Development of an Anthropomorphic Phantom Coronary Artery Network for CT Imaging

Karolina Stepniak

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Department of Mechanical and Industrial Engineering University of Toronto

Karolina Stepniak

Master of Applied Science

Department of Mechanical and Industrial Engineering University of Toronto

2019 Abstract

In this work, the design of anthropomorphic phantom coronary arteries for optimization of

Computed Tomography Coronary Angiography (CTCA) is presented. First, a phantom vascular wall model for minimization of motion artifacts is manufactured. The mechanical properties of porcine coronary arteries and four soft rubber materials were tested and compared to each other, and the wall attenuation of tubular contrast-filled samples was characterized and compared to literature values as well. A coronary artery model was manufactured by segmenting the main coronary arteries from CT images, 3D printing a mold, and coating it in the rubber material with the most appropriate mechanical and CT attenuation properties. In the second part, the feasibility of 3D printing a phantom directly using four flexible commercially available materials is investigated. The CT attenuation properties match ranges for lipid-rich and fibrous plaque; thus the phantom can be used for optimization of non-calcified plaque imaging.

Acknowledgments

I would like to thank my supervisors, Prof. Hani Naguib and Dr. Narinder Paul for providing me with the opportunity to work on this interesting project and for your commitment in teaching me how to become a better researcher throughout; the completion of this thesis would not have been possible without your expertise. Thank you also to Ali Ursani of the Medical Imaging department at Toronto General Hospital, who always made time in his schedule to help me with my imaging experiments and provided insightful answers to my endless questions. Thank you to my predecessor in medical imaging research, Sherif Ramadan, who did a fantastic job in introducing me to the project and provided me with invaluable resources to ensure that I hit the ground running at the start of my studies. Thank you to my fellow SAPL lab members for helping me use laboratory equipment and for providing me with your thoughtful feedback; I truly appreciate your eagerness and genuine interest in helping me succeed. Thank you to all the bright undergraduate students I had the pleasure of working with, including Capstone group members Sarah, Matthew, Gina, and Jonathan, as well as my summer research students Amanda and Rana for all the hours of hard work you dedicated to running tests and analyzing data.

I would also like to thank Nikola, whose unshaken belief in me is always startling and inspiring. I truly would not have done this without your moral support and our countless pen and paper and whiteboard discussions; thank you for holding my hand through it all. Thank you also to best friend Michelle for the constant reminder that I am a strong independent woman.

Finally, I would like to thank and dedicate this work to my mom, dad, and sista Nicole. Thank you for your endless love and support, no matter the weather; you are awesome.

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Table of Contents

Acknowledgments ...... iii

Table of Contents ...... iv

List of Tables ...... vii

List of Figures ...... viii

Chapter 1 ...... 1

1 Introduction ...... 1

1.1 Preamble ...... 1

1.2 Anatomy of the Coronary Arteries ...... 1

1.3 Coronary Artery Disease ...... 2

1.4 Diagnostic Tools and Procedure ...... 3

1.5 Principles of Computed Tomography ...... 4

1.6 Computed Tomography Coronary Angiography ...... 7

1.7 CTCA Optimization ...... 9

1.8 Imaging Phantoms ...... 11

1.9 Vascular Phantoms ...... 13

1.10 3D Printing Technology ...... 14

1.11 3D Printing for Phantom Manufacturing ...... 15

1.12 3D Printed Vasculature ...... 16

1.13 Mechanical Properties of Coronary Arteries ...... 18

1.13.1 Static Properties ...... 18

1.13.2 Dynamic Properties ...... 19

1.14 Motivation ...... 20

1.15 Thesis Objectives ...... 22

1.16 Thesis Organization ...... 22

References ...... 24

Chapter 2 Development of a Phantom Network for Optimization of Coronary Artery Disease Imaging using Computed Tomography ...... 28

2 Summary ...... 28

2.1 Introduction ...... 29

2.2 Methods ...... 30

2.2.1 Mechanical Behavior of Coronary Arteries ...... 30

2.2.2 Mechanical Behavior of Tissue-Mimicking Materials ...... 34

2.2.3 CT Attenuation Properties of Tissue-Mimicking Materials ...... 35

2.2.4 Phantom Manufacturing and Validation ...... 36

2.3 Results ...... 37

2.3.1 Mechanical Testing of Coronary Arteries ...... 37

2.3.2 Mechanical Testing of Tissue-Mimicking Materials ...... 42

2.3.3 CT Attenuation Properties of Tissue-Mimicking Materials ...... 44

2.3.4 Phantom Manufacturing and Validation ...... 46

2.4 Discussion ...... 48

2.5 Conclusion ...... 51

References ...... 53

Chapter 3 Novel 3D Printing Technology for CT Phantom Coronary Arteries with High Geometrical Accuracy ...... 56

3 Summary ...... 56

3.1 Introduction ...... 56

3.2 Materials and Methods ...... 58

3.2.1 Mechanical Properties of 3D Printing Materials ...... 59

3.2.2 CT Attenuation Properties ...... 61 v

3.2.3 Manufacturability ...... 62

3.3 Results ...... 64

3.3.1 Mechanical Properties of 3D Printing Materials ...... 65

3.3.2 CT Attenuation Properties ...... 67

3.3.3 Manufacturability ...... 69

3.4 Discussion ...... 70

3.5 Conclusion ...... 72

References ...... 73

Chapter 4 Conclusions and Recommendations ...... 76

List of Tables

Table 1.1. List of variables used in Equations (1) to (4) and their significance ...... 5

Table 1.2 Factors and CT parameters affecting image quality in CTCA ...... 10

Table 2.1 CT scan settings used for imaging candidate materials ...... 35

Table 2.2 Data for outer diameter and wall thickness measurements of tested samples, as well as their diameter to thickness ratios ...... 37

Table 2.3 Values of average Young’s moduli and transition stresses and strains for each arterial branch ...... 40

Table 2.4 Average storage and loss moduli of each coronary artery location at a frequency of 1.25 Hz...... 41

Table 2.5 Average physiological Young’s moduli of tissue-mimicking materials, as well as their percent difference relative to the average physiological Young’s modulus of the coronary arteries listed in Table 2.3...... 42

Table 2.6 Average storage and loss moduli at 1.25 Hz for the tissue-mimicking materials and percent difference relative to the tested coronary arteries...... 44

Table 2.7 CT Number Measurements from axial sections of each cast sample ...... 45

Table 3.1 Materials and printers used to manufacture each sample ...... 59

Table 3.2 Young’s moduli of flexible 3D printing materials and their percent difference relative to the Young’s modulus of coronary arteries ...... 66

Table 3.3 Dynamic moduli of flexible 3D printing materials at 1.25 Hz (75 bpm) ...... 67

Table 3.4 CT Numbers of tubular samples with various degrees of stenosis measured along average diameter ...... 68

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List of Figures

Figure 1.1 The anatomy of the coronary arteries; the main branches are highlighted in yellow (courtesy of Shahoud and Tivakaran, 2019) ...... 2

Figure 1.2 Left: Aerial X-ray image acquired during CCA; the white arrows indicate regions of stenosis (courtesy of Zalewska-Adamiec et al 2013). Right: Cross sectional image of arteries acquired during IVUS with various plaque types highlighted (Reprinted from Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging , 1 st Ed., Lambros et al, Chapter 1: “Principles of Coronary Imaging Techniques”, pp 23-27, Copyright © 2017 with permission from Elsevier ) ...... 4

Figure 1.3. Various tissue types (top) and their corresponding CT Numbers (bottom) ...... 6

Figure 1.4 Example of no motion artifact (left), mild motion artifact (middle), and severe motion artifact (right) of the RCA lumen (circled in red). Blurring occurs when image acquisition cannot be timed properly due to irregularities in heart rhythm (courtesy of Otton et al 2013) ...... 7

Figure 1.5. Comparison of non-calcified (A,B), mixed (C,D,E), and calcified (F,G) plaque. B,D,E, and G are cross-sectional views of lines drawn through vessels shown in A, C, and F. (Courtesy of Brolin et al 2014) ...... 8

Figure 1.6. Gammex 467 phantom (Reprinted from Zeitschrift fuer Medizinische Physik , Vol. 25, Issue 4, Schwahofer et al, “The application of metal artifact reduction (MAR) in CT scans for radiation oncology by monoenergetic extrapolation with a DECT scanner”, pp 314-325, Copyright © 2015, with permission from Elsevier) ...... 12

Figure 1.7 QRM Anthropomorphic thorax phantom ...... 13

Figure 1.8 Example of a static plaque phantom (Reprinted from Investigative Radiology, Vol. 41, Issue 6, Suzuki et al , “Accuracy of attenuation measurement of vascular wall in vitro on computed tomography angiography: Effect of wall thickness, density of contrast medium, and measurement point”, pp 510-515, Copyright © 2006, with permission from Wolters Kluwer ) ... 14

Figure 1.9. Three techniques used to manufacture phantoms using 3D printing ...... 16

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Figure 1.10. 3D printed vascular phantoms with plaque simulating various levels of stenosis (Reprinted with permission from “Stenosis quantification of coronary arteries in coronary vessel phantoms with second-generation dual-source CT: influence of measurement parameters and limitations”, Toepker et al , American Journal of Roentgenology Vol. 201, Issue 2, Copyright © 2013, American Roentgen Ray Society) ...... 17

Figure 1.11 Example of a stress-strain curve of coronary arteries; this viscoelastic behavior is typical for soft tissue (Reprinted from European Journal of Vascular and Endovascular Surgery , Vol 39, Issue 6, Duprey et al , “In Vitro Characterisation of Physiological and Maximum Elastic Modulus of Ascending Thoracic Aortic Aneurysms Using Uniaxial Tensile Testing”, pp 700- 707, Copyright © 2013, with permission from Elsevier ) ...... 19

Figure 1.12. Schematic of existing dynamic anthropomorphic heart phantom ...... 21

Figure 2.1 a) Porcine heart with an outline of the cutting path used to extract the LAD b) an example of an arterial segment after dissection ...... 31

Figure 2.2 a) Arterial segments are prepared for tensile testing by securing both ends to sandpaper using cyanoacrylate glue before b) tensile testing of arterial segments until failure ... 32

Figure 2.3 Procedure used to prepare samples for DMA, in which a) the artery is first cut longitudinally, and the strip is then b) folded onto itself to create c) a square disk geometry ..... 33

Figure 2.4 Tensile (left) and DMA (right) samples prepared using tissue-mimicking materials . 34

Figure 2.5 Example of a typical stress-strain curve of a coronary artery ...... 38

Figure 2.6 Average stress-strain curve for the CX arteries demonstrating the process of finding the transition point (indicated by the red circle) ...... 39

Figure 2.7 Average Young’s moduli and transition stresses and strains for each arterial branch 40

Figure 2.8 Average a) storage and b) loss moduli of CX (square markers), LAD (triangle markers), and RCA (circle markers). Each point represents an average of five tested samples... 41

Figure 2.9 Average physiological Young’s moduli of tissue-mimicking materials ...... 42

Figure 2.10 Average a) storage and b) loss moduli of PU rubber (square markers), Sn-Si rubber (circle markers), latex (diamond markers), and Pt-Si rubber (triangle markers) and the average storage and loss moduli of the tested coronary arteries (solid lines)...... 43

Figure 2.11 Ranges for CT Numbers of tissue seen in CTCA images, as well as the average CT numbers of samples that fit within the ranges...... 45

Figure 2.12 a) 3D model of the coronary artery positive mould after segmentation of CT files in 3DSlicer and the addition of handles in Meshmixer b) The coronary artery positive moulds, printed using a polypropylene-like resin on the Form2 SLA printer c) The phantom coronary arteries after casting the Pt-Si rubber onto the positive moulds...... 46

Figure 2.13 a) The phantom coronary arteries integrated with the DAHP and anthropomorphic thorax phantom for CT scanning b) arteries secured to the existing phantom heart, using latex rubber as both an adhesive and epicardial adipose tissue simulator c) 3D rendering of scanned phantom d) Axial slice from a CT scan of the phantom, with coronary arteries indicated in yellow bubble e) A close-up of the coronary arteries with CT numbers indicated...... 47

Figure 3.1 Schematics of the printing systems used in the present study a) FDM printer b) SLA printer c) Polyjet printer ...... 59

Figure 3.2 Samples 3D printed for tensile and DMA tests ...... 60

Figure 3.3 Dimensions of tubular samples prepared for CT scanning; each sample was 3D printed using the materials and systems listed in Table 3.1 ...... 61

Figure 3.4 a) Axial CT slice showing the cross section of a tubular sample (PJ 2 with 75% stenosis) with indicated outer, average, and inner diameters; a line is drawn from the center of the contrast-filled lumen to the average diameter to measure the CT number of the wall b) An intensity plot showing the variation in CT number along the length of the drawn line; the measured value for this sample is 28 HU...... 62

Figure 3.5 a) Images from a patient CTCA scan showing the axial (left), sagittal (middle), and coronal (right) views of the heart. The green regions represent tissue with an attenuation of 200 HU or greater after the thresholding function was applied. The arterial walls are not visible;

x however, the contrast enhanced lumina can be seen and are labeled for each of the three major arteries b) A solid computer model of the arterial lumina obtained after segmentation in 3D Slicer software c) A hollow computer model of the coronary arteries after modifications are made in Meshmixer Software ...... 63

Figure 3.6 Young’s Moduli of the tested materials ...... 65

Figure 3.7 a) Storage moduli and b) loss moduli of the tested 3D printing materials ...... 67

Figure 3.8 CT Numbers of 3D printed tubular samples with 50% (light grey), 75% (medium grey), and 90% (dark grey) stenosis ...... 68

Figure 3.9 Hollow 3D printed models of the LAD, CX (left), and RCA (right) ...... 70

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Chapter 1 1 Introduction 1.1 Preamble

Coronary Artery Disease is a leading cause of death worldwide [1]. Computed Tomography Coronary Angiography (CTCA) can be used to diagnose and monitor its development by observing the size and composition of plaque deposits in the coronary arteries; however, blurring due to improperly timed image acquisition as well as hardware and software limitations compromise image quality and may lead to improper diagnosis. Phantoms mimic the CT attenuation properties of tissues and organs and can be used as a tool in studying the effects of various parameters on image quality. However, existing vascular phantoms fall short of combining attributes including accurate replication of arterial geometry and motion. In the following section, background knowledge and a literature survey necessary for the design and manufacturing of a phantom coronary artery network which can achieve both of these shortcomings will be presented. It is hoped that these phantoms will enable the design of imaging protocols which minimize motion artifacts and optimize plaque imaging.

1.2 Anatomy of the Coronary Arteries

The coronary arteries encircle the heart and are responsible for supplying it with oxygen and nutrient rich blood. The left main coronary artery (LMCA) and the right coronary artery (RCA) stem from the aorta [2]. The LMCA branches a short distance away from its proximal end into the left anterior descending artery (LAD) and the circumflex artery (CX). Figure 1.1 illustrates the anatomy of the coronary arteries [3]. Together, the LAD, CX, and RCA make up the main arterial branches, and are highlighted in yellow below.

Figure 1.1 The anatomy of the coronary arteries; the main branches are highlighted in yellow (courtesy of Shahoud and Tivakaran, 2019)

1.3 Coronary Artery Disease

Coronary Artery Disease (CAD) is a medical condition that affects the coronary arteries, and involves the accumulation of plaque on their inner walls. Plaque is more likely to form along the proximal and middle segments of the main coronary arteries (the RCA, LAD, and CX) and around bifurcations where they branch into smaller arteries [4]. The following are steps in Coronary Artery Disease development and progression [5][6][7]:

1. Endothelial cell damage and dysfunction allows lipids from bloodstream to enter and bind to proteins in the arterial wall; inflammatory response to repair damaged endothelial cells initiates and white blood cells are recruited and endothelial cell growth is stimulated

2. Lipid accumulation causes diffuse thickening in arterial walls (xanthomas); lipid molecules undergo chemical processes which damage endothelial cells and white blood cells, further stimulating inflammatory response

3. A mass with accumulating lipid at its core surrounded by white blood cells, endothelial cells, and dead cells causes outward bulging, known as positive remodeling, of arterial wall (necrotic core)

4. Advanced inflammatory response stimulates smooth muscle cells to migrate from media to intima and grow over necrotic core to form fibrous cap (fibroatheroma); extracellular matrix forms and calcification may begin

5. Fibrous cap thins due to digestive processes in the formed extracellular matrix (thin- capped fibroatheroma)

The likeliness of a plaque to cause complications depends on the size of the lipid core, thickness of the fibrous cap, and extent of calcification. One of these complications includes plaque breaking off of the arterial wall and occluding the lumen of the coronary artery, a process known as rupture, resulting in myocardial infarction (heart attack). Plaques with a large lipid core, thin fibrous cap, and minimal calcifications are more likely to rupture and occlude the lumen since the lipid-rich plaque is soft and the thin cap no longer provides the necrotic core with adhesion to the inner arterial wall.

1.4 Diagnostic Tools and Procedure

Common methods of diagnosis include Catheter Coronary Angiography (CCA) and Intravascular Ultrasound (IVUS), both of which are shown in Figure 1.2 [8][9]. In CCA, a catheter is passed through the coronary arteries, and a contrast dye is injected through it into the patient’s bloodstream. X-ray images of the chest are then taken to obtain an aerial view of the arterial network and regions where reduced intensity of the contrast dye is observable indicate occlusions [10]. CCA is limited in that it does not provide information regarding plaque composition, and only provides visualization of stenosis in one plane. In IVUS, a catheter with an ultrasound transducer at its end is passed through the coronary arteries and the variations in the speed of reflected sound waves is used to generate a cross sectional view of the arterial lumen and walls [11]; in addition to stenosis, the different grayscale values can be used to identify the composition of the wall and features of plaque which represent various stages of Coronary Artery Disease, as described in Section 1.3. However, the costly, time consuming, invasive, uncomfortable, and potentially dangerous nature of IVUS as well as CCA are a major

4 disadvantage [12][13][14]. CT imaging of the heart and its vasculature, known as Computed Tomography Coronary Angiography (CTCA) is becoming an increasingly popular method of diagnosis due to its relative cost and time efficiency, as well as non-invasive nature [15].

Figure 1.2 Left: Aerial X-ray image acquired during CCA; the white arrows indicate regions of stenosis (courtesy of Zalewska-Adamiec et al 2013). Right: Cross sectional image of arteries acquired during IVUS with various plaque types highlighted (Reprinted from Atherosclerotic Plaque Characterization Methods Based on Coronary Imaging , 1st Ed., Lambros et al, Chapter 1: “Principles of Coronary Imaging Techniques”, pp 23-27, Copyright © 2017 with permission from Elsevier )

1.5 Principles of Computed Tomography

In CT, photon beams of a known intensity are emitted, and undergo scattering and absorption as they pass through tissue [16]. The final intensity of the photon beams is then measured after transmission. The degree to which the photon beam has been attenuated is quantified by its linear attenuation coefficient, μ, and can be determined using Equation (1) below, where x is the distance which the photon beam travels, and Io and If are the initial and final beam intensities. The degree of scattering and absorption which determine Io is related to a material’s intrinsic properties, including its physical and electron density and molecular weight. Equation (2), (3), and (4) describe the dependence of the linear attenuation coefficient on these material properties [17]. The linear attenuation coefficient is then used to calculate a CT number

5 using Equation (5). The CT number is measured in Hounsfield Units, and each CT number is associated with a certain grayscale value, which is essentially representative of a material’s density. For example, the CT number of air (density 0.0012 g/cm3) is -1000 HU and appears black in images, whereas the CT number of bone (density 1.8 g/cm3) is reported to be around1000 HU, and appears white. Figure 1.3 lists various tissue types, their CT numbers, and the grayscale value assigned to them.

(1)

(2)

(3)

(4)

Table 1.1. List of variables used in Equations (1) to (4) and their significance

linear attenuation coefficient

electron density number of electrons per gram of material physical density total molecular weight of material amount of some element in material atomic number of corresponding element Avogadro’s constant effective atomic number for photoelectric absorption effective atomic number for Rayleigh scattering photon energy a, b, c, k, l, m, n constants

(5)

Figure 1.3. Various tissue types (top) and their corresponding CT Numbers (bottom)

CT image quality is key in providing patients with accurate diagnoses and depends on many factors related to both hardware and software. In the present study, a 320-row, 0.5mmx0.5mm multi-detector CT (MDCT; also often referred to as multi-slice or multi-row CT) scanner (AquilionOne, Toshiba Medical Systems) was used. Its detectors are squares with side lengths of 0.5 mm which line the inner surface of the gantry forming 320 rows. This implies that a CT scan covering a large distance of up to 16 cm in the z-direction (such as the length of the heart) can be completed quickly. This is ideal in CTCA since it minimizes motion artifacts. The chosen tube voltage and current determine the initial energy and emission frequency of photon beams. In addition, there are image reconstruction settings which can be manipulated in order to optimize image quality. Slice thickness refers to the volume of the tissue captured in each image. Reconstruction slice increment is the amount of overlap in scanned volume for each slice. Collimation width refers to the width to which an emitted photon beam has been narrowed. The reconstruction kernel is a filter that is applied to the image which enhances visibility of the tissue type that it is designed for (e.g. soft tissue, bone, or lung). Spatial resolution is a CT scanner’s capability in detecting two objects which are adjacent to one another, and is dependent on the spacing between detectors on the machine. Contrast resolution is a measure of how well tissues with similar attenuation properties, and thus grey values, can be distinguished, and is dependent on the tissue of interest, its surroundings, and whether or not contrast agent is used.

1.6 Computed Tomography Coronary Angiography

CT imaging of the heart is referred to as Computed Tomography Coronary Angiography (CTCA). The most prevalent way in which CTCA is used clinically is to confirm or exclude Coronary Artery Disease in patients experiencing chest pain [18]. This is done by examining images to identify plaque deposits causing significant luminal stenosis, which is typically greater than 50% in these patients.

CTCA is more complex than standard CT, since the heart is a dynamic organ. This requires x-ray images to be acquired in such a way as to minimize blurring, which can be achieved during the diastolic phase of the cardiac cycle when the heart is in its most relaxed state [19]. Prior to CT imaging, a patient’s ECG signal is mapped to identify diastole, and adjustments are made to CT protocol to time image acquisition appropriately. Ideally, the patient will have a steady, regular heart rhythm (consistent 60-75 bpm frequency); however, many patients with cardiovascular diseases experience irregular heart rhythms, including bradycardia (heart rate lower than average; less than 60 bpm), tachycardia (heart rate higher than average, greater than 75 bpm), and fibrillation (irregular frequency), making it difficult to accurately predict diastole. Beta blockers are drugs which can be administered to patients beforehand and which may slow their heartbeat, although this too may be ineffective. The size and location of plaque deposits is greatly affected by motion artifacts. Figure 1.4 shows an example of a motion artifact in which the lumen of the RCA is obscured due to an irregular heart rate and improper image acquisition protocol [20].

Section 1.3 outlines the stages of Coronary Artery Disease and concludes that plaque with a large lipid content, a thin fibrous cap, and minimal calcification is most likely to cause acute myocardial events. In CTCA, limitations in spatial and contrast resolution prevent the visibility of these small features; instead, plaque is broadly classified as calcified, mixed, or non- calcified [21][22]. Non-calcified plaque is perceived as being most vulnerable; however, as presented in Section 1.3, their risk varies depending on the size of the lipid core and the degree of fibrosis. Thus, non-calcified plaque is further sub-classified as predominantly fibrous or lipid- rich. Since it is softer and more likely to rupture, lipid-rich plaque is viewed as a greater predictor of emergency events. For this reason, there is interest in characterizing non-calcified plaque as predominantly lipid-rich or fibrous using their measured CT Number. The CT Number of lipid-rich plaque has been reported to range from -30 to 60 HU and the CT numbers of fibrous plaque range from 60 to 150 HU [12][23][24][25] Since the vascular wall, noncalcified plaque, and smaller plaque deposits of various composition may blend in with surrounding tissue, contrast agent is used to enhance the visibility of the coronary arteries, and typically has at CT number between 200 and 500 HU. Figure 1.5. shows a comparison between the three plaque types from two different angles [26].

1.7 CTCA Optimization

Based on the principles and challenges of CTCA listed in Section 1.6, one goal in improving CTCA is to find a combination of parameters which will produce optimal image quality in various situations. In this context, image quality is defined as how well plaque size can be determined by measuring the % stenosis in terms of diameter or area reduction. Image quality is affected by image noise, luminal contrast-enhancement level, and arterial motion as follows:

• Image noise refers to how visible features are in CT images, and is dependent on scan parameters including tube voltage and current, as well as image reconstruction parameters including filter selection and reconstructed slice thickness; the selection of these parameters is influenced by patient body mass (a higher body mass attenuates more x-ray beams and requires a larger amount of x-rays with greater intensity) as well as by the luminal contrast enhancement level.

• The luminal contrast-enhancement level is dependent on the concentration of the contrast agent administered to the patient, and is influenced by parameters associated with image noise. It is also related to the geometry of the arteries; at some contrast levels, the attenuation of the lumen will obscure smaller plaque deposits [27][28].

• Arterial motion refers to the patient’s heart rate and rhythm, which determines how and how well a patient’s ECG can be synced with CT image acquisition timing, as described in Section 1.6. This is controlled by gantry rotation time and gating protocol.

The above concepts are summarized in Table 1.2. In general, image quality increases with tube voltage, tube current, as well as CT image acquisition time and overlap. However, these parameters cannot be increased indefinitely due to the increasing radiation dose administered to the patient. Therefore, in addition to improving image quality, another goal in improving CTCA is to minimize radiation exposure by finding a middle ground for these factors.

The third goal in CTCA research is to improve techniques for determining plaque composition by measuring its CT number. One of these techniques includes development of software which can automatically segment various plaque types in a CT image. In order to do so, a range of CT numbers must be defined for each plaque type. However, CT numbers of plaque vary depending

10 on the CTCA parameters listed above as well as with arterial geometry, and it is necessary to first determine how CT number measurements will change based on both of these factors. Some examples of these relationships are listed below:

• The CT number decreases with plaque size/arterial wall thickness

• The CT number increases when measured closer to the contrast-enhanced lumen [27]

• The CT number will increase with luminal contrast level [27][28]

o In turn, luminal contrast level decreases with diameter [28][29]

• The CT number will increase with decreasing tube voltage

o Luminal contrast level also increases with decreased tube voltage

• The CT number standard deviation will decrease with reconstructed slice thickness.

Table 1.2 Factors and CT parameters affecting image quality in CTCA

Factor How It Affects How Related CT Parameters Considerations Affecting Image Quality Problems Can in Selecting Image Be Addressed CT Quality Parameters

Determine which phase of Gating Protocol Evaluation of the cardiac Motion Radiation Dose plaque size cycle should be used for Gantry Rotation Speed reconstruction

Increase kVp, mA Evaluation of radiation dose

plaque size Patient Body Rotation Pitch Mass

Noise Increase

Detection of resolution with small plaque which images Radiation Dose are acquired Slice deposits Thickness/Reconstruction Slice Thickness

Increase Image reconstruction sharpness filter

Evaluation of Contrast agent Patient Body plaque size concentration Mass

Increase or Contrast- Detection of Timing of contrast agent decrease Arterial Enhancement plaque deposits injection and image contrast level Geometry acquisition

CT Number Radiation Dose Measurements

1.8 Imaging Phantoms

Imaging phantoms serve as substitutes for biological tissue by mimicking their CT attenuation properties. These materials have similar atomic and physical properties to the tissue which they aim to mimic, including elemental composition, molecular weight, as well as physical and electron density, as described in Section 2.1. Phantoms are used to select a CT protocol which will optimize image quality while minimizing patient radiation exposure, and can be used to improve the plaque characterization, as listed in 1.7.

Phantoms are commercially available. Geometric phantoms are blocks, cylinders, or slabs of tissue-mimicking materials which do not resemble the tissue or organ anatomically. The Gammex 467 phantom shown in Figure 1.6 is a commercially available example of a geometric phantom, where each insert has the same electron density as the tissue it represents [30]. Anthropomorphic phantoms replicate not only the tissue’s atomic and physical properties, but also their shape. The QRM phantom (QRM GmbH, Moehrendorf, Germany) shown in Figure 1.7 is a commercially available example of an anthropomorphic thorax, where the outer shell represents the chest walls, the inner chambers represent lungs, the cylindrical insert positioned at the bottom represents the spinal cord, and the cavity in the middle represents the area where the heart is found. Since the heart is a dynamic organ and its motion will affect image quality,

12 dynamic anthropomorphic heart phantoms, such as the CIRS model 008C phantom (CIRS Inc., Norfolk, VA, USA), have been manufactured as well. These phantoms consist of an anthropomorphic heart model which is attached to a mechanical and electrical apparatus which generate the heart motion. Due to the lack of customization options, many researchers have developed their own.

Figure 1.7 QRM Anthropomorphic thorax phantom

1.9 Vascular Phantoms

Vascular phantoms have been created for motion analysis. These phantom vessels are usually incorporated with dynamic anthropomorphic phantoms, and are made using flexible tubing consisting of materials such as polyurethane [32], silicone [33] and latex [34] in order to enable them to achieve a desired range of motion. Their designs vary from single, straight sections to interconnected networks. Phantom plaque inserts can be placed inside the vasculature to study the effect of motion over a range of heart rates on the accuracy of registering plaque size and location in CT images.

Plaque phantoms have been manufactured to study how CT number measurements change with various factors as well. Figure 1.8 shows a vascular phantom created by Suzuki et al [27]. The phantom consists of tubes made from a low-density plastic (ethylene vinyl alcohol) with identical inner diameters and wall thicknesses ranging between 0.5, 1, and 1.5 mm. The walls represent concentric arterial plaque while the inner diameter represents the lumen. The phantom was used to study the effects of wall thickness, luminal contrast level, and measurement point within the wall on CT number measurements.

1.10 3D Printing Technology

3D printing is a quick and relatively inexpensive manufacturing method which offers a high degree of geometric accuracy and is capable of producing complex geometries. Once 3D models of the parts to be printed are prepared (usually in CAD software), they must be saved in Standard Triangle Language (STL) file format. This converts the parts into hollow objects, the surfaces of which are made up of triangles; this is called a 3D triangular mesh. The meshes are loaded into slicing software, which divides it into layers to generate printing instructions for 3D printers. Once sliced, the models are ready to be printed. Common types of 3D printing technologies used include Fused Deposition Modeling (FDM), Stereolithography (SLA), and Polyjet [35]. In FDM printing, nozzles melt and dispense a thermoplastic filament onto a build platform below, building the part from the bottom up. In SLA printing, liquid photo-curable thermosetting resin in a vat is cured in layers by UV light. The model can be built either from the top down if the platform is located above the light source or from the bottom up if the build platform is located below the light source. In the Polyjet printer, liquid photopolymer is deposited onto a build platform layer by layer and cured by a laser source. The build platform moves down as the object is printed from the bottom up.

Once printed, the model requires post-processing before it is ready to use. Support material must be removed either manually or dissolved in a solvent bath. For SLA prints, there is also excess liquid resin on the surface of the printed part, which requires that it is soaked in a bath of isopropyl alcohol in order to remove it.

1.11 3D Printing for Phantom Manufacturing

The application of 3D printing materials as tissue substitutes has been investigated in literature. Polymer 3D printing materials generally have densities of approximately 1 g/cm3, which makes them suitable for simulation of biological tissue. Geometric phantoms have been 3D printed. For example, Dancewicz et al. have 3D printed cylindrical inserts using various materials and compared their CT number measurements to those of Gammex 467 inserts [36]. Of particular interest are the CT attenuation properties of flexible commercially available materials. Thermoplastic polyurethane (TPU) was found to have a CT number between -140 and 10 HU [37][38], while CT number values for an SLA resin range between 46 and 98 HU [36][39]. One study reported the CT number of a flexible polyjet material to be 83 HU [40].

It is also possible to manufacture anthropomorphic phantoms as well [41]. The first step is to obtain physiologically accurate geometry. This is accomplished through medical image segmentation, where patient CT, MRI, or ultrasound images are used to isolate the tissue of interest. The highlighting of the desired tissue in each image results in a 3D virtual model of the tissue, which must be saved in STL format. The STL file can then be modified in specialized 3D triangular mesh editing software such as Rhinoceros 3D, Meshlab, Blender, and Meshmixer (Autodesk Inc.). Often, the model is used to design a mold. The molds are 3D printed and materials which mimic the mechanical properties of the desired organ or tissue are cast to make the final model. As an alternative to casting, 3D printed molds can be used as positives and be dip or brush coated to produce the final model. The commercial availability of flexible 3D printing materials has made it possible to manufacture compliant phantoms directly as well. These three phantom manufacturing pathways are summarized in Figure 1.9.

Figure 1.9. Three techniques used to manufacture phantoms using 3D printing

Arcaute and Wicker demonstrated the application of this process in creating a vascular phantom [42]. The authors showed that a patient’s CT, MRI, or ultrasound files can be used to extract the vascular geometry to create a solid model of the vessel lumen. In order to improve image segmentation, a thresholding function built into the image processing software was used to filter out tissue above a certain CT number. The vessels were 3D printed on an FDM system using water-soluble support material. Subsequently, the printed vessels were dip spin coated in silicone and dissolved in water once the material cured, resulting in flexible and geometrically accurate vessel replicas.

1.12 3D Printed Vasculature

3D printing has been used to manufacture vascular models directly. For example, Kimura et al. 3D printed a compliant hollow cerebral aneurysm model for surgical simulation using the flexible polyjet material, and accurate geometry was obtained by segmenting patient CT images, as described in Section 3.3 [43]. Biglino et al. used the same material and printed an aortic model

17 to demonstrate its compliance and suitability in flow simulations [44]. Toepker et al. used polyjet technology to 3D print a vessel phantom [45]. The vascular wall was simulated using a rigid material and eccentric plaque inserts representing various percentages of stenosis were printed using a soft material. The phantom was used to investigate how CT number and stenosis measurements change with vessel diameter, degree of stenosis, luminal contrast attenuation, and plaque geometry. Similarly, Richards et al. also used a rigid material on a polyjet system to print a tubular vascular phantom [46]; however, instead of using the model to represent the vascular wall, the authors used it to simulate plaque. This plaque phantom was then secured to a dynamic heart phantom and used to determine the accuracy of measured stenosis under dynamic conditions. Since the model was 3D printed, its dimensions were well defined and provided an accurate means of assessing measurement error.

1.13 Mechanical Properties of Coronary Arteries

In order to achieve a physiologically relevant range of motion, the mechanical properties of materials which can be potentially used as phantom materials should be similar to the mechanical properties of the coronary arteries. The mechanical properties of the coronary arteries have been reported in literature; however, there is a large variation in the testing methods used and the reported values.

1.13.1 Static Properties

The static mechanical properties of the coronary arteries have been characterized using a number of means, including via in-vivo measurement using intravascular ultrasound (IVUS) [47], and ex-vivo measurement using tensile [48][49] and pressure-inflation [50][51] tests. In IVUS, the Young’s modulus of the arterial walls can be measured based on the speed of the reflected sound waves; this method is usually used when studying live subjects. In pressure- inflation tests, the coronary arteries are dissected from the heart and secured to a pump, which uses saline solution to pressurize the arteries to physiological pressures. Pressure and outer diameter measurements are used to produce a stress-strain curve. In tensile testing, the dissected arteries are loaded into a tensile tester, which pulls the specimens apart until failure. Load- displacement data is used to produce a stress-strain curve.

The Young’s modulus is a measure of stiffness and has been used to characterize the mechanical behavior of the coronary arteries. Due to the viscoelastic nature of the coronary arteries, the stress-strain curve typically has three regions, namely an initial linear region, an elbow-shaped non-linear region, and a final linear region culminating in failure. Figure 1.11 below shows a typical stress strain curve with this behavior [52]. The non-constant slope of the curve implies that the Young’s modulus varies depending on the level of stress and strain. The Young’s modulus has been reported as the slope of the final linear region [48], and is indicated in the figure below as the “maximum” elastic modulus. It is known, however, that the elbow- shaped region corresponds to a physiological range of stress experienced by the arteries in-vivo [53]. Hence, some authors report a “physiological” Young’s modulus [52][49][54]; as shown in the figure below, it is the slope of a line tangent or secant to a point at the middle of the elbow- region, often referred to as the “transition point”.

1.13.2 Dynamic Properties

Due to their viscoelastic nature and the cyclic loading they experience in-vivo, the dynamic properties of the coronary arteries have been investigated as well. The dynamic modulus provides a measure of how a material responds to loading with time, and is a vector sum of its components, namely the storage and loss moduli. The storage modulus describes the elastic response of a material, whereas the loss modulus describes its viscous response. In order to measure the storage and loss moduli, pressure-inflation tests have been used apply pressures cyclically between normotensive blood pressures of 80 and 120 mmHg. Burton et al recently characterized the storage and loss moduli of porcine LAD arteries by using a dynamic mechanical analyzer (DMA) [55]. Unlike a tensile tester, which applies a longitudinal load to a specimen at a constant rate, the DMA can be used to apply a cyclic force and provides a variety of clamping options which provide different forms of loading. Arterial segments tested by Burton et al were loaded in a tensile clamp, and an oscillatory load was applied for frequencies ranging between 0.5 and 10 Hz. Ramadan et al. used the DMA to test the dynamic properties of heart tissue at physiologically relevant frequencies between 0.5 and 3.5 Hz [56], representing

20 heart rates of 30 to 210 beats per minute (bpm). In order to mimic physiological conditions, a compression clamp was used in a chamber filled with saline solution heated to 37°C.

1.14 Motivation

As stated in section 1.7, image quality and the ability to identify plaque according to its CT number are dependent on physiological factors, including arterial geometry and motion. Hence, the phantoms described in sections 1.8 and 1.9 do not account for imaging errors which result from both of these factors, and do not simulate a patient’s realistic radiation exposure. Patient-specific phantoms mimic a tissue’s physiological geometry.

Image segmentation and 3D printing are proven means of achieving physiologically accurate geometry, and have been used to manufacture various tissues and organs, including vascular networks via a casting process. 3D printing materials have also been used to simulate tissue in medical imaging, and recently developed commercial soft 3D printing materials have made it possible to create models for dynamic applications. In addition, 3D triangle mesh manipulation allows models to be customized as desired. In previous work, a dynamic anthropomorphic heart phantom, the schematic of which is shown in Figure 1.12 below, was designed and built in-house, in which an electrical and mechanical apparatus simulate the contraction, relaxation, and twisting motion of a heart model with physiologically accurate geometry and mechanical properties, as well as the appropriate CT attenuation when scanned using a dual-energy 320-row multidetector CT (MDCT). Plaque inserts mimicking various plaque compositions have been manufactured as well. In order to study the effects of motion and arterial geometry on plaque imaging using various CT protocols, a phantom coronary artery network with physiologically relevant geometry as well as mechanical and CT attenuation properties using a simple and customizable manufacturing process is required.

Figure 1.12. Schematic of existing dynamic anthropomorphic heart phantom

These patient-specific phantoms will enable cataloguing of CT protocols most suitable for imaging of various patient types. The following are three examples of how these phantoms can be used; each example reflects a factor which affects image quality and radiation dose, both of which must be balanced:

• The dynamic cardiac phantom can be programmed to beat at an irregularly high or low heart rate and used to experiment with CT image acquisition timing to minimize blurring in plaque imaging

• The phantom heart with the arteries can be placed within an anthropomorphic thorax phantom simulating patients with irregularly high or low body mass and experiment with tube voltage and current required to produce a clear image while minimizing radiation dose

• The phantom arteries can be filled with contrast agent diluted to produce various levels of attenuation and CT numbers of the wall/plaque can be recorded for various arterial diameters and wall thicknesses for reference when trying to identify plaque type while analyzing patient images and designing new image reconstruction software

1.15 Thesis Objectives

The overall objective of this research is to fabricate phantom coronary arteries which will serve as a tool for minimizing motion artifacts in CTCA and optimizing plaque imaging. The phantom coronary arteries should have physiologically accurate CT attenuation, geometric, and mechanical properties. Two approaches to simulating the arterial network will be explored. In the first approach, the phantom will be created to mimic the vascular wall. In this context, the short term objectives are to determine the physiologically relevant mechanical properties of the coronary arteries and of various tissue-mimicking materials which can potentially be used to manufacture the vascular wall phantom, determine the CT number of each material, and manufacture a geometrically accurate phantom via casting. In the second approach, 3D printing will be used to directly manufacture the phantom to mimic both arterial and plaque characteristics. Here, the short term objectives are to determine the physiologically relevant mechanical and CT attenuation properties of commercially available flexible 3D printing materials, and to assess manufacturability by designing and 3D printing a hollow arterial network using each material. In both studies, the primary objective will be to match the desired CT attenuation and obtain physiologically accurate geometry since the intended purpose of the final product is to serve as an anthropomorphic phantom. Matching the mechanical properties to those of the coronary arteries, although highly desirable, will be a secondary objective.

1.16 Thesis Organization Chapter 2 describes the process of designing and manufacturing a vascular wall phantom, as described in the first approach. The static and dynamic mechanical properties of porcine coronary arteries are investigated first. The mechanical properties of several soft rubber materials known to have been used in soft tissue imaging applications are then determined and compared with those of the coronary arteries. Subsequently, samples of each material are CT scanned under clinically relevant conditions and compared to literature values for muscular tissue. The process of manufacturing the phantom with physiologically accurate geometry is also presented, and involves segmentation of arterial lumina in patient CT images and using the resulting geometry to 3D print a positive mold. The material with the most appropriate mechanical and CT attenuation properties is used to manufacture the phantom arteries via a dip and brush coating technique. Chapter 3 describes the process of designing and manufacturing a vascular plaque phantom directly using 3D printing. The CT attenuation and mechanical properties of various

23 soft 3D printing materials are first investigated in order to assess their suitability as non-calcified plaque analogs and for dynamic applications. Using a 3D triangular mesh-editing software, the solid lumen models prepared in Chapter 2 are modified to create hollow models, which are then printed to demonstrate manufacturability. Finally, conclusions of this research as well as recommendations of future work are provided in Chapter 4.

References

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Chapter 2 Development of a Phantom Network for Optimization of Coronary Artery Disease Imaging using Computed Tomography

2 Summary

Computed Tomography provides a practical means of diagnosing Coronary Artery Disease in patients, but requires improvements in image quality through optimization of scan parameters and development of new image reconstruction software. In this work, the design of an anthropomorphic phantom coronary artery network with appropriate CT attenuation and mechanical properties is described. The static and dynamic mechanical properties of fresh porcine coronary arteries are characterized at physiological loading conditions through uniaxial tensile testing and dynamic mechanical analysis. The Young’s modulus was determined to be 0.16±0.04 MPa, and the storage and loss moduli at 1.25 Hz were 0.02±0.01 MPa and 0.004±0.002 MPa. The static and dynamic mechanical properties and CT attenuations of a platinum-cured silicone rubber, a tin-cured silicone rubber, a polyurethane rubber, and latex rubber previously used in soft tissue phantom applications were investigated and compared to those of coronary arteries. The platinum-cured silicone rubber was found to be the most suitable material for simulation of the arterial walls, with a physiological Young’s modulus of 0.13 ± 0.03 MPa (19% lower than that of the coronary arteries), storage modulus of 0.07±0.03 MPa (273% higher), and loss modulus of 0.005±0.002 MPa (43% higher). The CT Number of the platinum-cured silicone rubber was found to best suit the required range of values representative of the arterial walls. To manufacture the phantom coronary arteries, moulds of the arterial lumen were 3D printed on a commercial SLA printer, onto which the platinum-cured silicone rubber was dip/brush coated. The phantom coronary arteries were attached to a phantom heart model, filled with an iodine contrast and CT scanned. The average CT number of the phantom artery wall was found to be 65 HU, which is in agreement with the accepted range. The work presented in this study demonstrates the feasibility of in-house manufacturing of imaging phantoms.

2.1 Introduction

Computed Tomography Coronary Angiography (CTCA) is a relatively quick and minimally invasive alternative to other catheter-based procedures, including intravascular ultrasound and coronary angiography, which can be used to diagnose Coronary Artery Disease (CAD) in patients with 50% or greater luminal stenosis. Current research aims to improve CTCA for identification of smaller plaque deposits as well as identifying plaque composition. However, the small diameter of the arteries as well as limitations in CT spatial and contrast resolution make it difficult to distinguish non or mildly calcified plaque from its surroundings and identify whether it has a predominately lipid-rich or fibrous composition [1], as well as to accurately assess the size of calcified plaque due to the occurrence of bloom artifacts [2]. Additionally, CT image quality is affected by physiological factors including a patient’s body mass, a high and irregular heart rate, and respiratory motion [2,3]. Previous studies have found that image quality can be improved by making adjustments to parameters such as the luminal contrast, scan tube voltage, and kernel choice [4,5,6]. New image reconstruction and analysis software are continuously being developed to improve image quality as well. In order to determine the influence of factors such as those mentioned above, find optimal settings for various patient types, and validate new software, repeated testing via CT scanning is required.

Imaging phantoms serve as substitutes for biological tissue by mimicking their CT attenuation properties. However, commercially available options cannot simultaneously replicate the motion and geometry of the coronary arteries, both of which are factors that can significantly affect the acquired image [2,5,7]. Previous research has focused on in-house development of a dynamic anthropomorphic heart phantom (DAHP), in which an electrical and mechanical apparatus simulate the contraction, relaxation, and twisting motion of a heart model with physiologically accurate mechanical and geometric properties, as well as the appropriate CT attenuation when scanned using a dual-energy 320-row multidetector CT (MDCT) [8,9]. Phantom plaque inserts mimicking the CT attenuation of plaque with various compositions have been developed for the DAHP as well [10]. The present study describes the next stage of development, which involves creating an anthropomorphic phantom coronary artery network to be integrated with the DAHP.

A well-established method for manufacturing compliant organ models exists in literature [11-17]. Various elastomeric materials, such as polyurethane, tin- and platinum- cured silicone rubbers, and natural rubber (latex) are examples of soft-tissue mimicking materials used [18-21]. The selection of a suitable phantom material will be partially based on comparing their Young’s Moduli. The tensile properties of coronary arteries are available in literature [22-25]; however, due to variation in the methods used and reported values, as well as the lack of information regarding any differences in properties among the main arterial branches, the tensile properties will be characterized for the right (RCA), left anterior descending (LAD), and circumflex (CX) arteries in the present study. In order to account for their viscoelastic nature and cyclic loading experienced in-vivo, the dynamic properties of the coronary arteries will be investigated as well. Burton et al recently characterized the storage and loss moduli of porcine LAD arteries by using a dynamic mechanical analyzer (DMA) [26]. The present study will test and compare the behavior of the RCA, LAD, and CX branches.

In addition to having the appropriate mechanical properties, the phantom material must have a CT number comparable to that of the coronary artery walls, which due to their small size, blend in with surrounding myocardial tissue, which ranges from 40 to 100 HU. Due to limitations in resolution, the measured CT number decreases for objects less than 5mm in thickness, and increases with the use of contrast agent; the effect of these factors on the CT number of the phantom arteries should be taken into consideration as well [4,27,28]. The present study will demonstrate the manufacturing technology and tissue-mimicking materials that can be used to create anthropomorphic and compliant phantom coronary arteries.

2.2 Methods

2.2.1 Mechanical Behavior of Coronary Arteries 2.2.1.1 Sample Acquisition

Hearts were harvested from 1.5 year old domestic pigs, vacuum- packed, and refrigerated at 4℃ for up to 24 hours. The hearts were removed from storage just before dissection. For each test, 5 proximal segments of the LAD, CX, and RCA ranging from 11 to 31 mm in length were obtained. Figure 2.1 a) shows a typical porcine heart from which the arteries were extracted, as well as the dissection path along which the LAD was cut. Care was taken to remove any loose

31 connective tissue from around the arteries and to avoid excessive stretching of and damage to arterial walls. Figure 2.1 b) shows an example of an extracted arterial segment. Dissections were carried out at a room temperature of 23℃, and the heart and arteries were continuously sprayed with saline solution to avoid drying. Once isolated, three measurements of both the outer and inner diameter were taken, and the average wall thickness was calculated, after which the arteries were placed into saline solution for up to 6 hours until testing. Prior to testing, the arteries were removed from storage, and test samples for dynamic mechanical analysis and uniaxial tensile testing were both prepared at room temperature.

Figure 2.1 a) Porcine heart with an outline of the cutting path used to extract the LAD b) an example of an arterial segment after dissection

2.2.1.2 Uniaxial Tensile Testing

ASTM standard D412-16, Standard Test Methods for Vulcanized Rubber and Thermoplastic Elastomers-Tension was used to design the protocol for tensile testing of the coronary arteries, since their mechanical properties are very similar to thermoplastic elastomers

32 in terms of their low stiffness and high elongations. A uniaxial tensile testing machine (Instron 5848 Microtester) with a load cell of 500 kN was used to test the static mechanical properties of the samples. To prepare the samples for testing, 2.5 cm x 2.5 cm pieces of sandpaper were cut to match the size of the grips to be used for securing the arteries in the testing equipment. Each artery’s end was then glued between two sandpaper squares using cyanoacrylate super-adhesive, as shown in Figure 2.2 a). Samples were continuously sprayed with saline solution to avoid drying. The samples were loaded into the machine, ensuring that no slack was present in the specimen and proper alignment in the grips. The gauge length was then measured and taken as the initial specimen length. Tests were carried out until failure at a temperature of 23℃ and 15% humidity, with a strain rate of 50 mm/min. The tensile testing setup is shown in Figure 2.2 b).

Figure 2.2 a) Arterial segments are prepared for tensile testing by securing both ends to sandpaper using cyanoacrylate glue before b) tensile testing of arterial segments until failure

2.2.1.3 Dynamic Mechanical Analysis

A dynamic mechanical analyzer (TA Instruments, DMA Q800) was used to measure the dynamic mechanical properties of the coronary arteries in accordance to ASTM standard D5992. In order to simulate the in-vivo environment of the coronary arteries, a submersion compression clamp was used. To prepare samples for dynamic mechanical analysis, the arteries were first cut longitudinally into strips. The strips were then folded into layers to form square disks.

Cyanoacrylate glue was applied in between layers during folding to ensure that samples retain their square disk shape during testing. This procedure is shown in Figure 2.3 a) to c). The edge length and thickness of the square disks was then measured. The samples were continuously sprayed with saline solution to avoid drying. Samples were tested in saline solution to simulate the aqueous environment of coronary arteries and the DMA was programmed to heat the sample to 37℃ to simulate body temperature. A frequency range of 0.5-3.5 Hz was selected to represent a range of physiological heart rates including bradycardia (< 1 Hz; <60 bpm), healthy heart rates at rest and during maximum exertion (1-3 Hz ; 60-180 bpm), and tachycardia (>3 Hz ; up to 210 bpm).

Figure 2.3 Procedure used to prepare samples for DMA, in which a) the artery is first cut longitudinally, and the strip is then b) folded onto itself to create c) a square disk geometry

2.2.1.4 Statistical Analysis

Statistical analysis was performed in Microsoft Excel 2007. A one-way ANOVA was performed on the outer diameter and wall thicknesses of the coronary arteries to determine

34 whether there were any significant differences in size between the proximal segments of the tested LAD, CX, and RCA samples. A one-way ANOVA was also performed on the tensile test results to determine whether there were any significant differences between the physiological Young’s moduli. A one-way ANOVA was performed to compare results for storage and loss moduli at 1.25 Hz against the coronary artery segments as well. All tests were carried out using an alpha value of 0.05.

2.2.2 Mechanical Behavior of Tissue-Mimicking Materials

2.2.2.1 Sample Preparation

The representative compounds chosen for the present study were polyurethane rubber (Brush-On 40, Smooth-On Inc.), platinum-cured silicone rubber (Ecoflex 00-35, Smooth-On Inc.), tin-cured silicone rubber (Moldmax 10T, Smooth-On Inc.), and latex rubber (Moldcraft FMF, Burma Rubber Co.). The polyurethane (PU), platinum-cured silicone (Pt-Si) and tin-cured silicone (Sn-Si) rubbers consisted of part A and part B compounds which were prepared in accordance with instructions provided by the manufacturer. To increase the viscosity of the Sn-Si rubber, 6 drops of a thickener (THI-VEX, Smooth-On Inc.) were added to the mixture. For tensile testing, cylindrical moulds 50 mm in length and 4.76 mm in diameter were dip and brush coated in each material to create tubular samples with a target wall thickness of 1 mm. For dynamic mechanical testing, samples were prepared by casting sheets of material 5 mm in thickness and cutting them into 1mm x 1mm square disks. Three tubular and three square disk samples were made for each material. The prepared samples are shown in Figure 2.4 below.

Figure 2.4 Tensile (left) and DMA (right) samples prepared using tissue-mimicking materials

2.2.2.2 Testing

Uniaxial tensile testing was carried out on tubular samples (see Section 2.2.1.2 for procedure). Dynamic mechanical analysis was performed on the square disk samples using the same experimental setup as for the coronary arteries, but at room temperature and without soaking the sample in saline solution to simulate the operating environment of the phantom coronary arteries.

2.2.3 CT Attenuation Properties of Tissue-Mimicking Materials

Samples were CT scanned (320 MDCT AquilionONE; Toshiba Medical Systems) at Toronto General Hospital. A standard CTCA protocol was used in order to simulate clinical practice; these scan settings are listed in Table 2.1. To determine the actual attenuation of each material, the square disk samples were placed in a polypropylene container and scanned in air, with bolus material (Superflab, Mick Radio-Nuclear Instruments Inc.) placed on top and below to simulate the thickness and density of the thorax. To determine the attenuation of each material as a vessel wall, the tubular samples were scanned in the same container oriented parallel to the z-axis, first hollow and then filled with iodine contrast (Visipaque, GE Healthcare Inc.) agent diluted with ultrasound gel for an average attenuation of 250 HU. Images from the axial view were used in finding CT numbers. For square disk samples, an ROI of approximately 5mm 2 was placed in the center of their cross section (Radiant DICOM Viewer software). For hollow tubular samples, a line was drawn through their cross section and an intensity profile was created (ImageJ software), from which the peak intensity was taken as the wall attenuation. For filled samples, an intensity profile was created for a line drawn from the center of their cross section to their average radius, and the final intensity value was taken as the wall attenuation. An average CT number for each sample was calculated by taking the mean of twenty recorded CT numbers.

Table 2.1 CT scan settings used for imaging candidate materials Scan Type Helical Tube Voltage 120 Kv Tube Current 100 mA Axial Slice Thickness 0.5 mm Single Collimation Width 0.5 mm Reconstruction Slice 0.3 mm Increment Reconstruction Filter Medium soft tissue

2.2.4 Phantom Manufacturing and Validation 2.2.4.1 Mould Preparation

To obtain the correct arterial geometry for the mould, DICOM files from a patient CTCA were obtained from Toronto General Hospital [10] and the coronary arteries were segmented in 3D Slicer software to create a 3D computer model. A thresholding function in the software was used to filter out any tissue with a CT number lower than 200 HU, after which the lumen of the coronary arteries can be isolated by manual highlighting of the corresponding area in each image. Meshmixer software (Autodesk Inc.) was used to refine the model by removing any surrounding tissue that remained, smooth rough surfaces, and fill gaps. Knobs were also added to the base of the arteries for easier handling of the mould. The 3D model was then saved as an STL file and the moulds were 3D printed on a FormLabs 2 SLA printer using a polypropylene-like resin (Durable resin, FormLabs Inc.). After printing, the moulds were soaked in an isopropyl alcohol bath for 12 hours, in order to remove excess material and increase their flexibility to ease their removal after casting. The models were then removed from the bath and left to dry overnight.

2.2.4.2 Casting of Phantom Coronary Arteries

The material which was found to be most suitable in mimicking the coronary arteries was used to cast the phantom coronary artery network. The material was prepared in accordance with the supplier’s specifications, and then brushed onto the 3D printed moulds. Once the material vulcanizes, the moulds are removed from inside of the phantom and disposed of.

2.2.4.3 Validation

The cast coronary arteries were integrated with the DAHP at Toronto General Hospital. The arteries were secured to the surface of the heart model in the desired position. Iodixanol contrast agent (Visipaque, GE Healthcare Inc.) was mixed with ultrasound gel to mimic contrast enhanced blood with an average CT number of 385 HU, and injected into the coronary arteries. A previously manufactured phantom plaque insert [10] was added inside the phantom artery. Holes were made at the distal ends of the coronary arteries to aid in the removal of air bubbles and filling with the ultrasound-contrast agent mixture. The DAHP was set up on the patient table, the heart model assembly was placed within a commercially available anthropomorphic thorax

37 phantom (QRM GmbH, Moehrendorf, Germany) and CT scanned (320 MDCT AquilionONE; Toshiba Medical Systems) using the same settings listed in Table 2.1. This setup is pictured in Figure 8a) and b).

2.3 Results

2.3.1 Mechanical Testing of Coronary Arteries 2.3.1.1 Uniaxial Tensile Testing

The dimensions of all tested specimens, as well as the average dimensions for each arterial branch are listed in Table 2.2. A typical stress-strain curve for the coronary arteries is shown in Figure 2.5, and features an initial linear region, a non-linear elbow region, and a final linear region, followed by the gradual tearing of the arterial tissue.

Table 2.2 Data for outer diameter and wall thickness measurements of tested samples, as well as their diameter to thickness ratios

Sample Outer Diameter (mm) Wall Thickness (mm) Do/t Ratio

CX1 2.43 0.18 13.5

CX2 3.67 0.50 7.34

CX3 3.30 0.69 4.78

CX4 3.47 0.84 4.13

CX5 3.80 0.54 7.04

CX average 3.33 ± 0.54 0.55 ± 0.25

LAD1 3.98 1.14 3.50

LAD2 3.48 0.85 4.09

LAD3 3.02 0.57 5.30

LAD4 3.49 0.82 4.26

LAD5 3.52 0.76 4.63

LAD average 3.50 ± 0.34 0.83 ± 0.21

RCA1 3.22 0.84 3.83

RCA2 3.34 0.60 5.57

RCA3 3.42 0.81 4.22

RCA4 4.83 0.79 6.11

RCA5 3.81 0.54 7.06

RCA average 3.72 ± 0.66 0.72 ± 0.14

Figure 2.5 Example of a typical stress-strain curve of a coronary artery

Only data up to 80% strain, which was the lowest yield strain observed for all tested samples, was considered. Data for the LAD, CX, and RCA samples was then fitted with a fourth order polynomial and averaged. The elbow region of each average stress-strain curve was used to

39 find the transition point. Figure 2.6 below demonstrates this process. First, a secant line from the origin to the final value was drawn, and its equation was used to find the stress values along its length. The difference between the stress values along the secant and the average stress values of the arteries was used to find the greatest vertical distance (max ∆σ). The stress and strain values at this point constitute the transition point. To calculate the physiological Young’s moduli, a secant line was drawn from the origin to the transition point, and calculated using equation (1). The Young’s Moduli of the coronary arteries, as well as the stress and strain at the transition point, are shown in Figure 2.7 and listed in Table 2.3.

Figure 2.6 Average stress-strain curve for the CX arteries demonstrating the process of finding the transition point (indicated by the red circle)

(1)

Figure 2.7 Average Young’s moduli and transition stresses and strains for each arterial branch

Table 2.3 Values of average Young’s moduli and transition stresses and strains for each arterial branch Physiological Transition Stress Transition Arterial Branch Young’s Modulus (MPa) Strain (mm/mm) (MPa) CX 0.104 ± 0.0641 0.55 ± 0.022 0.19 ± 0.12

LAD 0.0615 ± 0.0119 0.52 ± 0.042 0.12 ± 0.027

RCA 0.0759 ± 0.0322 0.45 ± 0.029 0.18 ± 0.074

Average 0.0805 ± 0.0216 0.51 ± 0.051 0.16 ± 0.036

2.3.1.2 Dynamic Mechanical Analysis

By carrying out repeated testing, it was determined that the most consistent results were obtained when samples with a height between 3 and 5 mm were used. Samples with a height above 5 mm showed a characteristic increase in loss modulus, suggesting sample slippage [37]. Results for storage and loss moduli for each arterial branch are presented in Figure 2.8 a) and b), and averages for a frequency of 1.25 Hz are shown in Table 2.4.

Figure 2.8 Average a) storage and b) loss moduli of CX (square markers), LAD (triangle markers), and RCA (circle markers). Each point represents an average of five tested samples.

Table 2.4 Average storage and loss moduli of each coronary artery location at a frequency of 1.25 Hz.

Arterial Section Storage Modulus (MPa) Loss Modulus (MPa)

CX 0.0220 ± 0.0154 0.00397 ± 0.00342

LAD 0.0120 ± 0.00306 0.00217 ± 0.000537

RCA 0.0300 ± 0.0191 0.00543 ± 0.00430

Overall 0.0213 ± 0.00902 0.00385 ± 0.00163

2.3.2 Mechanical Testing of Tissue-Mimicking Materials

2.3.2.1 Uniaxial Tensile Testing

A comparison of the average physiological Young’s moduli of the tissue-mimicking materials and coronary arteries is shown in Figure 2.9. These values as well as the percent difference between the coronary arteries and the material samples are listed in Table 2.5.

Figure 2.9 Average physiological Young’s moduli of tissue-mimicking materials

Material Average Physiological Young’s Modulus % Difference

(MPa) (±%)

Pt-Si 0.13 ± 0.03 -19

Sn-Si 0.23 ± 0.04 +77

Latex 0.83 ± 0.03 +438

PU 1.23 ± 0.44 +669

2.3.2.2 Dynamic Mechanical Analysis

The mean storage and loss moduli of the tissue-mimicking materials as well as the tested arteries are shown in Figure 2.10 a) and b). Values for each material and frequency are an average of three tested samples. Measurements at 1.25 Hz, as well as the percent difference relative to the coronary arteries are listed in Table 2.6.

Table 2.6 Average storage and loss moduli at 1.25 Hz for the tissue-mimicking materials and percent difference relative to the tested coronary arteries.

Material Storage Percent Loss Modulus at Percent Modulus at 1.25 Difference 1.25 Hz Difference Hz (±%) (MPa) (±%) (MPa)

Pt-Si 0.0704 ± 0.0332 +273 0.00500 ± 0.00198 +43

Latex 0.0916 ± 0.0768 +385 0.00755 ± 0.00655 +115

Sn-Si 0.0800 ± 0.0171 +324 0.00959 ± 0.00191 +173

PU 0.191 ± 0.276 +911 0.0229 ± 0.0328 +553

2.3.2.3

Results of the one-way ANOVA for sample dimensions show that there are no significant differences in the average outer diameter (p=0.52) and wall thicknesses (p=0.13) of each arterial branch. ANOVA for the mechanical properties of the coronary arteries show no significant differences in the physiological Young’s moduli (p=0.33), as well as storage and loss moduli (p=0.18 and p=0.30, respectively).

2.3.3 CT Attenuation Properties of Tissue-Mimicking Materials

The CT numbers of the square disk samples as well as the hollow and contrast-filled tubular samples are listed in Table 2.7 below. The average wall thickness of the tubular samples was 1.03±0.05 mm collectively and the mean measured CT number for the contrast enhanced lumen was 212±15 HU. The CT number of the bolus material was 52±21 HU. Ranges representing the CT numbers for the myocardial tissue (coronary artery walls), the contrast enhanced arterial lumen, and the epicardial adipose tissue surrounding them as well as the CT attenuations of samples which fit these ranges are shown in Figure 2.11.

Table 2.7 CT Number Measurements from axial sections of each cast sample

CT Number (HU) Material Tubular (Hollow) Tubular (Contrast-Filled) Square Disk

Pt-Si -306 ± 48 76 ± 31 129 ± 13

Latex -261 ± 34 -87 ± 28 -133 ± 13

PU 76 ± 46 74 ± 48 295 ± 10

Sn-Si -215 ± 38 43 ± 25 184 ± 2

Figure 2.11 Ranges for CT Numbers of tissue seen in CTCA images, as well as the average CT numbers of samples that fit within the ranges.

2.3.4 Phantom Manufacturing and Validation

The 3D computer models and printed positive moulds, as well as the final models of the phantom coronary arteries are shown in Figure 2.12 a)-c), respectively.

Images and CT scan slices of the coronary arteries integrated with the heart phantom are shown in Figure 2.13. CT number measurements were taken using a hospital cardiac imaging workstation (Vitrea, Vital Images).

2.4 Discussion

After dissection and measurement of the proximal coronary artery segments, no significant differences in the average outer diameter and wall thicknesses of each branch were found. In previous studies, the Laplace formula for thin-walled, cylindrical pressure vessels was used to calculate a stress range based on diastolic and systolic blood pressures, which can be used to derive physiologically relevant mechanical properties from stress-strain data. This method requires that the diameter to thickness ratio of the artery is greater than 20 [29], which in the current study is not met according to the measurements listed in Table 2.2. Instead, the midpoint of the non-linear portion of the stress-strain curve (the transition point) was used as the maximum average stress and strain which the arteries experience in-vivo. Several authors have confirmed that the elbow region on the stress-strain curve corresponds with physiological pressures [30]. Using this knowledge, the average physiological Young’s moduli for each arterial branch were found as the slope of the secant line joining the transition point to the origin. Although differences in arterial stiffness occur due to variations in elastin and collagen content, which change according to age and health, differences in the Young’s moduli of the CX, LAD, and RCA were found to be insignificant. This may be attributed to the young and approximately equal age of the pigs from which the specimens were harvested, and in which such differentiation had not taken place. The overall average physiological Young’s modulus of the coronary arteries was found to be 0.16 MPa. Although this value is lower than that determined through tensile testing by Karimi et al [22], it corresponds well with the value reported by Shimazu et al where it was determined in live patients via coronary angiography [31], suggesting the means by which physiological Young’s moduli were characterized in the present study are appropriate.

An alternative experimental protocol from the one recently proposed in literature [26] was used for DMA testing of the coronary arteries, which allowed the samples to be soaked in saline solution while heated to body temperature. A similar technique has been used in previous studies for different soft tissue types [32]. Differences in the storage and loss moduli of the CX, LAD, and RCA were found to be insignificant. The storage and loss moduli at 1.25 Hz were chosen for comparison to represent an average patient heart rate of 75 bpm. As listed in Table 2.4, the average storage and loss moduli at 1.25 Hz were 0.021 MPa and 0.0039 MPa, respectively. Although these values are higher than those reported by Burton et al for the same

49 frequency, the orders of magnitude for the storage and loss moduli obtained in the present study are comparable to those found in studies that investigate various other soft tissues using similar methods [32-35].

Although a decrease in elasticity is commonly observed during mechanical testing of biological tissue, no changes in the elasticity of the coronary arteries were seen in this study; this may be due to the fact that all samples were hydrated using saline solution to prevent them from drying out during dissection, preparation, and testing. In addition, since tensile tests were carried out at a strain rate of 50 mm/min resulting in failure within the first 30 seconds of the test, there may have been insufficient time for samples to dry out and stiffen.

Due to insignificant differences in the physiological Young’s moduli, as well as the storage and loss moduli of the arterial branches, the overall average values were used as a comparison for the mechanical properties of the tissue-mimicking materials tested. Due to the imprecise nature of the dip/brush coating technique, it is difficult to maintain accurate dimensional control when preparing samples; however, consistency was most achievable when fabricating samples with a wall thickness of 1mm; hence tubular samples with walls approximately 1mm thick were used to investigate the suitability of each material as a coronary artery in this study. The overall average Young’s moduli for each material are shown in Figure 2.5, with the overall average Young’s modulus for the coronary arteries included for reference. The high standard deviations can be attributed to imperfections in the sample as a result of the dip and brush coating techniques used to prepare them. Results show Pt-Si and Sn-Si rubbers have Young’s moduli of 0.13 ± 0.03 MPa and 0.23 ± 0.04 MPa, respectively, which when compared to that of the coronary arteries are 16% lower and 77% higher. The results from dynamic mechanical analysis of the elastomers are presented in Figure 2.10 and listed in Table 2.6, and also show Pt-Si rubber as having the lowest, and thus the most physiologically representative, storage and loss moduli.

The CT numbers for the square disk samples represent the attenuation of each material without the partial volume effect seen with the thin-walled tubular samples, and are listed in Table 2.7. Yunker et al report CT numbers of 219 HU for Sn-Si rubber, 7 HU for PU rubber, and 133-211 HU for Pt-Si rubbers [18], and Supratnam et al report a value of -107 HU for natural rubber [19]. One reason for this discrepancy the tendency for air bubbles to form in the material

50 as it is being cast into or applied to a mould during sample preparation. This difference may also be due to the particular formulation of the materials used, since x-ray attenuation properties are dependent on a material’s density and molecular weight [36].

Due to limitations in CT spatial and contrast resolution, the attenuation properties of objects smaller than 5 mm (as in the case of the coronary arteries) are reduced significantly. Thus, in order to determine the attenuation properties of each material as a vessel wall, tubular samples were scanned both with and without contrast agent. The walls of the unenhanced PU tubes were found to have an average CT number of 76 HU. As shown in Figure 2.11, this value lies within a range representative of surrounding myocardial tissue. PU may thus be a useful material in manufacturing phantom vessels for use where contrast is not used, such as with calcium scoring. The average CT number of the ultrasound gel-iodine contrast solution mixture was 212 HU, which represents a low luminal enhancement within the 200 to 500 HU range common for CTCA. Using this enhancement level and the scan parameters listed in Table 2.1, Pt-Si (76 HU), Sn-Si (43 HU) and PU (74 HU) were found to have an average attenuation suitable for modelling vasculature, as shown in Figure 2.11. Although values for latex (-87 HU) are much lower than the acceptable range for myocardial tissue, they match the -40 to -122 HU range acceptable for adipose tissue.

Although Pt-Si, Sn-Si, and PU rubber all have suitable average CT attenuation properties, PU rubber is almost 700% stiffer than the coronary arteries, whereas Pt-Si and Sn-Si rubbers are 19% below and 77% above target, respectively. Despite similar measured CT numbers and mechanical properties for the tested Pt-Si and Sn-Si rubbers, its cure time of 5 minutes and suitability for use in brush-on applications make the Pt-Si rubber a more practical option. In comparison, the Sn-Si rubber has a cure time of 24 hours and requires the addition of a thickening agent for brush-on application, as well as vacuum degassing to prevent the formation of a large amount of bubbles, which if not eliminated, may affect CT number measurements. Hence, the Pt-Si rubber was chosen for manufacturing of the phantom coronary arteries. Since the arteries are embedded within the epicardial fat in vivo, rubber latex was used both to attach the coronary arteries to the DAHP’s existing heart model and act as an epicardial adipose tissue simulator.

As shown in Figure 2.12, the phantom coronary artery network created in the present study contains the LAD, CX, a proximal portion of an observed ramus, and RCA, with smaller coronary arteries omitted. One reason for this was difficulty in segmenting their small lumen in the CT images, which is a result of limitations in CT scanner contrast and spatial resolutions, as well as that of imaging software. 3D printer limitations also pose a challenge in producing robust thin features, especially when supports are required. The simplifications made can be pardoned by physiological and clinical relevance. It is well known that plaque tends to accumulate in the proximal and middle sections of the main arteries [37], placing priority on imaging of these regions of the LAD, CX, and RCA. The resulting wall thicknesses of the cast coronary arteries ranged from 0.8 to 1.2 mm. Figure 2.13 shows the integrated phantom arteries and images from a CT scan of the DAHP. The iodine contrast and ultrasound gel mixture seen in the lumen of the arteries had an average CT number of 385 HU, which lies in the target range of 200-500 HU for contrast-enhanced blood. The CT numbers of the anthropomorphic thorax phantom were found to be 34 ± 13 HU (soft tissue), -864 ± 28 HU (lungs), and 236 ± 18 HU (spine). The average CT number of the phantom artery wall was found to be 65 HU, which is 15% lower than the average CT Number of the contrast-filled tubular Pt-Si sample shown in Table 2.7, and may be attributed to thinner wall sections, and a higher luminal contrast level. In this case, the CT number still falls within the 40-100 HU range accepted as the CT number of the arterial walls; however, future studies investigating the suitability of materials for CT applications should also consider the effect of decreased wall thickness and luminal contrast level.

While the results of this study contribute to knowledge of coronary artery mechanics, as well as to the materials and methods that can be used to design CT imaging phantoms capable of mimicking various tissue types, future studies on manufacturing methods with greater control over geometric, CT attenuation, and mechanical properties would be beneficial to achieving greater accuracy and reproducibility of phantoms such as the one created in the present study.

2.5 Conclusion

The stiffness of the three main coronary artery branches was investigated under static and dynamic conditions. The Young’s Moduli were derived from a physiologically relevant region of the stress-strain curves, and lie in good agreement with data for healthy human arteries. Dynamic mechanical analysis of the coronary artery walls using the method described in the present study

52 has not been used before, but values are in good agreement with other biological tissue tested in a similar manner. The average values of the arterial mechanical properties were used as design parameters. The mechanical and CT attenuation properties of platinum and tin cured silicone rubber, polyurethane rubber, and natural rubber were characterized and compared to those of coronary arteries. A platinum-cured silicone rubber was determined to be suitable for simulating the coronary arteries, and latex was found to be suitable for modeling adipose tissue. Segmentation and 3D printing provided an accurate and controllable means of producing a geometrically accurate mould of the arterial network. The properties characterized in this study can be applied to the design of other tissue-mimicking phantoms and biomedical devices, and the design process demonstrates a means for in-house anthropomorphic phantom manufacturing.

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Chapter 3 Novel 3D Printing Technology for CT Phantom Coronary Arteries with High Geometrical Accuracy 3 Summary

There is a growing interest in using Computed Tomography (CT) in imaging non- calcified plaque; however, the complex geometry of the coronary arteries poses a challenge in achieving good image quality, which is crucial in providing patients with an accurate diagnosis. Minimizing artifacts associated with cardiac motion is also an important step in improving CT diagnostic accuracy of Coronary Artery Disease. In this study, the process of manufacturing a plaque phantom with physiologically accurate geometry of the coronary arteries is demonstrated. A computer model is obtained by segmenting CTCA images, and several flexible commercially available materials are used to 3D print the model. The static and dynamic mechanical properties of the 3D printing materials are investigated under physiologically relevant loading and the CT numbers of contrast-enhanced tubular samples with 50%, 75%, and 90% concentric stenosis are characterized and compared with ranges for lipid-rich and fibrous plaque. The proposed plaque phantom design offers the possibility of investigating the effect of non-calcified plaque geometry and arterial motion on various parameters in CT optimization studies.

3.1 Introduction

Computed Tomography Coronary Angiography (CTCA) is highly effective in identifying Coronary Artery Disease based on observing plaque buildup causing a luminal stenosis of 50% or more [1,2]. However, the risk of acute myocardial events is related to plaque composition and higher when non-calcified plaque is present. Noncalcified plaque is often sub-classified as lipid- rich or fibrous. Since it is softer, lipid-rich plaque is more likely to break off of the inner arterial wall and occlude the lumen. For this reason, there is also interest in characterizing noncalcified plaque as predominantly lipid-rich or fibrous using their measured CT Number. This can be done using intravascular ultrasound (IVUS); however, it is a time consuming, relatively costly, and invasive means as compared with CTCA [3]. Based on in-vivo studies, a recommended range of CT numbers for lipid-rich plaque has been defined to be between -30 and 60 HU and the recommended range of CT numbers for fibrous plaque lies between 60 and 150 HU [3–7].

Although there is a large deviation in measured CT numbers between the plaque types, providing a range is necessary for designing plaque visualization software which depends on these values for automatic plaque segmentation. The CT number measurements of the vascular wall are influenced by various parameters including the luminal contrast level [8,9], which varies between 200 and 500 HU in clinical settings; the measurement location within the wall relative to the center of the vessel [8]; and the arterial diameter, which tapers from approximately 5mm to 1mm along the length of major arteries [9,10]. In order to design imaging protocol which can identify plaque composition more effectively, it is necessary to determine how CT number measurements will change based on these parameters.

Plaque phantoms used to study the effects of these parameters and imaging protocol have been manufactured using low density materials such as acrylic [10,11], acrylonitrile butadiene styrene (ABS) [11,12], nylon [12], and ethylene vinyl alcohol (EVOH) [8]; these phantoms are often manufactured as tubes with various wall thicknesses which simulate plaque of various shapes and levels of stenosis. Toepker et al. [13] took advantage of the geometric control and customizability of 3D printing technology to create a tubular vessel phantom (105 HU) with separate plaque inserts (72 HU) representing various degrees of luminal stenosis.

A shortcoming of these plaque phantoms is their regular, constant diameter, which does not represent the tortuous, tapered geometry of in-vivo coronary arteries, on which image quality is highly dependent. In addition, their rigidity limits their application in studies investigating the effect of cardiac motion on CTCA imaging where dynamic heart phantoms are used [14–16]. A well-established method for obtaining a geometrically accurate model of biological tissue exists, and consists of segmenting the desired tissue in DICOM images to obtain a virtual model which can then be 3D printed [17–22]. To make compliant soft tissue models, the 3D printed structures are typically used as molds onto which a soft material is then cast. However, the commercial availability of flexible 3D printing materials has recently made it possible to manufacture compliant organ and tissue models directly [23–27], reducing manufacturing time and providing greater geometrical control.

The goal of this study was to leverage existing image processing techniques and commercially available flexible 3D printing materials and systems to manufacture plaque phantoms with physiologically relevant geometry in a quick and controllable manner. These

58 phantoms would serve as a tool for optimizing CTCA protocol by investigating the effect of arterial geometry, degree of stenosis, and motion on CT number measurements of plaque which will enable radiologists to characterize plaque type with greater accuracy. To determine whether the manufactured phantom arteries are representative of in-vivo arterial tissues, we investigated their CT attenuation and mechanical properties and compared them to values reported in literature. To this end, we CT scanned four flexible 3D printing materials, and the measured values were within the reported ranges for CT numbers of lipid-rich and fibrous plaque types.

3.2 Materials and Methods

In this study, we adopt well established additive manufacturing technology in order to produce complex coronary artery geometry. We used Fused Deposition Modeling (FDM), Stereolithography (SLA), and Polyjet (PJ) [28]. A schematic of each system is shown in Figure 3.1. In the FDM printer (Figure 3.1 a)), a thermoplastic filament is fed into nozzles heated to their melting temperature. Material is deposited in its molten state and solidifies on contact with the printing bed or layers below, and the build platform moves down as the object is printed from the bottom up. In the SLA printer (Figure 3.1 b)), a liquid photopolymer is cured layer by layer by a laser source from below. The build platform moves up as the object is printed from the top down. In the Polyjet printer (Figure 3.1 c)), liquid photopolymer is deposited onto a build platform layer by layer after every overhead pass of the print head and cured by a laser source from above. The build platform moves down as the object is printed from the bottom up.

These methods were adopted for their capability of printing flexible materials. Table 3.1 below lists the printing systems and the materials that were used. We list some of their properties, as well as the name by which each material will be referred to throughout the rest of the present study. Namely, FDM 1, SLA 1, PJ 1, and PJ 2 based on the 3D printing system for which they were designed and used with.

Table 3.1 Materials and printers used to manufacture each sample

Sample Name Commercial Specific Shore Hardness Printer Material Name Gravity

FDM 1 TPU filament 1.14 92A F370

(Stratasys Ltd.) (Stratasys Ltd.)

PJ 1 TangoGrey resin 1.17 75A Objet 30

(Stratasys Ltd.) (Stratasys Ltd.)

PJ 2 TangoBlack resin 1.15 61A Objet 30

(Stratasys Ltd.) (Stratasys Ltd.)

SLA 1 Flexible resin 1.11 73A Form 2

(Formlabs Inc.) (Formlabs Inc.)

Figure 3.1 Schematics of the printing systems used in the present study a) FDM printer b) SLA printer c) Polyjet printer

3.2.1 Mechanical Properties of 3D Printing Materials

Although the most important property of phantoms is to replicate the attenuation properties of in-vivo tissue, we wanted to investigate the mechanical properties for potential

60 application in dynamic imaging. The mechanical behavior of each material was characterized under static as well as dynamic loading simulating the heart’s motion. Materials were tested in flexure to mimic the contraction and expansion of the heart. The Young’s modulus was used as a measurement of material stiffness and the storage and loss moduli were used to assess their time- dependent response. CAD models of rectangular samples 60 mm in length, 12.5 mm in width, and 3.2 mm in thickness were prepared (Onshape CAD software) and three copies of each sample were 3D printed using the materials and systems listed in Table 3.1. The samples are shown in Figure 3.2 below.

Figure 3.2 Samples 3D printed for tensile and DMA tests

The Young’s moduli of the flexible 3D printing materials was characterized via three point bend tests (Instron 5848 Microtester), using a span length of 58 mm, strain rate of 1 mm/min and an average deflection of 2.5 mm. Three samples were tested for each material, with each sample being tested once. The storage and loss moduli of each flexible 3D printing material were characterized via dynamic mechanical analysis (DMA Q800, TA Instruments) using a dual cantilever clamp with a span length of 35 mm and frequency sweep from 0.5 to 3.5 Hz, in order to represent a range of physiological heart rates. The oscillation amplitude was 2.5mm. Three samples of each material were tested, and each sample was tested once.

3.2.2 CT Attenuation Properties

We selected sample geometry based on well accepted dimensions of the coronary arteries. Tubular samples representing vessels 5mm in outer diameter (OD) were prepared, with wall thicknesses representing 50%, 75%, and 90% concentric area reduction (stenosis). Equation (1) was used to determine the inner diameter based on the desired level of stenosis and the outer diameter of large coronary arteries; sample dimensions are shown in Figure 3.3.

(1)

Figure 3.3 Dimensions of tubular samples prepared for CT scanning; each sample was 3D printed using the materials and systems listed in Table 3.1

A mixture of ultrasound gel and iodine contrast solution (Iodixanol, GE Healthcare) was prepared. The average CT Number of the mixture was found to be 200 HU, representing the low range of luminal contrast enhancement in CTCA. Each sample was filled with the gel mixture, and placed in a polypropylene container oriented parallel to the z-axis. A bolus material (Superflab, Mick Radio-Nuclear Instruments Inc.) was placed around the container in order to simulate the thickness and density of the tissue in the chest cavity. A 320 MDCT (Aquilion One, Toshiba Medical Systems) was used to scan the samples in helical mode. A tube voltage of 120 kVp and tube current of 300mA were used. Images were reconstructed with a thickness of 0.5mm and at an increment of 0.3mm.

To quantify the CT numbers of each sample, we performed image analysis in ImageJ software [29]. An overlay of concentric circles with the samples’ inner, outer, and average diameters was created to identify the boundaries of each sample’s walls, and a line was then drawn from the center of the samples’ contrast enhanced lumen to the circle representing the average diameter, as shown in Figure 3.4 a). An intensity plot of the line was generated, and the final reading was recorded as the CT Number of the wall, as shown in Figure 3.4 b). This was repeated 10 times along the circumference of the wall at the average diameter. Three axial slices showing the circular cross-section of the tubes were used to obtain measurements for each sample. The CT number was calculated as an average of 30 measurements.

3.2.3 Manufacturability

As mentioned earlier, the ability to obtain high quality images is crucial in providing accurate diagnosis to the patient. This process is highly dependent on working with physiologically relevant arterial phantom geometry. Therefore, we investigate the feasibility of 3D printing a hollow coronary artery network for use as an anthropomorphic plaque phantom with the materials and systems listed in Table 3.1. Although the arterial walls are not visible in CT images, arterial geometry can be isolated from surrounding tissue using their contrast- enhanced lumen. Hence, patient DICOM images were first segmented in 3D Slicer software to

63 isolate the lumen of major arteries, as shown in Figure 3.5 a). A thresholding function was used to filter out tissue with an attenuation of less than 200 HU, representing the lowest typical level of luminal enhancement. The lumina were manually highlighted in each image to generate a solid 3D lumen model, as shown in Figure 3.5 b). The model was saved as an STL file and imported into Meshmixer software (Autodesk Inc.) for refinement. The solid model representing the arterial lumen was 3D printed in order to determine the feasibility of creating the necessary geometry using each material and system listed in Table 3.1. The mesh was then edited to create a hollow model. In order to do so, the model was duplicated and both copies were overlaid. The duplicated model was offset outwards by 0.8 mm and a Boolean subtraction operation was used to create a hollow interior within the original model, as shown in Figure 3.5 c). The hollow models were then 3D printed using each material and system listed in Table 3.1. In this study, we define manufacturability qualitatively as the ability to reproduce each branch of the arterial network shown in Figure 3.5 b) and c), ensuring that it is hollow on the inside, and that the walls of the models are solid.

Figure 3.5 a) Images from a patient CTCA scan showing the axial (left), sagittal (middle), and coronal (right) views of the heart. The green regions represent tissue with an attenuation of 200

HU or greater after the thresholding function was applied. The arterial walls are not visible; however, the contrast enhanced lumina can be seen and are labeled for each of the three major arteries b) A solid computer model of the arterial lumina obtained after segmentation in 3D Slicer software c) A hollow computer model of the coronary arteries after modifications are made in Meshmixer Software

After multiple iterations, printing processes were designed to meet the needs of each printing system. For SLA 1, hollow models required piecewise printing, i.e. the hollow model was cut into 6 smaller segments for the left arterial branch, and into 3 smaller segments for the right arterial branch in Meshmixer software. Once the pieces were printed and post-processed, they were glued together by applying liquid SLA 1 resin to each junction and curing it using a UV LED flashlight. Distal segments of the arteries were modified with cylindrical vents 1.5mm in diameter. This, and piecewise printing were necessary to avoid the entrapment of excess liquid resin inside the print and facilitate easier cleaning. For both PJ 1 and PJ 2, hollow segments longer than approximately 10 cm could not be printed, due to difficulty in removing support material from inside the model. Since gluing the arteries seamlessly was not possible as with models printed using SLA 1, each branch of the arteries was sliced at its maximum length. Solid models were made for each cut off distal region. Once all pieces were printed, the hollow portions were slipped onto the solid ones.

3.3 Results

The most important characteristic of the plaque phantoms are their attenuation properties, which are characterized by their CT number. Being able to distinguish different tissue types is directly correlated with being able to produce phantoms within specific CT number ranges. Moreover we are interested in characterizing the mechanical properties because they are also relevant in the context of dynamic testing. The following section will present mechanical properties in Section 3.3.1 and CT numbers in Section 3.3.2. Section 3.3.3 will investigate the degree of complexity which we were able to achieve and the challenges encountered with each of the methods.

3.3.1 Mechanical Properties of 3D Printing Materials

The Young’s modulus indicates a material’s stiffness and is an important consideration in designing components which can achieve the desired motion. The Young’s Moduli of the 3D printing materials, as determined from the three point bend tests, are shown in Figure 3.6 and listed in Table 3.2 below. The average Young’s modulus of the coronary arteries, as determined from tensile tests in a previous study [30], is also included for comparison. The vertical scale is logarithmic for better data visualization.

Figure 3.6 Young’s Moduli of the tested materials

*the Young’s Modulus of the coronary arteries was obtained via tensile testing [30]

Table 3.2 Young’s moduli of flexible 3D printing materials and their percent difference relative to the Young’s modulus of coronary arteries

Material Young’s Modulus (MPa)

FDM 1 26.76 ± 0.94

PJ 1 6.39 ± 0.30

PJ 2 5.22 ± 0.32

SLA 1 4.86 ± 0.30

The storage and loss moduli indicate a material’s response when subjected to oscillatory loading and are relevant to consider when designing for integration with a dynamic heart phantom and because the polymeric materials are viscoelastic in nature. The results of the dual cantilever frequency sweep tests carried out on the DMA are shown below in Figures 3.7 a) and b), respectively, and values at 1.25 Hz, representing an average heart rate of 75 bpm, are listed in Table 3.3. The storage and loss moduli of the coronary arteries at the average heart rate are also included for comparison. The vertical scale in both of the below figures is logarithmic for better data visualization.

Figure 3.7 a) Storage moduli and b) loss moduli of the tested 3D printing materials

Table 3.3 Dynamic moduli of flexible 3D printing materials at 1.25 Hz (75 bpm)

Material Storage Modulus (MPa) Loss Modulus (MPa)

FDM 1 29.14 ± 0.57 4.71 ± 0.24

PJ 1 14.60 ± 0.58 16.98 ± 0.55

PJ 2 4.43 ± 0.19 1.23 ± 0.51

SLA 1 8.34 ± 0.36 7.26 ± 0.35

3.3.2 CT Attenuation Properties

In order to assess the CT attenuation properties of each material, the tubular samples were scanned using clinically relevant conditions, which included filling the samples with contrast agent with a CT number of 200 HU and placing bolus material around the samples to simulate the chest wall. The CT Number of the bolus material was found to be 52 ± 21 HU. The CT Numbers of PJ 1, PJ 2, and SLA 1 samples with 50%, 75%, and 90% stenosis are shown

68 below in Figure 3.8, and their values are listed in Table 3.4. We were unable to prepare FDM 1 samples due surface imperfections and difficulty in dissolving support material from the inside.

Figure 3.8 CT Numbers of 3D printed tubular samples with 50% (light grey), 75% (medium grey), and 90% (dark grey) stenosis

Table 3.4 CT Numbers of tubular samples with various degrees of stenosis measured along average diameter

Material 50% stenosis 75% stenosis 90% stenosis

PJ 1 -81 ± 27 -12 ± 13 37 ± 17

PJ 2 -57 ± 31 7 ± 16 32 ± 15

SLA 1 -26 ± 29 -16 ± 17 25 ± 15

3.3.3 Manufacturability

In order to determine the feasibility of manufacturing geometrically accurate plaque phantoms, models were printed using each flexible 3D printing material. The printed solid and hollow artery models are shown in Figure 3.9. Although achieving the general geometry was possible, the hollow model printed with FDM 1 was not manifold, the surface finish was poor, and dissolving support material from the lumen was difficult. It was only the SLA 1 model in which all branches of the arterial network could be 3D printed hollow. This was made possible by printing branch segments separately and assembling the pieces. Using SLA 1 as glue to assemble the pieces ensured that there was no loss of flexibility in the model. An advantage of piecewise printing is that pieces can be printed with different wall thicknesses to simulate different levels of stenosis. Although the polyjet system is not capable of producing the entire arterial network, the larger arterial sections can be printed successfully, offering the possibility of using PJ 1 and PJ 2 to manufacture geometrically accurate proximal and middle arterial segments, where lesions most often occur [31].

Figure 3.9 Hollow 3D printed models of the LAD, CX (left), and RCA (right)

3.4 Discussion

The mechanical properties of the tested 3D printed materials were compared with those of porcine coronary arteries, which were characterized in a previous study via tensile testing and DMA using a compression clamp [30]. Percent differences of 47% (PJ 2), 51% (SLA 1), 72% (PJ 1), and 198% (FDM 1) relative to the Young’s modulus of the coronary arteries were found, and percent differences of the storage and loss moduli of all materials relative to the coronary arteries were approximately 200%. Although not directly comparable to soft tissue and low stiffness elastomers often used to mimic their properties, these results can be used in designing

71 components for applications where they are required to move. Standard deviations in mechanical tests ranged between 0.2 and 0.9 MPa; these low values can be attributed to the reproducibility and accuracy offered by 3D printing.

The solid artery models demonstrate that the geometry of the segmented major arterial branches can be 3D printed. We also demonstrated the feasibility of manipulating the original mesh of the lumen to create a hollow model of a certain wall thickness. Despite its versatility, limitations exist in the minimum wall thickness required for successful printing. The wall thickness of the tubular samples presented in this study ranges from 0.73 mm to 1.71 mm. While it was possible to print all samples using SLA and polyjet systems, FDM was found to be unsuitable for replicating their thin-walled geometry due to poor resolution and flaws in the surface of the print. As part of preliminary research, it was determined that for SLA and PJ prints, the minimum printable wall thickness which would prevent the samples from breaking during post-processing was 0.7 mm. However, we also found that the lumen in SLA prints fused when the inner diameter was 0.95 mm or less. Given these limits, the wall thickness of the hollow arteries printed in this study was chosen to be 0.8 mm. In this study, the manufacturability of these phantoms was defined qualitatively; hence, the next step is quantifying their geometric accuracy by CT scanning each model and overlaying its geometry with that of the coronary arteries shown in Figure 3.5.

Although the wall thickness was constant throughout these models, we demonstrate the feasibility of generating a hollow model with the desired geometry at the minimum robust wall thickness. Using the process we describe, the wall thickness can be adjusted to represent the desired level of stenosis within 3D triangular mesh editing software. The simplicity of the editing process suggests that models can also be cut into segments, with each segment thickened by various amounts, and recombined to create a plaque model with variable amounts of stenosis throughout the arteries.

Figure 3.7 shows the CT numbers of each sample, and values are listed in Table 3.4. The CT numbers for samples with 90% stenosis range from 6 to 66 HU (PJ 1), 9 to 65 HU (PJ 2), and 1 to 57 HU (SLA 1). The CT numbers for samples with 75% stenosis range from -32 to 20 HU (PJ 1), -33 to 49 HU (PJ 2), and -56 to 16 HU (SLA 1). These ranges are similar for each material, and correspond well with documented ranges for lipid-rich plaque, as well as the CT

72 numbers of plaque phantoms used in previous studies. The CT numbers for samples with 50% stenosis range from -138 to 2 HU (PJ 1), -96 to 11 HU (PJ 2), and -99 to 13 HU (SLA 1). While these ranges overlapped partially with those of lipid-rich plaque, the average CT number measurements are below the accepted range. This is due to the samples’ thin walls (0.73mm) relative to the pixel size (0.5mm) and partial volume effects from pixels in the background. Thus, samples with a wall thickness greater than 1 mm, including those with 75 and 90% stenoses best replicate the CT numbers of lipid-rich intimal plaque under the chosen scan parameters.

3.5 Conclusion

In this work, the feasibility of 3D printing flexible plaque phantoms with the physiologically accurate geometry of coronary arteries is demonstrated. Hollow models were printable using SLA and Polyjet technology, and the CT numbers of samples with wall thicknesses between 0.7 and 1.7 mm were found to mimic lipid-rich plaque. The mechanical properties of each material demonstrate their flexibility and suitability for dynamic applications, including integration with dynamic cardiac phantoms. This phantom can be used in studies where the effect of various parameters, including the complex arterial geometry and motion, on CT number measurements of lipid-rich plaque is to be investigated, and will enable the design of optimal imaging protocol.

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Chapter 4 Conclusions and Recommendations

Looking back at the overall objective of this research from Section 1.14, the feasibility of fabricating phantom coronary arteries which can be used in dynamic imaging and plaque characterization studies has been demonstrated. In the first study, our goal was to create synthetic vasculature which would mimic the coronary artery walls. In the second study, our goal was to use 3D printing as a direct means of manufacturing a phantom which combines the geometry and mechanical properties of the artery walls, while simulating plaque in terms of its CT number measurements. A summary of the short term objectives of this research and their outcomes is as follows:

• In the first study, the Young’s moduli as well as storage and loss moduli of the RCA, LAD, and CX have been determined under physiologically relevant loading and environmental test conditions, and no significant differences were found between the three branches. These properties have also been characterized for four types of elastomeric materials which are commonly used in tissue-mimicking applications, namely silicone (Sn-Si, Pt-Si), polyurethane (PU), and latex rubbers. The CT number of tubular samples made from each tissue-mimicking material was determined under clinically relevant parameters as well. Platinum-cured silicone rubber (Pt-Si) was found to best mimic the coronary arteries in terms of both mechanical and CT attenuation behavior at the luminal contrast level, CT imaging protocol, and reconstruction parameters used. Image segmentation and 3D printing were found to be successful means of producing a geometrically accurate coronary artery network, with each of the main branches, a ramus, and several side branches, which could be used to cast the silicone.

• The geometry of the arterial lumen generated in the segmentation process described in the first study was used to successfully produce a hollow version using 3D triangular mesh editing software. Four flexible 3D printing materials (FDM 1, SLA 1, PJ 1, and PJ 2) which have been reported to have CT numbers within the range for soft tissue were used to 3D print the models. The CT Numbers of tubular samples with various levels of concentric stenosis were characterized using clinically relevant parameters. The Young’s moduli, as well as storage and loss moduli were characterized and compared with those

of the coronary arteries. Although PJ 1, PJ 2, and SLA 1 were found to have similar mechanical properties and CT numbers, SLA 1 was deemed to be most suitable for this application due to the possibility of printing robust hollow artery models in their entirety.

Based on the experiences and insights gained in each study, there are several possible suggestions for future work. One of these is to investigate the CT measurements of both elastomeric and 3D printed materials at various conditions, including different luminal contrast levels, tube voltage and current settings, as well as reconstruction filter types. These studies will determine whether the range of CT numbers measured in this study will change with each parameter, and whether each material will continue to be suitable for its application. Carrying out a motion analysis of the manufactured artery models and comparing arterial displacement at various points of the cardiac cycle with those reported in literature will also provide a means of validating the results of mechanical tests conducted in this study. Since Pt-Si is an ideal candidate for mimicking the vascular wall in terms of CT number and Young’s modulus, another possibility for future work is to explore methods by which to modify the material’s storage and loss moduli and match them with the dynamic moduli of the coronary arteries, through means such as the addition of fillers. In the second study, the printability of the phantom arteries was demonstrated. The next step towards validating each 3D printing system as a means to manufacture physiologically accurate geometry is to CT scan the printed arterial networks, and overlaying their images with those of the original patient to demonstrate similarity. Additionally, concentric plaque of a constant thickness was simulated in the phantom. Since plaque most often occurs eccentrically and is confined to certain areas, we suggest the design of a process in 3D mesh-editing software which would enable the user to have greater control over plaque geometry and placement within the arterial model. Another exciting possibility is to dope SLA resin with contrast-enhancing additives which will increase its electron or physical density and allow modeling of soft tissue with higher CT attenuations, such as fibrous and calcified plaque or myocardial tissue. Factors which would have to be considered include resin viscosity, cure time, and maintaining the additive’s dispersion. A study of how additives at different concentrations affect the mechanical properties of printed parts would also assess their suitability for dynamic applications. Designing the vascular network as a hydraulic circuit will enable radiologists to simulate blood flow and study the effect of various arterial and plaque characteristics on hemodynamic flow patterns. Finally, the molecular weight, elemental composition, and electron

78 density of each material used in the present study should be characterized, as these properties are useful in calculating theoretical CT numbers using the equations listed in Section 1.5. Furthermore, there is an opportunity to correlate the empirical constants a, b, c, k, l, m, and n in equation (2) with theoretical CT numbers, which will provide a more direct route to predicting material suitability in mimicking various tissues using CT.