to another value. Irregular or more random orientation and arrangement of tubes, as well as different tube spacings are also possible. The dialysis tubes

were bundled at each end by potting them in epoxy (EPO-TEK 301, Epoxy Technologies,

Billerica, USA), and then carefully cutting the ends with a scalpel. The cut tubes were inspected under a microscope to ensure they remained open; a fine tipped needle was used to reopen any tubes closed during the cutting process. This method was found to enable patent and parallel flow through the tubes in the phantom. The inner volume of the container around the tubes was filled with agar (0.5 wt%, Sigma-Aldrich Canada Ltd., Canada) by slowly injecting liquid agar between 50-60°C through a small opening in the side wall, which was plugged and sealed once the chamber was fully filled to avoid any possibility of air bubbles in the phantom. The agar gel which was used to surround the tubes provides a compliant tissue mimic [92, 93]. Flow through the phantom was achieved by coupling the end caps to standard ¼‛ hose barbs in the wall. Figure 2.3 shows a completed unit, as well as a close up of the dialysis tube ordering. A detailed step-by-step fabrication process is described in the appendix.

2.2.3 Characterization of Multi-Modality Phantom

Initial flow experiments were performed using the microvascular phantom to characterize the relationship between pressure, flow and velocity through the dialysis tubes.

Pressure was applied to the phantom by using a constant head of water in a reservoir, as shown in Figure 2.4. The flow lines connecting the phantom to the experimental setup were taken from an IV drip line kit and had an inner diameter of 2.5 mm. The length of tubing from the mixing chamber to the phantom was 15 cm. The pressure felt across the phantom using a gravity flow setup (utilizing a reservoir holding tank) was compared against using a flow pump (Harvard

Pump 11 Plus, Harvard Apparatus, Holliston, USA). Given a constant head pressure, the flow rate and mean flow velocity can be calculated from the amount of water that passes through the phantom in a given time. Assuming flow is laminar, the mean flow velocity is calculated as the measured flow divided by the cross sectional area. The driving pressure was determined using a pressure gage (MG-9V, SSI Technologies, Janesville, USA).

Figure 2.4 A schematic diagram of the experimental setup.

The contrast imaging experimental flow setup had to account for the unique properties of the contrast agents. Gadolinium-based chelates are heavier than water while microbubbles tend to float, making it difficult in an in vitro setting to maintain homogeneous mixing of the contrast agent with the water flow line. A diluted bolus, along with a custom-made inline mixing chamber placed before the phantom, ensured a well mixed environment. The flush

volume used to administer the contrast agent into the flow line ensured that the entire contrast agent left the injection line.

The kinetics of contrast enhancement with US and MR imaging were studied using two phantoms, with 600 µm and 900 µm centre-to-centre spacing between the 5x5 arrangement of dialysis tubes. Water was flowed into the phantoms at a constant rate of 0.047 ml/s, which translates into a flow velocity within arteriole's physiological range [90] within each fibre. Use of a fixed-height reservoir produced a driving pressure of ~35.6 mmHg across the tubes. The gravity flow setup was used instead of a pump mainly for simplicity and MRI-compatibility.

An automatic syringe pump was used to deliver contrast agents into the flow line for all experiments in order to achieve consistent injection rates and volumes. Due to the flow rates and length of tubing used, the delay between injection and arrival of contrast agents to the imaging chamber was approximately 30 s. Since the phantoms are one time use only, there was only a single contrast experiment performed for each phantom.

2.2.4 Contrast-Enhanced Ultrasound Imaging

Contrast enhanced ultrasound imaging (CE-US) was performed using a Philips iU22 ultrasound scanner (Philips Healthcare, Andover, USA). Definity (Lantheus Medical Imaging,

N. Billerica, MA, USA) microbubbles were activated following the manufacturers recommendations and diluted with de-ionized water to a volume ratio of 0.13:0.87. A 0.1 ml diluted bolus was injected into the flow circuit using a 2 ml flush of water at a rate of 1 ml/s.

The injection of the contrast agent was initiated after a 15 second delay from the start of ultrasound imaging. The imaging exam continued for an additional five minutes to capture the

complete signal enhancement and return to baseline within the tubes. Imaging was performed using an L9-3 transducer (9-3 MHz frequency range) and a contrast general sequence for detection of the harmonic response from microbubbles (Frame Rate = 11 kHz, Depth of Focus =

6 cm, MI = 0.03, Compression = 40, Receiver Gain = 70%, Time Gain Compensation (TGC) = flat,

Angle of Insonation (from horizontal) = 60°). These parameters were chosen to produce a strong harmonic signal while avoiding signal saturation and bubble disruption.

2.2.5 Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Dynamic contrast enhanced MRI (DCE-MRI) was performed on a 1.5T scanner (Signa,

GE Healthcare, USA) using a custom-made single channel RF receive coil enabling high resolution imaging of the phantom. The baseline T1 of the phantom prior to injection of the contrast agent was measured using an inversion recovery fast-spin echo sequence (TR = 4000 ms, TE = 24 ms, TI = 50/200/800/1200/1600/3200 ms, BW = 15.63 kHz, ETL = 8, Nx/Ny/NEX =

256/256/1, FOV = 45 mm, slice thickness = 5 mm). In order to segment the compartments within the phantom, i.e. inside/outside the tubes, a high SNR image (TR = 12.5 ms, TE = 2.9 ms, Flip

Angle = 20, BW = 15.63 kHz, Nx/Ny/NEX = 256/256/5, FOV = 45 mm, Slice Thickness = 5 mm) was acquired transverse to the phantom which was used in subsequent analysis as a mask. In addition, the flow line was arranged such that the inflow and outflow tubes passed through the transverse slice, as shown in Figure 2.4. The contrast agent Omniscan (GE Healthcare, USA)was diluted with de-ionized water (50:50 volume ratio) and injected into the flow line within a 0.1 ml bolus at 1 ml/s using a 2ml flush and an MR compatible pump (Spectris Solaris EP,

MEDRAD Inc., Warrendale, USA). Injection of the contrast agent was delayed 15 seconds after

the imaging sequence was initiated. Dynamic contrast-enhanced imaging was performed using a 2D fSPGR acquisition (TR = 12.5 ms, TE = 2.9 ms, Flip Angle = 20, BW = 15.63 kHz,

Nx/Ny/NEX = 256/256/1, FOV = 45 mm, Slice Thickness = 5 mm) where 256 images were acquired over approximately 14 minutes with a temporal resolution of 3.3 s and no delay between acquired images.

2.2.6 Data Analysis

The DICOM MR image data was transferred from the imaging systems to a PC for off- line analysis using MATLAB (The MathWorks Inc., Natick, USA). A region of interest (ROI) was drawn around the enhanced region. This border was drawn from the image where the average signal enhancement over the imaging exam was highest. Regions where the tubes were not perfused were excluded. Once the regions of the tubes were subtracted from the ROI, this mask was used for the gel compartment. Determining the region of the tubes was found through the high SNR image acquired. The high SNR MR image of the phantom acquired prior to contrast imaging was used to create a binary mask for separating the enhancement signals from regions within and outside of the tubes (Figure 2.5). Since the imaging slice was normal to the direction of flow, the tubes display flow-related signal enhancement due to time of flight effects. The high SNR image allows enhanced visualization of the fibres, which facilitates separation of the fibres from the gel with image processing. From this, three regions of interest could be analyzed: the input entry line prior to the phantom, the intravascular signal within the tubes, and the extravascular region in the agar. The output of the phantom can also be analyzed and the mask for it is present in Figure 2.5, but will not be discussed in this work. Note that the

masks were only applied to MR data. These masks were used to isolate the different compartment signals by multiplying them across the data sets and averaging the signal within each mask. In order to compare signals from different regions, the signals had to be adjusted to a common baseline due to signals from different regions having different initial intensities. The common baseline signal used was the gel baseline. Using the native T1 (T10) values calculated, the equilibrium longitudinal magnetization (Mo) can be calculated:

−푇푅 푀표 ∙ (1 − 푒 푇10 ) ∙ 푠푖푛휃 푃푟푒 퐶표푛푡푟푎푠푡 푆푖푔푛푎푙 퐴푣푔 = −푇푅 (1 − 푐표푠휃 ∙ 푒 푇10 )

Knowing Mo and T10 allows conversion of the measured signal intensity curves to T1 values

−푇푅 푇1 = 푀푒푎푠푢푟푒푑 푆푖푔푛푎푙 퐴푣푔 − 푠푖푛휃 ∙ 푀 푙푛 표 푀푒푎푠푢푟푒푑 푆푖푔푛푎푙 퐴푣푔 ∙ 푐표푠휃 − 푀표 ∙ 푠푖푛휃

which can be converted to contrast agent concentration [CA] via

1 1 − 퐶퐴 = 푇1 푇10 푟1

Ideally this process would be done on a pixel-by-pixel basis to create parametric maps. For the purpose of this thesis, the average signal within the ROIs was used instead to demonstrate an overall behaviour characteristic of the phantom. This limits an assessment to be made on the

non-uniform nature of contrast agent diffusion from the tubes which can be observed from cross sectional images (Fig. 2.7) and parametric maps. Greater discussion regarding this is found in Chapter 3.

Figure 2.5 A high SNR axial MR image (A) acquired for generating a mask (B) to separate out the compartment signals. The resulting binary mask was only applied for MR data.

Ultrasound DICOM image data was analyzed using QLAB (v 6.0, Philips Ultrasound,

Bothell, WA, USA) by drawing a region of interest around the area where microbubble echoes were present. Intravascular compartment signals for both US and MR were fitted to a gamma variate function. The formula was fitted using a Levenberg-Marquardt approach for nonlinear least-squares fitting and optimization. A user defined to was used to render the optimization more robust; fitted parameters α and β were determined, and the coefficient of determination

(R2) was calculated. The parameter K is not included for comparison since it is a scaling factor and absolute concentrations between US and MR were not considered.

2.3 Results

2.3.1 Characterization of Multi-Modality Phantom

Figure 2.6 displays the average pressures measured due to a given average flow rate delivered either from a gravity flow tank or a flow pump using a phantom with 600 µm spacing between tubes (n = 5). There is good agreement shown, suggesting that the use of a gravity flow tank would yield similar results to that of a flow pump. This motivates the use of the gravity flow tank, which simplifies the experimental setup at the MR. Also shown in Figure 2.6 is the result from a phantom with 900 µm spacing between the tubes and the flow pump. The results suggest that given a fixed number of tubes, the pressures felt across the phantom is independent of tube spacing.

The dialysis tubing proved to be effective as a vessel mimic for a leaky phantom.

Figure 2.7 shows cross sectional images of the 600 µm spacing phantom at different periods of enhancement. Diffusion of the MR contrast agent into the gel is seen by signal intensity changes in the regions between the tubes. Decrease of this signal over time indicates diffusion of contrast agent in the gel back into nearby tubes. As well, movement of the MR contrast agent away from the outer ring of tubes in the gel was observed as a gradually enhancing region shown over time. Microbubbles are too large to pass through the pores of the dialysis tubing, and thus represent an intravascular signal. This was shown by looking at the cross sectional images from US, where the region of signal did not enhance outwardly at all. The cross sectional images from the 900 µm spacing phantom demonstrated the same characteristics.

Figure 2.7 Cross sectional images from different periods of the imaging scan for both ultrasound and magnetic resonance. The periods presented are the arrival of the bolus within the imaging slice, the peak enhancement observed, and the decay of the enhancement. Note that the MR image contrast has been adjusted in this figure to highlight enhancement outside of the dialysis tubes.

2.3.2 Contrast Kinetic Curves

Microbubbles were shown to remain only within the tubes; thus, the US contrast kinetic curve represents an intravascular compartment. For MR imaging of the mimicking vascular tissue mimic, the mask successfully separated the compartmental signals, namely the intravascular and extravascular compartments. Figure 2.8 compares the US and MR signals acquired from the imaging chamber through which the tubes intersected for the 600 μm spaced phantom. The arrival of the microbubble bolus occurs at approximately 32 seconds, which corresponds with the arrival of the Gd bolus. Thus, precise timing of the bolus arrivals is demonstrated for both modalities with this experimental setup. As well, both the US tube signal and the MR tube signal peak at approximately 1.2 minutes, demonstrating a similar rise time (slope). However, the washout phase of the tube signal curves are much different, where the US signal declines to baseline within approximately 3.5 minutes while the MR signal washes out much more gradually. The peak of the extravascular MR compartment curve (MR: Gel) intersects the MR intravascular curve (MR: Tube), indicating the point at which the concentration of Omniscan is equal between the intravascular and extravascular compartments.

This intersection occurs after approximately 2.6 minutes.

The 900 μm spaced phantom also demonstrated similar trends. The microbubble bolus arrived within the imaging plane around 32 seconds, while the Gd bolus arrived at approximately 26 seconds. Along with the 600 μm phantom, the US and MR tube signals both peaked at approximately 1.2 minutes, demonstrating a similar slope. As well, the washout phases for the US and MR signals were different from each other, with MR washing out much more gradually. The intersection of the two MR compartment signals occurred after approximately 3.6 minutes. Figure 2.9 depicts the US and MR curves for the 900 μm phantom on the same plot.

2.3.3 Gamma Variate Fit

The MR signal taken from the inflow line represents a true intravascular signal in a non-leaking region; therefore, it was fitted to the gamma-variate model. Likewise, the US signal was fitted to the gamma-variate model; Figure 2.10 plots both of these curves for the 600 µm spacing phantom. The agreement between the measured MR inflow signal and the gamma variate model was very good, with an R2 value of 0.986 (Figure 2.11). Although the US signal tail settled at a slightly offset baseline value, there was also good agreement between the measured US signal and its fit, with an R2 value of 0.929 (Figure 2.11). The fit to the measured inflow MR signal was compared to the fit of the US signal in order to compare intravascular characterization; the R2 value was 0.955. A phantom with 900 µm spacing between the tubes

was also evaluated. Table 1 summarizes the R2 values for both phantoms, as well as the shape parameter fitting values (α and β) from the gamma variate fit.

Figure 2.10 The MR Inflow and US Tube signals for the 600 µm phantom, which represent signal that has not leaked into the extravascular space.

Table 1 Fitting parameters from the gamma variate fit along with R2 for the intravascular compartmental signals.

Parameters MR 600 US 600 MR 900 US 900 α 1.14 ± 0.03 2.94 ± 0.04 0.90 ± 0.03 2.57 ± 0.03 β 0.449 ± 0.009 0.237 ± 0.003 0.61 ± 0.01 0.272 ± 0.003 R2 0.986 0.929 0.985 0.928 R2 between derived fits for 0.955 0.866 US and MR

Finally, if we compare the two MR inflow signal curves to each from each respective phantom, we find an R2 value of 0.957. For the US signal curves, we get an R2 of 0.958.

2.4 Discussion

A microvascular phantom suitable for multi-modality contrast imaging studies has been developed. The fabrication of the phantom is straightforward and utilizes readily available components, compatible with MR and US imaging. The design of the phantom allows customization of the imaging chamber volume, tissue mimicking material, and flow rate. The flow rates and velocities achieved through the phantom overlap with those observed for arterioles; this performance was observed across both phantoms where tube spacing was 600

µm and 900 µm. The initial characterisation of ultrasound and MR contrast imaging demonstrated the compatibility of the phantom with both modalities and their respective contrast agents. These results suggest that the phantom would be suitable for investigating contrast kinetics between modalities to understand their inter-relationship and to explore the potential to combine this information for a more comprehensive characterization of tumour microvasculature.

The experimental setup proved to be well suited for comparing ultrasound and MR contrast kinetics. The arrival time, peak time, and rise of the "tissue" intravascular MR signal, that is to say the MR signal arising from within the dialysis tubes, was almost identical to that of the US signal intensity; however the signal decay was different between the two measurements.

The MR signal decayed much slower due to extra-vascular contrast agent in the agar gel diffusing throughout the gel and back into the tubes. This mimics the situation in vivo, where the intravascular nature of US contrast agents results in a much shorter presence within the vasculature. These differences in the kinetics of US and MR contrast agents are readily apparent

using the phantom. There appears to be different initial slopes between the curves for the intravascular signal components between US and MR, where the US slope was slightly less than the MR. We would expect these slopes to be similar if not the same since there is no contrast agent outside of the tubes during the signal acquisition. The discrepancy could be due to the differences in contrast agent physical properties. Another possible explanation is the possibility of experimental error in agent handling; there is a slight bump in the slope of the US curve which could indicate a non uniform administration of the microbubble bolus.

There was good agreement when comparing the curves produced between the two phantoms. Both the ultrasound curves and the MR inflow curves from the two phantoms had similar profiles when compared against each other. As well, they both behaved similarly to the gamma variate function which previously has been used to describe the first-pass of a bolus

[27]. This leads the discussion towards whether or not the two contrast agents exhibit similar kinetics when no leakage occurs. While there is good agreement from the R2 value in the 600

µm spaced phantom to support this, different parameter values were derived from the gamma variate fits. Further work would be needed to clarify whether or not microbubbles are suitable to complement MR kinetic studies as a descriptor for the intravascular component.

The separated intra- and extra-vascular MR kinetic curves taken from gel signal exhibited similar characteristics to what would be observed in vivo; how the peak of the extravascular curve intersects the intravascular curve to denote the point of equal concentration between the compartments. This was observed for both phantoms with different spacings between tubes. The later arrival of this intersection point in the phantom with 900 µm tube spacing demonstrates the increased distance with which the contrast agent must travel before it

diffuses completely from one tube to another. The mechanism of diffusion, however, may contain a convective component. While the contrast agent should travel down its concentration gradient from within the tubes into the gel, there may be a convective transport component whereby water travels through the pores of the tubes and into the outside space. This possible convection would contribute to contrast agent extravasation as well. Considering that the enclosed phantom construction is rigid enough that the phantom will not expand beyond a point due to the increased water in the gel, a steady state should be reached whereby any water travelling out of the tubes is matched with water in the gel travelling back into the tubes.

Understanding these mechanisms and its possible influences on enhancement trends and patterns will be essential for moving forward with phantom characterization.

The tail-end of the MR compartment curves demonstrates the extravascular signal as higher than the intravascular signal, with both signals decreasing slowly. The extravascular signal decrease will continue due to contrast agent leaving the gel; however, there will be residual agent left as a border around the tubes on the outskirts. Unfortunately there is no clearance in this region, and thus the extravascular signal will never completely return to the original baseline value. A possible solution to this design is proposed in the following chapter.

Being able to resolve compartmental signal enhancement due to contrast agents provides a good platform for investigating pharmacokinetic models. The highly ordered structure of the tubes enables control over the intravascular volume fraction. This information could be used to either remove a fitted parameter from the modeling process or to compare the predictive results from a variety of modeling methods. This is also the case for flow rate, which some models attempt to account for. Knowing the actual values of these fitted parameters could

enable better insight into the significance of Ktrans and its interpretation. Finally, in addition to the predictive accuracy of pharmacokinetic models, the possibility of combining measurements with ultrasound and MRI could be investigated. Current techniques for fast imaging with potential applications to contrast studies could also benefit from a microvascular phantom for comparison against standard imaging techniques. As well, the importance of temporally resolved contrast kinetics for MR is of interest and may be evaluated alongside US measurements. Additionally, fast imaging techniques often sacrifice on spatial resolution in order for the increase in temporal resolution, which leads to novel methods of acquiring kspace.

Using this microvascular phantom, proposed fast imaging techniques may be evaluated for their ability to temporally resolve contrast kinetics while providing sufficient spatial resolution, as well as assess how the acquisition schemes may affect the signal enhancement.

There are a number of limitations with the microvascular phantom described in this study. The most important limitation is the current single use of the phantom. Ideally, multiple experiments would be conducted in order to test for reproducibility and consistency. This limitation may be overcome by using a different tissue substitute material that can be easily removed. A possible solution would be low melting point agar which could be removed from the chamber without damaging the surrounding phantom structure. Another idea is to use a fluid such as water in the chamber, and keep it evenly mixed using a mixer; greater details for these ideas are in the next chapter.

The dialysis tubing is an approximation of arterioles, although the diameter is somewhat larger. Similarly, the ordering of the tubes represents an idealized situation which is not well representative of in vivo. While the dialysis tubes accomplish the task of mimicking a

leaky vessel, it does present a couple potential modeling challenges. The wall thickness is not insignificant at 30 µm thick, and could be considered a compartment in and of itself where contrast agent may reside. As well, the pores in the tube walls are much larger than the endothelial tight junctions within arterioles. These physical limitations of the dialysis tubes may influence the results and should be considered with more rigorous analysis methods. In addition, not all of the contrast agent that leaks into the agar gel within the phantom is able to diffuse back into the tubes, seen mostly at the edges of the 5 x 5 matrix. A possible solution would be to surround the 5 x 5 matrix with a ring of tubes which are only perfused with water.

A proposed design is described in the following chapter. .

A manufacturing limitation is that the epoxy potting process relies heavily on the ability of the fabricator, and may result in blockage within 1 or 2 tubes. This was observed in the phantom presented. While we acknowledge this limitation, we believe that this is a limitation that may simply be resolved with improved manufacturing technologies. The experiments conducted with the microvascular phantom represented a first-pass scenario, which does not account for recirculation effects. Future studies can address this by using a re-circulating flow setup that is MR compatible. Additionally, a re-circulating flow setup would potentially produce more realistic kinetic curves. In vivo, recirculation of the MR contrast agents contributes to a new signal intensity baseline that is higher than it originally began. Such is not the case with the current setup, where signal intensity returns to the original baseline after some period of time. Finally, we believe that the US curves shown in Figures 2.8 and 2.9 that display the tail of the curve returning to an offset baseline is due to a few large microbubbles present in our bolus solution that remained trapped within a tube. While this did not allow our signal

curve to exhibit a complete washout, the corresponding model fits and their good agreement with their analogous MR counterparts suggest that the gamma variate model is adequate at describing first-pass dynamics of contrast agents.

2.5 Conclusions

Here we have presented a microvascular phantom for ultrasound and magnetic resonance contrast imaging. The characterization of the phantom has shown that the phantom is suitable as a leaky phantom for MR contrast imaging, yet remains an intravascular system for

US contrast imaging. Thus, this phantom motivates contrast studies that seek to combine the information gathered with each individual modality in order to provide a more comprehensive assessment of vasculature. Further applications being investigated include dual modality pharmacokinetic models and comparing the dynamic behaviour of a range of contrast agents, including intravascular MR agents.

Chapter 3

3. Conclusions and Future Works

3.1 Summary of Thesis Contributions

Multi-modality imaging is an attractive solution to address individual imaging modality limitations and to potentially ascertain better characterization of tissue through combination of complementary measurements. Much of the foundational research and theory has been conducted for the major imaging modalities, i.e. ultrasound, magnetic resonance, computed tomography, and future work seeks to address the identified modality limitations.

Multi-modality imaging is one such possible solution. The work presented in this thesis aims to contribute to the combination of ultrasound and magnetic resonance data into a more complete model for tumour characterization. The presented phantom is not only compatible for ultrasound and magnetic resonance imaging, but also complements the contrast agents available for each modality. The compatibility with microbubbles, which remain intravascular, and the Gd-chelates, which may traverse into the extravascular extracellular space, makes this

leaky phantom unique amongst multi-modality phantoms. The results demonstrate the utility of the phantom for investigating multi-modality contrast-enhanced imaging, and motivate further investigations which include contrast-enhanced computed tomography and applications with specific imaging techniques. In addition, this phantom could serve as a platform for validation of contrast kinetic models aimed at determining underlying tissue properties based on contrast imaging information.

3.2 Future Works

3.2.1 Optimizing Design and Fabrication

The phantom design presented is one possible configuration among many possibilities.

The method of fabrication permits customization of many phantom characteristics, including the number of tubes, their spacing with respect to each other, and their spatial arrangement pattern. The 5-by-5 matrix shown here is highly ordered and predictable. In order to more closely mimic tumour vascular networks, the tubes could be more heterogeneously distributed through the phantom. A chaotic ordering could be achieved by not threading the tubes parallel to each other and allowing them to be kinked and twisted around each other, effectively making them a ‚tangle of vessels‛.

Future designs to further improve the phantom for mimicking the microvasculature would be to incorporate multiple inputs and outputs. Along the lines of this idea, of interest would be to study the clearance of exogenous contrast agents due to the lymphatic system. One possible design for studying this would be to fabricate a phantom with two inputs/outputs,

where one input leads to a 5-by-5 arrangement of tubes while the other input leads to a ring of tubes around this 5-by-5 arrangement (Figure 3.1). Contrast agent can be flowed through the inner 5-by-5 arrangement, and the clearance of the contrast agent can be observed at the outer edges where the ‚lymphatic tubes‛ effectively act as sinks for diffusing contrast agent. (show figure) Other iterations on this idea could interleave ‚contrast‛ tubes and ‚lymphatic‛ tubes.

Another idea for consideration would be to contrast the Gd-chlelate kinetics within a leaky phantom and a non-leaky phantom. This would be of interest for pharmacokinetic modeling applications, and for comparing truly intravascular contrast agent kinetics

(microbubbles, SPIOs) with small paramagnetic contrast agent kinetics under two different vessel states (leaking/non-leaking). Presented here is a leaky phantom, which is evidenced by beads forming on the outside of the tubes when water is forced through them (Figure 3.2). In order to make the phantom an intravascular system, the phantom may be parylene coated. A

test phantom was parylene coated with no noticeable deformations made to the phantom.

Flowing water through the parylene coated phantom did not produce beads of water on the outside of the tubes (Figure 3.2).

3.2.2 Investigations Using Computed Tomography

The contrast agents commonly used with computed tomography (CT), mostly iodine based, are exogenous just like the Gd-chelate based agents used with MR. In principle, the experiments and applications presented in this work should be reproducible with perfusion CT.

Preliminary investigations using the phantom and perfusion CT have been conducted on an older phantom. Initial observations were that the contrast agent used (Omnipaque, GE

Healthcare, USA) was very heavy and viscous; thus, it did not mix well with the water in the flow line. Analysis of the dynamic image data set showed increase in signal which immediately reached a plateau. The signal enhancement did not decrease afterwards. Figure 3.3 depicts

signal enhancement around the dialysis tubes, along with the signal enhancement curve. One of the CT limitations would be separating the signal into its compartmental components. One possible solution would be to register the CT and MR images in order to use the mask generated from MR for CT. Another possible solution would be to use a microCT with high spatial resolution to resolve the tubes; an initial scan with a microCT showed promise that it could differentiate the tubes from the gel.

3.2.3 Applications With Specific Imaging Techniques

The advent of a leaky phantom opens up a whole host of interesting studies that can be performed with MR. Recent fast imaging techniques such as PR-TRICKS and SENSE may benefit from such a phantom in order to evaluate the influence of the acquisition method on signal and noise characteristics, as well as evaluating the high temporal resolution and its advantages when evaluating contrast kinetics.

The leakiness of the phantom is not restricted to contrast agents, as water may also diffuse across the boundary, traveling between the individual compartments. This property may find applications with diffusion weighted imaging, which seeks to map apparent diffusion coefficients and has found use as a sensitive indicator for early detection of ischemic injury [94].

Arterial spin labelling (ASL), which may have applications for a variety of acute and chronic cerebrovascular diseases [95], is another imaging technique of potential interest. ASL uses water as an endogenous tracer by magnetically labelling water. Tissue magnetization is altered downstream by the magnetized inflowing water. The presented phantom may provide a suitable platform for comparative ASL techniques (continuous or pulse), as well as novel sequence development.

3.2.4 Single Tube Experiments

Future investigations should describe the mechanism of action for MR contrast agent passage inside the tubes into the space outside. Previous studies attempting to measure contrast diffusion may provide insight for our particular phantom [96, 97]. It is likely that there is fluid convection at work within our phantom. In order to determine the properties of convection, a simple single tube experiment is proposed. Using a flow setup similar to the one described in Figure 3.4, a phantom is constructed with only 1 tube passing through it. Using a known flow rate, an infusion with constant Omniscan concentration is passed through the tube.

The MR scan sequence used would be an axial IR-FSE with a TI set to the null point of the agar gel. Thus, the image produced should display nulled signal where there is gel. As the infusion

flows through the tube, Omniscan will begin to pass through the pores of the dialysis tube into the gel structure. This will produce a ring of enhancement around the tube, indicating

the distance the Omniscan has travelled from the tube. Dynamic scanning of a constant infusion will allow determination of the diffusion rate of Omniscan, and could help elicit the mechanism of action for passage across the wall boundary, specifically determining potential convective influences on the diffusion process.

3.2.5 Alternative Configuration for Imaging Chamber

While the gel structure within the imaging window provides a solution as a tissue mimic, the current iteration does limit the phantom to single use. A different gel with low melting point may be an alternative for repeated phantom use. While a bit labour intensive, the mylar films covering the imaging chamber could be removed to melt out low melting point gels

from the phantom. The advantage of the low melting point is to reduce potential damage to the phantom materials; however, having to replace the mylar films is not ideal. Another possibility is to replace the gel with a viscous fluid mixture. A highly viscous mixture that does not ebb through the tubes is a possible solution for phantom repeatability. In this case, it would also be possible to match the fluid density to the Gd-DTPA-BMA mixture flowing through the tubes with a high concentration sugar mixture. Replacing the mixture would be similar to the low melting point gel scenario. A much simpler approach would be to fill the chamber with water.

A problem in the case of just water though is to overcome the sinking of Gd-based agents.

Having an evenly mixed chamber using a mixer would overcome this problem. Figure 3.5 describes a possible schematic.

A mechanical mixer would not be possible to use within the MR bore. In order to simplify the mechanism and overcome this limitation, an air channel is attached to the wall. An air current through the channel turns the blades of a fan, which connects to a mixer within the imaging chamber. This will ensure that any Gd-chelate based contrast agent that exits the tubes through the walls would evenly mix with the chamber fluid. Replacing the water is a simple procedure requiring opening the plug in the side wall and draining the fluid.

3.2.6 Further Analysis Methods

Being able to apply a mask to the data set allows for separation of the different compartmental signals. However, what’s more commonly done today is the application of compartmental models and analysis to extract the different signal components. The advantage of compartmental analysis methods is to derive the Ktrans parameter, which provides an indication of the system’s flow and surface area permeability product. As well, it was previously noted that the phantom displays heterogeneous diffusion patterns from the dialysis tubes. This trait lends itself well for parametric map analysis on a pixel-by-pixel basis, in order to provide spatial locations of parameter variation. Along with the fitted parameters derived from compartmental analysis, simple curve characteristic parameters may be extracted.

Summary parameters, such as mean transit time and time to peak, may provide a relative comparison to be made. They are not as quantitative as compartmental analysis; however, they are easier to calculate and may still prove useful for visualization purposes. As an example,

Figure 3.6 depicts the time to peak for the 600 µm spacing phantom. Note that the parametric map has separated the tube space from the gel space based on different time to peak values on a

pixel by pixel basis. In this manner, there are many different options for analysis, and the phantom presented is a tool that can facilitate the comparative analysis studies.

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5. Appendix

5.1 Procedure for Phantom Fabrication

1) Machine all the necessary parts out of acrylic

a) Materials: 2x rings, 2x ring holders, 2x wall pieces, 2x end pieces, nylon mesh with 300

micron hole spacing, dialysis tubing

b) Tools: tweezers, acupuncture needle, 2x wall pieces that have the slots spaced 1 mm less

(wall jig pieces), fabrication jigs to help hold your phantom

Figure 5.1 A) Manufactured phantom parts. B) Tweezers and accupuncture needle used during fabrication process.

2) Carefully remove dialysis tubing from cartridge (Baxter, Deerfield, USA). Throw out any

damaged tubes.

3) Trace the rings onto the nylon mesh, and cut out two nylon circles

4) Glue the nylon circles into the ring holders with #4 solvent cement, and insert the rings over

top. Make sure to press down to push the rings in tightly to clamp the nylon down.

Optionally use a clamp.

5) Place the ring holders into the wall jig pieces and

6) Find the centre of your phantom using the acupuncture needle. From this you can find your

starting position.

a) It’s best to make sure your initial tube runs perfectly parallel through the phantom, as

this first tube will dictate the orientation of the remaining tubes.

7) Begin threading tubing through the meshes in your desired pattern.

a) This is the longest step, and can take many hours to complete depending on the number

of tubes and the pattern you want. I recommend using tweezers that are slightly bent at

the tip so they only make contact at the very tip. This type of tool is very useful for

guiding the tubes mechanically while not crushing the tubes. As well, make sure the

tubes have no kinks in them before threading, as that shows they are already partially

damaged.

Figure 5.2 Threading of the fibres through the mesh, where A) depicts the first row of a 5 x 5 arrangement and B) depicts a finished 5 x 5 arrangement with silicon (Step 8).

b) If threading many tubes, keep spacers in between each row, 1 at each end and 1 in the

chamber. This is essential for visualizing the tubes, keeps them from tangling with each

other, and will help you separate the tubes in the next step when you need to part the

tubes in order to apply silicon between the tubes.

8) Apply silicon (65AR, Permatex, Hartford, USA) in between and all around the tubes at one

end. Make sure the nylon is completely covered with silicon. This is the step where you

begin to remove the end spacers

Figure 5.3 A) Spacers separating the different rows of tubes. B) Silicon applied to the nylon mesh, which holds the tubes to the mesh.

9) Let the silicon sit overnight since it takes ~24 hours to completely cure/dry.

10) Repeat steps 8 & 9 for the other end of the phantom.

11) Remove the wall jig pieces and all the centre chamber spacers.

12) Take one of the wall pieces, and apply a thin layer of # 16 solvent cement to both of the

depressed slots

13) Place the wall piece so that each ring holder piece slides into a depressed slot

14) Be careful to wipe excess glue away

15) Repeat steps 12 & 13 for the second wall piece.

16) Note that the tubes will stretch out to become straighter since the slots on the wall pieces are

farther apart then on the wall jig pieces by 1mm.

17) Lightly clamp the fixture together such that the walls glue well with the ring holder pieces.

Alternatively, place a book on top of the fixture.

18) Wait ~ 30 minutes.

19) Wet the tube bundles. This helps slide the end piece over the tubes.

20) Carefully slide 1 end piece over one tube

bundle, be sure to ensure that no tubes

bend or crimp.

21) Use #16 solvent cement (Weld-On 16, IPS

Corporation, Compton, USA) to seal the

end piece to the ring holder. Alternatively,

you may use #4 solvent cement (Weld-On

4, IPS Corporation, Compton, USA).

Downside of #16 is it is very sticky and

viscous, yet easy to apply. Downside of #4

is that it is very flowable and hard to

apply, yet dries quite quickly. This step is

up to the user’s discretion.

22) Use your jig to clamp the end pieces to the

fixture, or hold it tightly together with your Figure 5.4 A phantom with an end piece placed over the tube bundle. Note the jig used to support hands. Depending on the solvent cement the phantom upright.

you use, this step can take anywhere from 1-30 minutes.

23) Wait until the tube bundles are dry.

24) Set the oven to 65 degrees Celsius.

25) Use Epotek epoxy 301A/B , mix up 1 grams worth.

26) Use a 1ml syringe and a blunt tip 18 gauge needle to withdraw the epoxy, flick any large air

bubbles out.

27) Slowly apply epoxy to the tube bundles. You don’t need a lot, at most 0.4 mls per bundle

a) Apply on all sides of the bundle.

b) Have one hand covered with a latex glove, and use that hand to spread the epoxy out

over the bundle, and at the same time wipe off any excessive left over epoxy.

c) NOTE: there should be some epoxy that makes contact with the tubes and the inner

fixture walls.

28) Place the phantom in the oven (which should be at 65 degrees Celsius by now). Support

each bundle end with some scrap acrylic so that it does not droop and touch the oven

floor/surface.

29) Wait at least one hour.

30) Remove the phantom, turn off the oven. Let the phantom cool down.

31) Use a small 25-26 gauge needle and 3/5 ml syringe to fill the spaces between the tube

bundles and the end pieces of the fixture with silicon. Make sure there is a tight seal of

silicon such that flow can only enter the fixture through the bundles. Apply silicon around

the bundle and the surface of the acrylic end cap piece.

32) Mix up some more Epotek epoxy 301A/B

33) Using a syringe & needle, apply the epoxy around one of the flat surface that surrounds the

inner chamber. You don't need a lot, just enough to cover the surface area.

34) Place your mylar film over the epoxy, and stretch it so that it is as taught as possible. The

thinner the mylar the better.

35) Flip the phantom and repeat steps 34 &35 for the other bottom surface.

36) Place a weight on top of the phantom to ensure a good seal of the mylar sheets. An

aluminum plate works well.

37)

Figure 5.5 A mylar sheet sealing the imaging chamber. The phantom is now ready to be filled with a tissue mimicking material.

38) This step should only be followed if there are a large number of tubes, such that it is impossible to cut

the bundle with a scalpel. An example number of tubes would be 100.

a) Acquire ~ 300 mL of liquid nitrogen (wear safety equipment!), two solid plates

(preferably aluminum), and a pair of latex gloves

b) While wearing your gloves, dip one of the now hardened tube bundles into the liquid

nitrogen. Once you hear the crackling stop, remove the bundle and re-dip. Dip the

bundle around 2-3 times. Dip the bundle up to the desired break point.

c) Place the now frozen bundle ends between the two plates. With one hand, hold the

bundle end just below where you would like the break point. Use the other hand to

compress the two plates together as much as possible

d) Using the hand holding the tube bundles, apply a strong shearing vertical motion. This

should snap the tubes in such a fashion that minimizes the tubes compressing and

smearing shut.

e) Repeat steps a)-d) for the other bundle end.

39) If you haven’t done step 32, then use a scalpel to cut the bundle ends. Use a sharp, thin

needle to re-open any tubes that might have been smeared closed with the help of a

microscope.

40) Let the epoxy cure overnight.

41) Mix up your tissue mimicking material (here we will assume something agar based of low

wt %).

42) Ensure no bubbles are present, and wait until the temperature is around 50-60 degrees (you

don't want it viscous, liquid is better)

43) Fix the ends of the phantom with luer locks fittings and fill the tubes with water.

44) Fill your chamber with the gel from the bottom up through the opening in one of the walls.

You should use a needle and a 20 mL syringe. Also, it helps to fill along the edges as well.

45) Wait for a few minutes to let the gel cool. While the gel is still liquid, plug the opening in

the wall with a cap. If the cap is a tight fit, make sure you have a hole in the cap for the

displaced gel to flow out from. Cover the cap with silicon. Note: this is the most likely spot

for an air leak to occur that will dry out your gel.

46) You can optionally use silicon to seal any contact areas where you suspect a leak to occur.

47) Let the entire setup sit for 24 hour to let the gel solidify. You are now done!

5.2. Schematic Drawings for Phantom Parts

Figure 5.6 Phantom wall.

Figure 5.7 Phantom end piece.

Figure 5.8 Ring.

Figure 5.9 Phantom ring holder.

Figure 5.10 Schematic of an assembled phantom.