Development of an Anthropomorphic Dynamic Heart Phantom

Sherif Tarek Ramadan

A thesis submitted in conformity with the requirements for the degree of Master of Health Science Institute of Biomaterials and Biomedical Engineering University of Toronto

Sherif Tarek Ramadan

Master of Health Science

Institute of Biomaterials and Biomedical Engineering University of Toronto

2017 Abstract

Dynamic anthropomorphic heart phantoms are a developing technology which offer a methodology for optimizing current computed tomography coronary angiography techniques.

This work focuses on the development of a myocardial tissue analogue material that can be utilized as a synthetic heart for Toronto General Hospitals(TGH) current dynamic phantom.

First, the mechanical properties of myocardial tissue are studied to determine the static (young’s modulus) and viscoelastic (storage/loss modulus, tan delta) properties of the tissue. A dioctyl phthalate and poly(vinyl) chloride material is then developed which mimics the obtained properties and the computed tomography(CT) attenuation of myocardium. The material is then utilized to create a heart/coronary artery model which can be integrated with the phantom in a cardiac CT simulation scan. Through this study it is seen that the phantom provides: a visual simulation to myocardium, motion profiles of the coronary arteries and hearts, and can be used as a plaque analysis tool. iii

Acknowledgments

I would like to start by thanking my parents, Tarek and Manal, who have supported me tirelessly throughout the completion of this thesis. To my brother, Khaled, who always pushes me to be better and has been my greatest role model. My family are an important part of my life and I would not be here without them.

I would also like to thank my supervisors Professor Hani Naguib and Dr. Narinder Paul. Their guidance throughout my thesis has been invaluable. I have had the privilege to work with and be mentored by leaders in both the medical and engineering fields, a fact I do not take for granted. Their fruitful discussions and daily communication has been deeply enriching and has helped me to develop both professionally and personally.

Moreover, I would like to thank the entire SAPL Lab. Everyone in the lab is extremely welcoming and helped me feel part of a family from my first day. With their expertise and guidance, I learned more than I could have ever achieved on my own. A special shout out to Carlton Hoy who I first worked under when I entered the SAPL lab and helped me start to become a researcher.

Additionally, I would like to thank Ali Ursani and the TRIIO Lab for their help throughout this work. Ali worked tirelessly to help me utilize and understand the TGH dynamic heart phantom. His willingness to stay after hours and help perform CT scans and work on the phantom was invaluable and a demonstration of his excellent character. I would also like to thank him for his enthusiasm and energy which make working with him a pleasure.

Finally, I would like to thank my committee members Dr. Walid Farhat and Dr. Terry Yau. Your feedback and questions helped to guide my research and ensure it is applicable in a much wider context. iv

Table of Contents

List of Tables ...... vii

List of Figures ...... viii

Chapter 1 ...... 1

1 Introduction ...... 1

1.1 Coronary Artery Disease and Radiological Care ...... 1

1.2 Computed Tomography and Medical Phantoms...... 3

1.3 Dynamic Heart Phantoms ...... 5

1.3.1 Fluid flow and Mathematical Phantoms ...... 5

1.3.2 Computed Tomography Optimization Phantoms ...... 6

1.4 Motivation - Toronto General Hospitals Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP) ...... 10

1.5 Thesis Objectives and Organization ...... 12

1.5.1 Chapter 2 ...... 13

1.5.2 Chapter 3 ...... 14

1.5.3 Chapter 4 ...... 14

1.6 Novelty/Contributions...... 15

1.7 References ...... 16

Chapter 2 ...... 20

2 Myocardial Tissue Characterization ...... 21

2.1 Introduction ...... 21

2.2 Methods...... 23

2.2.1 Tissue Sample Preparation ...... 23

2.2.2 Dynamic Mechanical Analysis ...... 25

2.2.3 Frequency Response ...... 26 v

2.2.4 Static Tensile Testing ...... 29

2.2.5 Statistical Analysis ...... 30

2.3 Results ...... 30

2.3.1 Dynamic Mechanical Analysis ...... 30

2.3.2 Tensile Testing ...... 32

2.4 Discussion ...... 35

2.5 Conclusion ...... 38

2.6 References ...... 38

2.7 Appendix A ...... 43

Chapter 3 ...... 45

3 DEHP- Polyvinyl Chloride synthetic analogues ...... 46

3.1 Introduction ...... 46

3.2 Methodology ...... 48

3.2.1 Sample Preparation ...... 48

3.2.2 Fourier Transform Infrared Spectroscopy ...... 48

3.2.3 Static Tensile Testing ...... 48

3.2.4 Dynamic Mechanical Analysis ...... 49

3.2.5 Computed Tomography ...... 49

3.2.6 Optimization ...... 50

3.3 Results ...... 50

3.3.1 Fourier Transform Infrared Spectroscopy ...... 50

3.3.2 Static Tensile Testing ...... 52

3.3.3 Dynamic Mechanical Analysis ...... 54

3.3.4 Computed Tomography ...... 57

3.3.5 Optimization Parameters ...... 58 vi

3.4 Discussion ...... 61

3.5 Conclusion ...... 65

3.6 References ...... 66

3.7 Appendix B ...... 71

Chapter 4 ...... 72

4 Validation of a Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom ...... 72

4.1 Introduction ...... 73

4.2 Methodology ...... 75

4.2.1 TREAD-CAP Setup ...... 75

4.2.2 CT Acquisition ...... 79

4.2.3 Data Collection Methods ...... 80

4.3 Results ...... 80

4.3.1 CT Attenuation and Plaque Analysis ...... 80

4.3.2 Qualitative analysis of the Dynamic Phantom Motion ...... 83

4.3.3 Quantitative analysis of the DAHP volume displacement and motion ...... 87

4.4 Discussion ...... 91

4.5 Conclusions ...... 94

4.6 References ...... 94

Chapter 5 ...... 99

5 Conclusion ...... 99

5.1 Concluding Remarks ...... 99

5.2 Future Work ...... 100

5.2.1 Materials Projects...... 100

5.2.2 Imaging Projects ...... 101

vii

List of Tables

Table 2.1: Mean and Standard deviation measurements for (n=5 samples, for n=5 hearts) all 25 samples per animal (all measurements in mm).

Table 2.2: Average Modulus values for each of the five hearts tested per loading condition and animal. Each individual heart is an average of 3-5 separate tensile tests. Standard Deviation was determined based on all individual tests. Table 2.A.1 –DMA raw sample thickness and width measurements for both Ovine and Porcine hearts (all measurements in mm).

Table 2.A.2 – Complete Set of tensile data for both Ovine and Porcine hearts at varying preconditioning settings.

Table 3.1: Stress/Strain results from mechanical testing. An average of n=5 samples from each ratio tested is shown below. The tensile stress/strain at both break and yield are demonstrated as well as the average modulus.

Table 3.2: Summary of the generalized reduced gradient nonlinear equation solver across the various testing parameters. Literature porcine tissue values from Ramadan et al 2017 are utilized as a set of desired target values. The optimization finds that DEHP content of 80.17% creates the closest fit to the porcine tissue based on a weighted equation.

Table 4.1: CT attenuation values for plaques inserted into the Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom. Plaque 1* was tapered down from 4-3-2 mm throughout its length.

Table 4.2: Computed tomography settings and scan protocols. CT scans were performed utilizing a third-generation wide volume CT (Aquilion One Genesis, TMSC, Ottawa). The protocol utilized captured the simulated cardiac cycle.

Table 4.3: CT attenuation values of key phantom components.

Table 4.4: TREAD-CAP left ventricular output as compared to reference ranges from Prokop et al 2003. Deviations occur due to a lower pressure utilized in the current phantom setup. viii

List of Figures

Figure 1.1 – CTCA vs. Invasive Coronary Angiogram.

Figure 1.2: Static Gammex Phantoms utilized for CT calibration.

Figure 1.3: Motion artifact caused by mis-calibration and reconstruction CT procedures.

Figure 1.4: A) A dynamic heart fluid flow phantom complete with physiologically accurate valves. (B) A volumetrically accurate heart phantom intended for mathematical optimization of MRI algorithms.

Figure 1.5: Anthropomorphic beating heart phantom for CT imaging evaluation.

Figure 1.6 – Toronto General Hospitals Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP). A) Outline of critical components. B) Chest, Lung and Heart Phantom Apparatus. C) Inside mechanics of the heart phantom apparatus

Figure 1.7- Toronto General Hospitals Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP) components.

Figure 2.1: A cross sectional view of the heart outlining the anatomical location of both the DMA and Tensile testing specimens. N=5 Hearts were dissected per species and n=3-5 samples were taken from each individual heart.

Figure 2.2: Dynamic Mechanical Analyzer Submerged Compression test setup.

Figure 2.3: Effect of various preconditioning settings on measured properties. a) The inconsistency of measured results based on the effects of 0.01, 0.05N, and 0.1N preloads and oscillation depths of 25, 35, and 50 µm can be seen. b) Effects of a 0.01N preload can be observed with a 50µm oscillation depth. Insufficient contact creates inaccurate measurements between three samples from the same animal and heart. c) The effect of a 50 µm oscillation, 0.3N preload and sub 2mm sample thickness (1.447 and 1.528 Trials 1 and 2 respectively) can be seen through sample deformation leading to erroneous results. ix

Figure 2.4: Storage (a) and Loss Modulus (b) of porcine and ovine tissue with varying frequency. A linear increase is observed as frequency increases. Storage/Loss Modulus 푅2 values for

Ovine tissue are 0.9943/0.9965 and for Porcine tissue are 0.9873/0.993.

Figure 2.5: Stiffness of porcine and ovine tissue with varying frequency. A linear increase is observed as frequency increases. Stiffness 푅2 values for ovine/porcine tissue are 0.9944/0.9878 respectively.

Figure 2.6: Tan Delta of porcine and ovine tissue with varying frequency.

Figure 2.7: Comparison of Modulus values between the three preconditioning parameters: Standard –No load, Preload of 0.05 N and Cyclic Load for ovine myocardial tissue.

Figure 2.8: Comparison of Modulus values between the three preconditioning parameters: Standard –No load, Preload of 0.05 N and Cyclic Load for porcine myocardial tissue.

Figure 2.9: Comparison of Modulus values between porcine and ovine myocardial tissue for cyclic (a) and preloading (b) conditions.

Figure 3.1: Fourier transform infrared spectroscopy of the three DEHP-PVC ratio’s is shown. a) Demonstrates the full absorption spectrum of the DEHP-PVC ratios. b) Demonstrates the presence of a carbonyl absorbance spectrum at 1720 cm-1 for all samples. c) Demonstrates the effect of C-CL binding in the 650 – 750 cm-1 wave region and is an indicator of plasticizer-pvc interaction

Figure 3.2: Stress/Strain curves of each DEHP ratio that was tensile tested. Results from the (n=5) tests that were conducted per ratio is shown from zero strain until specimen failure. Increasing the plasticizer is shown to reduce the measured stress and strain.

Figure 3.3: The percent elongation from tensile testing of the DEHP-PVC ratios are shown as an average of n=5 samples per ratio. The plasticizer is shown to reduce the percentage elongation with increasing percentage of plasticizer. x

Figure 3.4: The Young’s modulus from tensile testing of the DEHP –PVC ratio’s is shown as an average of n=5 samples per ratio. The modulus is shown to decrease with increasing percentage of plasticizer.

Figure 3.5: Submerged compression DMA testing was performed to obtain the storage (a) and loss modulus (b) for (n=5) tests per ratio represented as an average at each data point. Testing was performed for varying frequencies from 0.5Hz to 3.5Hz (30 to 210 BPM) in 0.25Hz increments. The loss modulus is seen to linearly increase with frequency (all 푅2 > 0.978), whereas the storage modulus only exhibits this behavior for the 75% DEHP ratio (푅2= 0.9977).

Figure 3.6: Submerged compression DMA testing was performed to obtain the stiffness for (n=5) tests per ratio represented as an average at each data point. Testing was performed for varying frequencies from 0.5Hz to 3.5Hz (30 to 210 BPM) in 0.25Hz increments. The stiffness is seen to linearly increase with frequency only for the 75% DEHP ratio (푅2 = 0.9979).

Figure 3.7: Submerged compression DMA testing was performed to obtain the tan delta for (n=5) tests per ratio represented as an average at each data point. Testing was performed for varying frequencies from 0.5Hz to 3.5Hz (30 to 210 BPM) in 0.25Hz increments. The tan delta is seen to linearly increase with frequency (all 푅2 > 0.951).

Figure 3.8: Computed Tomography scans at tube voltages and currents of 135kVp/50mA, 120 kVp/75mA, 100kVp/100mA, 80 kVp/150mA were performed on each of the three DEHP-PVC ratios.

Figure 3.9: Data from the Computed Tomography testing (120kVp is commonly utilized in medical imaging), Tensile, and DMA (60,75,90,105 BPM) analysis was graphed across the DEHP-PVC ratios. Polynomial curves of best fit were obtained to be utilized in a generalized reduced gradient nonlinear equation solve

Figure B.1: Storage Modulus (a), Loss Modulus (b), Stiffness (c), and Tan Delta (d) from the dynamic mechanical analysis results for the DEHP – PVC samples submerged in a water solution.

Figure 4.1: Setup. A) The setup of the Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP) utilizes a patient ECG monitor and input/output module in combination with a mechanical pumping system to simulate heart motion. B) A close up of the outer chest and heart phantom models can be visualized.

Figure 4.2: The Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom has two epicardial arterial branches attached on the left and right side of the model; to mimic the left anterior descending and right coronary artery respectively. Plaques of various CT attenuation were inserted at different points within the phantom. Attenuation values can be found in table 4.1.

Figure 4.3: An intra-luminal diagnostic of the coronary arteries. A) A 3D render of the selected artery branch can be seen for the selected arterial branches with embedded plaque. The 3mm Vessel 1 is highlighted here as outlined in figure 4.2. B) An occlusive 400 HU plaque can be seen here. The phantom demonstrates the ability to replicate a variety of plaque conidtions with a visual representation similar to that of live patients.

Figure 4.4: Computed Tomography scan of the Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP). A) and B) Coronal and transaxial CT images of the phantom chambers. C) and D) Coronal and transaxial 3D Surface Shaded Reconstructions of the TREAD-CAP. The myocardium mimicking material, volumetric chambers, and coronary arteries can be clearly visualized.

Figure 4.5: Coronal views of the TREAD-CAP as it replicates the contraction and relaxation cycle of the in-vivo heart from 0 to 95% of the cardiac cycle.

Figure 4.6: Fused Surface Shaded Rendered CT images of the TREAD-CAP at various points in the cardiac cycle. A) Volume expansion from a minimum at 30% to a maximum at 75%. B) Maximum rotation between the 30% and 0% phases during ventricular contraction.

xii

Figure 4.7: Coronal CT images of the TREAD-CAP demonstrating generation of cardiac motion artifacts due to the faster speed of ventricular wall motion experienced at the 25% phase compared to the 70% phase of the cardiac cycle.

Figure 4.8: Volume of the left and right ventricular chambers is demonstrated throughout the cardiac cycle. The end of systole occurs at 30% of the cardiac phase and is associated with the lowest observed ventricular volume. The end of diastole is marked by the largest chamber volume at 75% of the cardiac phase.

Figure 4.9: Arterial motion in the X (Right/Left), Y (Anterior/Posterior) and Z (Superior/Inferior) directions for the left and right coronary arteries. The figure cycles and repeats the oscillatory motion throughout the cardiac cycle.

Figure 4.10: Net displacement of the coronary arteries throughout the cardiac cycle from 0 to 95%. Minimum displacement and rotation occur at the end of diastole as seen at 30% of the cardiac cycle.

Figure 4.11: Velocity profile of the left and right coronary arteries. The maximum velocity corresponds with the end of the systolic phase between 25 and 30% of the cardiac cycle. 1

Chapter 1 1 Introduction 1.1 Coronary Artery Disease and Radiological Care

Cardiovascular disease is one of the leading causes of mortality and morbidity in North America and is the number one killer in the United States (Centers for Disease Control and Prevention, 2014). The most common cause of cardiovascular disease is known as coronary artery disease (CAD). CAD occurs when there is a narrowing of the arteries that supply blood to the heart muscle tissue, myocardium. This narrowing is caused by a buildup of plaque which accumulates on the inner wall of the artery and slowly reduces the effective diameter of the vessel. Plaque consists of multiple different components: cholesterol, fatty substances, cellular waste, calcium, and fibrin. The accumulation of these materials not only narrows the vessels but also hardens them leading to a high resistance to blood flow. This stenosis limits the amount of blood and nutrients that reach the myocardial tissue. The inability of the body to oxygenate the myocardium can lead to severe pathologies including myocardial infarction. The accumulation of plaque is a long-term process and the exact mechanism of cause is under investigation. Many theorize it is caused by endothelium damage based on elevated cholesterol levels, high blood pressure, and cigarette smoking (Hansson et al 2005).

Regardless of the exact cause of CAD it is important for physicians to be able to effectively and accurately diagnose the quantity of plaque accumulation. This can help guide further treatment and determine the necessity of further operations such as a coronary bypass procedure. Currently there are two options for diagnosing CAD, one is invasive while the other is noninvasive. The gold standard diagnoses technique is known as a coronary angiogram (figure 1.1 b) This technique involves inserting a catheter through either the brachial or femoral arteries. The catheter can then be led to the coronary arteries where a contrast dye is injected. This contrast allows for visualization of blood flow in the coronaries under an X-Ray scan. As seen in figure 1.1 b the narrowing or blockage of a vessel prevents the contrast from travelling down the vessel allowing for a diagnoses of CAD severity to be performed. However, in any invasive procedure there is an inherent health risk to the patient. These risks include: allergic reactions to anesthesia, heparin induced thrombocytopenia (HIT) a serious complication caused by saline flushing,

2 infections, vascular injury, cholesterol emboli, and even death. (Tavakol et al 2012). Moreover, the prevalence of these incidents can be subject to the individual operator’s skill when performing the procedure. This being said, severe complications occur in less than 2% of the population. (Tavakol et al 2012). However invasive procedures also have high costs associated with them that supply a burden on the healthcare system, fueling the need to find an alternative methodology (Lowe et al 2014).

A noninvasive test has gained popularity in recent years as an alternative to coronary angiograms (Hamon et al 2006, Budoff et al 2008, Miller et al 2008, Meijboom et al 2008). This procedure is known as computed tomography coronary angiography(CTCA) as seen in figure 1.1 a. CTCA involves the utilization of computed tomography scanners to take a series of X ray slices that comprise the CT scan. Unlike a conventional X ray, the tube is on a motorized gantry which allows for images at multiple different angles to be taken. After each full rotation of the gantry a 2D image slice can be created. By adding in scans at multiple heights (thickness) a 3D image can be created. These images can then be compiled and turned into a stack of slices and/or a volume rendering. These scans allow for calcium and stenosis scoring to be performed while removing the need for an invasive test. (Rispler et al 2007).

Figure 1.1 – CTCA (Left) vs. Invasive Coronary Angiogram (Right) (Nieman et al 2001)

1.2 Computed Tomography and Medical Phantoms

Despite the clear benefits of CTCA it does have a notable drawback that has prevented its more widespread use, namely patient radiation dose exposure (Seigel et al 2004). This issue arises as a balance between the image quality that is obtained and the radiation dose that the patient is exposed to. Computed Tomography scanners fundamentally rely on X rays to create the images that physicians utilize. X rays are produced from a cathode by accelerating electrons utilizing a potential difference, also known as a tube voltage (kVp). These electrons then collide with a metal anode. X rays are then produced via two methodologies. First in characteristic X ray generation the high-energy electron collides with the anode and results in energy loss of the high-energy electron. This energy transfer causes an inner shell electron of the anode to be emitted. An electron on the anode fills in the subsequent “hole” and the associated energy loss emits an X-Ray Photon. The second method of X ray emission is associated with the deflection of the high-energy electron. The loss of energy caused by the loss of momentum from this deflection results in an X ray emission. This is known as the Bremsstrahlung effect. The emission of these X rays is affected primarily by two factors. The energy of the tube potential utilized (kVp) and the tube’s current (mA). Tube current is linearly related to radiation dosage whereas tube voltage determines the energy of the beam and has a squared relationship. However, the tube voltage and current are also directly related to the observed image quality of the scan (Mayo et al 1995). This leads to a balancing act that must occur between patient radiation dosage and observed image quality. In clinical scanning the tube voltages commonly used are 80 kVp, 100 kVp, 120 kVp, and 135 kVp, with tube currents selected to uniform radiation dosages within a study. Based on this 120 kVp and 250 mA will be used in this work.

To combat this issue medical phantoms are utilized. These phantoms are static cylindrical inserts as seen in figure 1.2. These phantoms aim to mimic organs and tissues such as the liver, lung, heart, etc. This is achieved through the material property, electron density. This parameter is measured in Hounsfield Units (HU) and is visualized through the density of the pixel that appears on the display when a CT scan is reconstructed. These phantoms therefore allow for calibration of the CT scanner to ensure tissues appear with the correct HU unit and provide proper image fidelity.

Figure 1.2: Static Gammex Phantoms utilized for CT calibration (Schwahofer et al, 2015)

However, these inserts are static and do not allow for dynamic calibrations. This is a crucial issue for organs such as the heart which are under constant motion and can suffer from motion artifacts. As observed in Figure 1.3, a serious pathology appears to be present but the overlapping of the vessels is an artifact due to blurring of the image.

Figure 1.3: Motion artifact caused by mis-calibration and reconstruction CT procedures. (Kachelrie et al 2015)

Although medical phantoms are effective at their given task there is a great need to improve on these static inserts to solve the challenges of dynamic calibration and image quality.

1.3 Dynamic Heart Phantoms

Dynamic heart phantoms are anthropomorphic devices that mimic the contractile beating nature of the heart. These phantoms can be utilized for a variety of purposes ranging from fluid-flow simulation equipment, mathematical simulation tools, or for optimizing computed tomography scans.

1.3.1 Fluid flow and Mathematical Phantoms

Figure 1.4 a and b display phantoms utilized for fluid flow and simulation procedures respectively. In figure 1.4 a a dynamic phantom was developed complete with functionally accurate mitral and aortic valves to allow for analysis of flow through these vessels. Figure 1.4 b demonstrates a phantom with volumetrically accurate chambers intended for optimization of mathematical algorithms for Magnetic Resonance Imaging. The overall goal of this category of phantoms is to try and replicate the chambers of the heart (atrium and ventricles are considered as a continuous chamber) and observe how fluid flow affects various properties. The unique benefit of these dynamic phantoms is that they allow researchers to control various parameters such as fluid volume, pressure, and heart rate and then quantify the resultant effect.

A B

Figure 1.4: A) A dynamic heart fluid flow phantom complete with physiologically accurate valves (Vannelli et al 2015). B) A volumetrically accurate heart phantom intended for mathematical optimization of MRI algorithms. (Zhu et al 2014).

1.3.2 Computed Tomography Optimization Phantoms Another important class of dynamic phantoms are those designed for optimization of computed tomography techniques. In current literature, these types of phantoms are approached in various manners. Due to the complexity of developing a physical model that mimics heart motion a common approach is to create computerized phantom models with multiple purposes. One such example, is a phantom which allows for the optimization of image reconstruction algorithms (Segars et al 2008, 1999). Development of these algorithms is accomplished through mathematical models which utilize scans to predict behavior.

Although algorithms are valuable in their application, this work aims to focus more on physical models. These models provide the unique benefit of allowing for physical experimentation with CT scanners. Moreover, these phantoms allow for a controlled environment that can accurately mimic a variety of patient populations scanning conditions. These systems are comprised of a combination of mechanical and electrical components that allow for dynamic simulation of a heart model A research example of this type of dynamic heart phantom can be seen in figure 1.5. Phantoms of this nature allow for development and identification of image protocols that are aimed at improving image quality whilst reducing radiation dosage in dynamic applications. These phantoms can be further equipped with tissue realistic coronary arteries. This allows for analysis of how motion affects plaque detection in coronary arteries for improvement of CAD technique diagnoses Greuter et al 2015. The aforementioned analysis can be completed at any stage of the cardiac cycle, allowing for the optimal analysis time period to be determined. Improvements of this nature are critical if CTCA will effectively replace coronary angiography as the gold standard CAD diagnostic procedure. Achieving this optimization requires the manipulation of several key CT scanner parameters; the tube voltage (kVp), tube current (mA), and the scanning protocol utilized (rotation speed/pitch, exposure time, field of view, among others) Boltz et al 2010. However further work is necessary to improve upon current dynamic phantoms. Toronto General Hospital (TGH) is currently developing an in-house Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP) to address this need. However, prior to discussing TGH’s TREAD-CAP which is the focus of this work it is important to properly address the factors that determine a successful dynamic phantom. Specifically, the material that the phantom is fabricated from can have significant affect on it’s functionality. The key material properties

7 desired in a dynamic heart phantom include the computed tomography attenuation, mechanical properties, and geometric properties of the material. Additionally, the phantom must be able to simulate both cardiac motion as well as coronary artery motion.

Figure 1.5: Anthropomorphic beating heart phantom for CT imaging evaluation

(Boltz et al 2010)

1.3.2.1 Computed Tomography Attenuation

The first and arguably most important parameter in a CT dynamic phantom is the CT attenuation of the material the phantom is developed from. CT attenuation is measured in a parameter called the CT number in Hounsfield units (HU). Equation (1) demonstrates the characteristics that determine CT number (Y Watanabe et al 1999).

푒푙푒푐푡푟표푛푠 (1) 푁 ( ) → 휌 (퐸푙푒푐푡푟표푛 퐷푒푛푠푖푡푦) → 휇(퐿푖푛푒푎푟 퐴푡푡푒푛푢푎푡푖표푛) → 퐶푇 푁푢푚푏푒푟 푔 푔푟푎푚 푒

CT Number is a function of the linear attenuation of a material (휇)(2), namely the amount of x rays that are absorbed or deflected when the material interacts with an X ray beam. An important note is that the CT number is standardized to set water as a HU value of 0. The linear attenuation is determined primarily by the energy of the incident X ray as well as the electron density of the material 휌푒, as seen in equation (3). The electron density is then based upon the electrons per gram of the material (4). The first critical material property is found here, the density of our material 휌. Finding the electron density of the material is achieved through our second material property the molecular weight (5).

휇−휇 (2)퐶푇 푁푢푚푏푒푟 = 1000( 푤) 휇푤 푧푚 푏푍푛 (3)휇 = 휌 (푎 푡 + 푅 + 푐(퐸) 푒 퐸푘 퐸푡

(4) 휌푒 = 휌푁푔

푁퐴 (5)푁푔 = (∑ 푛푖푍푖) 푀푊푇표푡

Overall these equations state that various materials will have differing CT attenuations under a CT scanner. Differing HU values correlate to the pixel density of a CT image. Very dense and high molecular weight materials such as bone appear very bright under a scanner and have HU values close to 1000HU. Soft tissue’s like muscle are much less dense and tend to hover around 50-100HU units. This leads to a more greyish appearance Finally; fats tend to however around -800HU and air is the bottom of the scale at -1000 HU (Ursani et al 2015). Therefore, when developing a dynamic heart phantom, it should be prioritized to ensure that the material that mimics the hearts myocardial muscle tissue has a HU value in the range of 50-100HU. Achieving this ensures that the CT scanners and medical operators will visualize the phantom as a heart in terms of pixel density and appearance. Based on Watanabe’s work as well as Hoy et al 푔 2016, we can hypothesize that a material with a density of approximately 1 and a sub 푐푚3 푔 1000 molecular weight will achieve this desired effect. 푚표푙

1.3.2.2 Mechanical properties

The second critical property in a dynamic heart phantom is the ability to mimic the dynamic motion of both the coronary arteries and heart tissue. There are two crucial parameters that any material which will be utilized for a dynamic phantom must possess. These factors are the static elasticity and viscoelasticity (dynamic motion) of the phantom material. Static elasticity is determined by the force-deformation relationship of the material, namely the amount of force required to cause material deformation. This then corresponds with the stress (휎 , Force per area) -strain (휀, deformation) curve of the material. The rate of change(slope) of the stress strain curve is called the Young’s Modulus (E) (6). From this curve the percent elongation of the material can be determined, namely the amount of deformation a material can withstand before failure occurs. These properties are commonly used to gauge the static strength of a material.

푆푡푟푒푠푠 (휎) 퐹표푟푐푒 ∆퐿 (6) 푌표푢푛푔′푠 푀표푑푢푙푢푠 퐸 = , 휎 = , 휀 = 푆푡푟푎푖푛 (휀) 퐴푟푒푎 푙푖

In the current dynamic heart phantom application, the phantom material will be subjected to both static and dynamic loading. Dynamic loading is a function of the beating of the heart that occurs at set heart rates, measured in beats per minute (BPM). This can then be related to the frequency of these heart rhythms. When designing a material, this phenomenon is known as the viscoelasticity of a material. Viscoelasticity describes the stress strain relationship with an oscillating load (7). This is translated to three key components the Storage Modulus, Loss Modulus, and Tan delta of a material. These parameters are described in equation (8,9).

(7)푆푡푟푎푖푛 = 휀 = 휀0 sin(휔푡) , 푆푡푟푒푠푠 = 휎 = 휎0sin (휔푡 + 휕)

휎 휎 (8)푆푡표푟푎푔푒 푀표푑푢푙푢푠 = 퐸′ = 0 cos(훿) , 퐿표푠푠 푀표푑푢푙푢푠 = 퐸′′ = 0 sin(훿) 휀0 휀0

퐸′′ (9)푇푎푛 퐷푒푙푡푎 = 퐸′

As seen from these equations we can convert our static stress-strain curve to a dynamic stress through the incorporation of 휔 which relates to frequency (휔 = 2휋푓). Inclusion of this parameter allows for the stress strain relationship of a material to be analyzed with relation to a frequency, in our case the heart rate of a patient. When quantifying the oscillating load our storage modulus is a measure of the in-phase component of stress. Essentially this describes the elastic response of a material through how much energy is stored in each loading cycle. From this, the loss modulus is the viscous response and measures the amount of energy that is dissipated as heat. Finally, Tan delta incorporates the two and is also called the dissipation factor. The Tan Delta ratio is a measure of energy dissipation of the material. A high tan delta means that the term is driven primarily by the loss modulus, indicating that significant energy dissipation is occurring. A low tan delta indicates that the material response is primarily elastic.

To effectively mimic heart motion the static and viscoelastic behaviors of myocardial muscle tissue must be understood. This will then allow for a material to be developed that will mimic these parameters. The static parameters will allow the material to induce the proper amount of deflection in response to the oscillation force. The viscoelastic properties ensure that the material

10 can maintain these properties over multiple cycles and various heartrates. This is of importance when considering the heartrate of the human heart which varies from 30 BPM to 210 BPM (.5 Hz to 3.5 Hz). As frequency increases we would expect a stiffening to occur and therefore increases in the storage and loss modulus of the heart tissue. Understanding this phenomenon will ensure an accurate stress-strain response which is crucial in ensuring an accurate motion profile. This will allow for accurate arterial motion and simulation when attempting to replicate conditions found in coronary artery disease.

1.3.2.3 Geometric Properties

Finally, the phantom must possess geometrical specifications like that of a human heart. To this end the developed synthetic myocardial material must be easily moldable and processable. Achieving a simple manufacturing process is critical to being able to pour the chosen material into a mold. The specifications for this requirement entails production of over 500grams of material simultaneously. Although a seemingly simple requirement many biomaterials are prohibitively expensive to manufacture in large quantities. Therefore, this requirement must be thoroughly considered before selection of the analogue material. Additionally, the phantom must integrate with Toronto General Hospitals current TREAD-CAP setup as seen in section 1.4 figure 1.6. The mold for this phantom consists of two 130ml chambers that hold diaphragms for fluid flow. The chambers are separated by a septum structure and the phantom possess an apex and a base. Overall this geometry allows for a visual experience like that of a human heart.

1.4 Motivation - Toronto General Hospitals Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP)

Toronto General Hospital has been developing their own dynamic phantom for optimization of CT scanning procedures. Figure 1.6 outlines the overall components required in this phantom. This phantom consists of several key components that allow it to accurately mimic heart motion. One of the unique aspects of the device setup is its potential to provide patient centric radiological care. This is achieved through an electrocardiogram (ECG) electronic simulator. The simulator can utilize the ECG data of a live patient directly input from the ECG monitor. This ECG gating is crucial for ensuring accurate cardiac motion to mimic the patient’s electrical cardiac activity. This is fed into the input/output module which signals the injector and air

11 cylinder to begin pumping air. The air pump then pushes fluid from the air buffer. This allows for a simple air pump system to propel a contrast based fluid (Visipaque 320) to mimic the conditions of a live patient scan. The contrast then flows through to the heart, lung and chest phantom as seen in figure 1.6b. The outer chest wall and lung phantom are intended to mimic the amount of x ray diffraction and beam hardening that occurs when scanning a live patient. In figure 1.6c the current TREAD-CAP can be seen. Prior to the start of this work, the phantom was a polyurethane material with two chambers to simulate the left and right sides of the heart. The fluid bladders that are used allow for the expansion and contraction of contrast fluid in the chambers. The phantom is also mounted on a rotary mechanism. The combination of expansion/contraction and rotation mimics the physiological squeezing mechanism of a human heart. The apparatus shown here then allows for a controlled setting where multiple parameters can be controlled. These include the heart rate of the incoming ECG signal, the pressure in the fluid bladders, the volume, and motion of the TREAD-CAP. These parameters are crucial when trying to mimic various patient’s specific pathology and anatomy.

A B

An overall outline of the TREAD-CAP can be seen in figure 1.7. The electrical and mechanical apparatus has been described and can be seen in figure 1.6. Previous work by Carlton Hoy in the Smart and Adaptive Polymers Lab (SAPL) was done to develop a wide range of modifiable and easily fabricated coronary plaques. This work allowed for accurate quantification and validation

12 of the electron density for various synthetic plaque materials. However, prior to this thesis, limited work had been performed on the development of a myocardial tissue analogue. This synthetic analogue is necessary to replace the current polyurethane insert with a material that possesses mechanical properties which match those of myocardial tissue. This is necessary for simulating accurate cardiac and coronary artery motion. Upon completion of this improvement, further studies and optimization algorithms can be tested on the dynamic anthropomorphic phantom. Overall these further computed tomography studies will allow for improvements to the CTCA CAD detection techniques.

TREAD-CAP

Coronary Synthetic Electrical Mechanical Plaque Myocardial Apparatus Apparatus Properties Tissue Analogue

Geometric Water + Contrast Properties ECG and Flow Pump CT Attenuation Simulator CT Attenuation Flow chambers + Pressure Regulation Fabrication Mechanical Rotation Properties

Figure 1.7- Toronto General Hospitals Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP) components.

1.5 Thesis Objectives and Organization

Overall the goal of this thesis project is to develop a synthetic myocardial tissue analogue material. The phantom developed from this material can then be utilized as a replacement for

Toronto General Hospitals current polyurethane heart model. To achieve the desired properties of the phantom the three factors discussed in Section 1.2.3 need to be addressed, namely the mechanical, CT, and geometrical properties of the phantom. The crucial mechanical properties include the following static and viscoelastic properties: Young’s, Storage, and Loss Modulus, % elongation, and Tan Delta. The desired CT attenuation is approximately 50HU and the developed material must be moldable/processable into a heart-like structure to ensure it can be integrated with the TREAD-CAP in Toronto General Hospital. In working towards these goals the thesis will be split into three key objectives and three main chapters.

Objective 1 – Mechanical characterization of myocardial tissue. This objective involves both tensile and dynamic mechanical analysis of porcine and ovine heart tissue. A novel testing procedure is developed to achieve this aim.

Objective 2 – Development of a synthetic analogue material. A material is chosen that meets the quantitative values obtained in objective 1. Additionally, the material must target a CT attenuation of 50-100 HU and must be easily processable so that it can be poured into a heart mold.

Objective 3 – Validation of the Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP). The efficacy of the developed material must be tested through an experiment utilizing a fabricated heart model integrated with the current TREAD-CAP setup.

Chapter 2 will focus on achieving the first objective of this work, the static and dynamic mechanical characterization of myocardial tissue. Chapter 3 will look at the second objective, the development of a synthetic analogue material to mimic the findings from objective 1. Finally, Chapter 3 will discuss the development of the TREAD-CAP’s myocardial and coronary artery components and validation of the motion profile which will cover the third objective.

1.5.1 Chapter 2

Chapter 2 will cover the first scientific goal of this thesis. Currently in literature there is a lack of concrete experimental data for the mechanical properties of myocardial tissue. This issue arises for both the static tensile as well as the dynamic viscoelastic properties of myocardial tissue. In this chapter a detailed testing methodology to obtain both parameters is investigated. Both tensile

14 and dynamic mechanical testing are performed on porcine and ovine tissues, as they are close human analogues. Tensile testing is performed following ASTM standards, and the dynamic testing is run at a frequency range that corresponded to heart rates of 30 BPM to 210 BPM to cover both healthy and pathological heart rates. Key quantitative values are found for the desired mechanical properties including the young’s modulus, storage/loss modulus, tan delta, and % elongation of myocardial tissue. In addition to the quantitative testing that is performed, an optimal testing methodology for tensile and dynamic testing of the soft myocardial tissue is investigated. From this, it is determined that a cyclic preload prior to tensile testing reduces the deviation of obtained results and is the recommended testing procedure. For the dynamic mechanical analysis, a novel viscoelasticity testing procedure for soft biological tissues is outlined utilizing the Dynamic Mechanical Analyzer (DMA) Q800.

1.5.2 Chapter 3

The focus of this chapter is on the development of a synthetic myocardial tissue analogue that could be utilized for the TREAD-CAP. The proposed material has four key target parameters. These parameters are the materials static mechanical, viscoelastic, manufacturing, and CT attenuation properties. From this criterion, a plasticized PVC (dioctyl phthalate and low molecular weight PVC) compound is selected based on the molecular weight, density, and manufacturing process of the raw materials. The material is a flexible thermoplastic which makes modifications of properties simple and easily modifiable. Additionally, being a thermoplastic allows for the material to be re-melted and cast which meets the mass-manufacturing goal. An investigation into the static, viscoelastic, and CT attenuation properties is then conducted at varying ratios and compositions of the two base parts. From this it is found that an 80% plasticizer: 20% PVC ratio is optimal for mimicking myocardial tissue. The young’s modulus, CT attenuation, and tan delta all corresponded to the porcine myocardial tissue tested in chapter 2.

1.5.3 Chapter 4

After developing a myocardial tissue analogue, the synthetic material was utilized to create a heart model for validation of the TREAD-CAP apparatus. In this chapter the phantom is integrated with the plasticized PVC model developed in chapter 3. Validation of the TREAD- CAP is achieved through three key parameters: accurate myocardial visualization, replication of

15 coronary artery motion, and plaque visualization and analysis. The dynamic apparatus is then run at physiological conditions and the mechanical/electrical system are utilized to simulate a patient ECG trace. The system is integrated with the developed synthetic model which was adapted to incorporate polyurethane coronary artery trees. A CT scan is performed with the phantom running a full cardiac cycle. The scan has several key findings. First the phantom mimics the CT visualization of myocardial tissue. This is an expected result based on the HU value of the material found in chapter 3. Secondly the phantom replicates the profile of cardiac motion with an increase in arterial velocity at the end of the systolic cardiac phase as seen in human physiology. Finally, plaques that were embedded in the coronary artery trees are clearly visualized based on the plaque embedded. The largest source of future work for the phantom is increased accuracy in replicating physiological pressures and creating larger volume and motion differences between the left and right chambers.

1.6 Novelty/Contributions

This work has several key contributions it makes towards materials and imaging research. The following items were previously missing from literature.

1. Quantitative analysis of myocardial tissue’s static and dynamic mechanical properties.

a. Myocardial tissue mechanical values for: young’s modulus, percent elongation, storage/loss modulus, and tan delta are obtained.

b. Investigation of preconditiong settings which reduce deviation in myocardial tissue tensile testing results. A cyclic preload is found to be the optimal testing condition.

c. Investigation, validation, and setting optimization of a dynamic mechanical analyzer for testing the viscoelastic properties of a soft biological tissue.

2. A simple to manufacture myocardial tissue analogue.

a. A novel PVC: Plasticizer material which mimics myocardial tissue.

b. Validation of the PVC: Plasticizer’s mechanical and CT attenuation properties matching that of myocardial tissue.

3. Advancements and validation of a dynamic phantom intended for optimizing CTCA procedures.

a. The integration of a synthetic myocardial tissue analogue material into a medical imaging phantom.

b. Validation of a dynamic phantom intended for optimization of CTCA procedures.

Through these three contributions future work can be performed to advance CTCA clinical evaluation procedures. Ultimately the design of a synthetic myocardial tissue that integrates with the TGH TREAD-CAP is the major contribution of this work. Moving forward the dynamic phantom will allow for the development of ultra-high resolution ultra-low dose Computed Tomography Coronary angiography procedures. This will be achieved through the experimental investigation and modification of CT protocols.

1.7 References

Boltz, T.F., Pavlicek, W., Paden, R., Renno, M., Jensen, A. and Akay, M., 2010. An anthropomorphic beating heart phantom for cardiac X-ray CT imaging evaluation. Journal of applied clinical medical physics, 11(1).

Budoff, Matthew J., David Dowe, James G. Jollis, Michael Gitter, John Sutherland, Edward Halamert, Markus Scherer et al. "Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial." Journal of the American College of Cardiology 52, no. 21 (2008): 1724-1732.

Centers for Disease Control and Prevention, 2014. Summary health statistics: National health interview survey, 2014. https://www.cdc.gov/nchs/fastats/heart-disease.htm

Greuter, M.J., Dorgelo, J., Tukker, W.G. and Oudkerk, M., 2005. Study on motion artifacts in coronary arteries with an anthropomorphic moving heart phantom on an ECG-gated multidetector computed tomography unit. European radiology, 15(5), pp.995-1007.

Hamon, M., Biondi-Zoccai, G.G., Malagutti, P., Agostoni, P., Morello, R., Valgimigli, M. and Hamon, M., 2006. Diagnostic performance of multislice spiral computed tomography of coronary arteries as compared with conventional invasive coronary angiography: a meta- analysis. Journal of the American College of Cardiology, 48(9), pp.1896-1910.

Hansson, G.K., 2005. Inflammation, atherosclerosis, and coronary artery disease. New England Journal of Medicine, 352(16), pp.1685-1695.

Hoy, C.F., Naguib, H.E. and Paul, N., 2016. Fabrication and control of CT number through polymeric composites based on coronary plaque CT phantom applications. Journal of Medical Imaging, 3(1), pp.016001-016001.

Kachelrieß, M., Ulzheimer, S. and Kalender, W.A., 2000. ECG‐correlated image reconstruction from subsecond multi‐slice spiral CT scans of the heart. Medical physics, 27(8), pp.1881-1902.

Lowe, A.S. and Kay, C.L., 2014. Recent developments in CT: a review of the clinical applications and advantages of multidetector computed tomography. Imaging.

Mayo, J.R., Hartman, T.E., Lee, K.S., Primack, S.L., Vedal, S. and Müller, N.L., 1995. CT of the chest: minimal tube current required for good image quality with the least radiation dose. AJR. American journal of roentgenology, 164(3), pp.603-607.

Meijboom, W.B., Meijs, M.F., Schuijf, J.D., Cramer, M.J., Mollet, N.R., van Mieghem, C.A., Nieman, K., van Werkhoven, J.M., Pundziute, G., Weustink, A.C. and de Vos, A.M., 2008. Diagnostic accuracy of 64-slice computed tomography coronary angiography: a prospective, multicenter, multivendor study. Journal of the American College of Cardiology, 52(25), pp.2135-2144.

Miller, J.M., Rochitte, C.E., Dewey, M., Arbab-Zadeh, A., Niinuma, H., Gottlieb, I., Paul, N., Clouse, M.E., Shapiro, E.P., Hoe, J. and Lardo, A.C., 2008. Diagnostic performance of coronary angiography by 64-row CT. New England Journal of Medicine, 359(22), pp.2324-2336.

Nieman, K., Oudkerk, M., Rensing, B.J., van Ooijen, P., Munne, A., van Geuns, R.J. and de Feyter, P.J., 2001. Coronary angiography with multi-slice computed tomography. The Lancet, 357(9256), pp.599-603.

Rispler, S., Keidar, Z., Ghersin, E., Roguin, A., Soil, A., Dragu, R., Litmanovich, D., Frenkel, A., Aronson, D., Engel, A. and Beyar, R., 2007. Integrated single-photon emission computed tomography and computed tomography coronary angiography for the assessment of hemodynamically significant coronary artery lesions. Journal of the American College of Cardiology, 49(10), pp.1059-1067.

Schwahofer, A., Bär, E., Kuchenbecker, S., Grossmann, J.G., Kachelrieß, M. and Sterzing, F., 2015. The application of metal artifact reduction (MAR) in CT scans for radiation oncology by monoenergetic extrapolation with a DECT scanner. Zeitschrift fuer Medizinische Physik, 25(4), pp.314-325.

Segars, W.P., Lalush, D.S. and Tsui, B.M., 1999. A realistic spline-based dynamic heart phantom. IEEE Transactions on Nuclear Science, 46(3), pp.503-506.

Segars, W.P., Mahesh, M., Beck, T.J., Frey, E.C. and Tsui, B.M., 2008. Realistic CT simulation using the 4D XCAT phantom. Medical physics, 35(8), pp.3800-3808.

Siegel, M.J., Schmidt, B., Bradley, D., Suess, C. and Hildebolt, C., 2004. Radiation dose and image quality in pediatric CT: effect of technical factors and phantom size and shape 1. Radiology, 233(2), pp.515-522. Tavakol, M., Ashraf, S. and Brener, S.J., 2012. Risks and complications of coronary angiography: a comprehensive review. Global journal of health science, 4(1), p.65. . Ursani, A., Hoy, C., Moghe, S. and Paul, N.S., 2015. Characterization of Vulnerable Plaque with Dual-Energy during CT Coronary Angiography: A Phantom Study. In World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada (pp. 91-94). Springer International Publishing.

Vannelli, C., Moore, J., McLeod, J., Ceh, D. and Peters, T., 2015, March. Dynamic heart phantom with functional mitral and aortic valves. In SPIE Medical Imaging (pp. 941503- 941503). International Society for Optics and Photonics.

Watanabe, Y., 1999. Derivation of linear attenuation coefficients from CT numbers for low- energy photons. Physics in medicine and biology, 44(9), p.2201.

Zhu, Y., Luo, X.Y., Gao, H., McComb, C. and Berry, C., 2014. A numerical study of a heart phantom model. International Journal of Computer Mathematics, 91(7), pp.1535-1551.

Chapter 2

Standardized Static and Dynamic Evaluation of Myocardial Tissue Properties

This chapter starts the research of this work through the investigation of the mechanical properties of myocardial tissue. The investigation includes the study of both the static and dynamic mechanical properties of myocardial tissue. Porcine and Ovine heart tissues were tested at physiological conditions (37◦ C in a saline solution) as they are close human analogues. Additionally, this work looked at the study of two testing methodologies and techniques to standardize mechanical testing results. First the effects of tensile testing on soft biological tissues utilizing different preconditiong settings was explored. These settings were: no preload, a static preload, and a cyclic preload. It was found that the cyclic preload best replicates physiological conditions and helps to alleviate the effects of the anisotropic heart fiber orientation. This allowed for the quantification of myocardial tissue’s Young modulus and percent elongation. Secondly dynamic mechanical analysis was performed utilizing a DMA Q800. This machine is currently utilized for soft rubbers, but is not extensively utilized for soft biological tissues. Initially work was done to determine the optimal sample size for a submerged compression test. These results allowed for a standardized testing methodology for the quantification of storage/loss modulus and tan delta of both porcine and ovine hearts. Overall this work allowed for a standardized quantification of myocardial tissues static and viscoelastic properties.

The content of this chapter has been published in the Journal of Biomedical Materials: IOP Science

Ramadan, S., Paul, N. and Naguib, H.E., 2017. Standardized Static and Dynamic Evaluation of Myocardial Tissue Properties. Biomedical Materials.

Biomed.Mater.12(2017)025013 https://doi.org/10.1088/1748-605X/aa57a5

2 Myocardial Tissue Characterization 2.1 Introduction

In biological tissue research, attempts have been made to understand and quantify the mechanical properties of various soft biological tissues through both structural and mechanical methodologies (Claes et al., 2010; Song et al. 2007; Claridge et al., 2009; Sommer et al., 2012; Yeh et al., 2002). These properties are of considerable interest, as they allow researchers to create biomaterials that mimic the stiffness and viscoelasticity of a specific tissue.

In mechanical analysis, the Young’s modulus of a material is commonly utilized to represent the static elasticity of a material. This mechanical property represents the amount of force required to cause deformation of a material. The storage-and-loss modulus, however, represent the dynamic viscoelasticity of a material. These dynamic properties embody the force required for deformation of a material in response to an oscillating load. The storage modulus represents the elasticity of a material and the loss modulus demonstrates the amount of energy dissipated as heat. When combined, these two properties allow for analysis of the energy efficiency and elasticity of materials under oscillating loads. Tan Delta is the ratio of the loss and storage modulus and is a measure of the degree to which a material dissipates this oscillating energy. A low tan delta is indicative of a highly energy efficient material.

Evaluation of these properties has demonstrated that repeatability and standardization of results is an issue for a variety of soft biological tissues, including cardiac muscle, tendon, ligament, and aortic valves (Carew et al., 2000; Pinto et al., 1980; Sverdlik et al., 2002; Woo., 1982). This issue manifests itself in different manners for the static and dynamic properties. When tensile testing biological tissues, unlike polymer testing, there is considerable inconsistency in the repeatability of results. The preconditioning of materials has been introduced in order to improve reproducibility and standardize tensile methodologies. Fung (1993) noted that preconditioning before tensile testing tissues is crucial in providing a known loading history and a reproducible set of test conditions. A variety of preconditioning settings have previously been investigated in literature. This includes the use of a static preload or as noted by Carew et al., (2004) a cyclic preload prior to mechanical testing. It has been shown that even subtle variations in the strain and pre-test setup can have significant effects on the measured mechanical properties of soft biological tissues

(Cheng et al., 2009). This has led to considerable interest in developing consistent testing methodologies to ensure an accurate comparison when studying mechanical properties.

Alternatively, dynamic mechanical testing has experienced separate challenges due to the variety of apparatus utilized to analyze the viscoelastic relationship in tendons, muscle, and other soft tissues (Valorta and Mazza, 2005; Stolz et al., 2004). Although each individual research methodology has been scientifically valid, concerns have been raised with the validity of comparing results between multiple different experimental setups. In materials testing the dynamic mechanical analyzer is widely used for testing various polymers, rubbers and biomaterials (Mano et al., 2002; Bartkowiak-Jowsa et al., 2013). Because many biological tissues behave similarly to soft rubbers Zhu (2007) has previously proposed that the dynamic testing procedure for biological tissues should be similar to that of soft polymers in order to provide a comparable level of standardization.

The issue in standardization of experimental design is of particular concern for human myocardial tissue. The commonly quoted modulus values for human myocardium is 0.02 - 0.5MPa (Nagueh et al., 2004; Nakano et al., 1990; Watanabe et al., 2006). These results were determined mathematically, by utilizing end-systolic wall stresses and wall thickness. However, Y Zhu et al (2014) commented on the lack of sufficient experimental data for myocardial tissue. As mentioned previously, the paucity of data creates issues regarding the reproducibly of results. This lack of data becomes apparent when attempting to compare biological tissues with polymer analogues. Poly(Glycerol-Sebacate) or PGS is a commonly used myocardial tissue analogue material (Rai et al., 2012). The mechanical behaviors of PGS and other polymer analogues are tested using highly regulated ASTM and ISO standards. These standards ensure the validity and accurate comparison of materials within a certain class (Li et al., 2013). However, the results from these highly standardized polymer tests are compared to custom/unique biological tissue set-ups. The differences between these setups results in an un-standardized environment, which makes the comparison of results difficult.

There are many potential applications that benefit from increasing the reliability and standardization of mechanical testing results. The development of phantoms and artificial muscles with correct mechanical properties is important for proper functionality. This holds true when developing a dynamic phantom that requires accurate viscoelastic properties to properly replicate

23 bodily motions. As Y Zhu et al (2014) noted, the lack of experimental myocardial tissue data has presented challenges when developing dynamic heart phantoms with accurate pulsatile and contractile heart flow. In addition, the process of bio-mechanical testing standardization extends to tissue scaffold polymers, heart patches, and polymer glues, as these are commonly designed to match the properties of biological soft tissues (Kofidis et al., 2002; Chen et al., 2008; Lang et al., 2014). Troyer et al (2011) and Abramowitch et al (2010) showed that the mechanical properties of tissues are crucial in developing mechanical models that can be utilized in robot-assisted surgery and medical haptic simulation.

This study aims to look at the efficacy of standardization techniques and processes when soft biological tissues are mechanically tested. To ensure a sufficient sample size, both porcine and ovine hearts are utilized as testing samples, as these large animal hearts are commonly used as human analogues (Gupta et al., 1994; Sacks et al., 1999). Two key procedures are analyzed. First, the Young’s modulus of myocardial tissue is obtained utilizing tensile testing. Three different preconditioning strategies are performed: standard testing practice, preloading, and cyclic preloading. The subsequent influence on the obtained modulus is one of the focuses of the study. Second, dynamic mechanical analysis, a commonly used polymer testing methodology, is investigated for use in soft biological tissues to obtain the storage and loss modulus as well as various viscoelastic properties.

2.2 Methods

To perform standardized mechanical elasticity tests for myocardial tissue both dynamic mechanical analysis and static tensile testing were performed. Freshly harvested Porcine (n=5) and Ovine (n =5) hearts were used for the experiments. Each heart had 3 to 5 samples taken along the long axis of the left ventricle to ensure the validity of measurements. All five hearts per species were tested at each loading condition. These hearts were used to quantify the Young’s modulus, stiffness, E’ (Storage modulus), E´´ (Loss modulus), and Tan Delta values.

2.2.1 Tissue Sample Preparation

To ensure standardization of testing conditions the following procedure was followed. Both porcine and ovine hearts were removed from the sacrificed animal and stored at 4°C for transportation to the research facility. The hearts were not frozen to ensure integrity of the muscle

24 tissue, as a freeze-thaw cycle has significant effects on the mechanical properties of biological tissues (Chow et al., 2011). The overall time from sacrifice to transport to testing was constrained to a one day period to limit the effect of tissue degradation. Although no chemicals were used, to limit outside effects on testing results, hearts were vacuum packed to limit bacterial growth.

Porcine hearts were harvested from 15 month old specimens whereas ovine hearts were harvested from 6-8 month old lambs. All hearts were dissected utilizing a surgical dissection kit. To ensure consistency of the tissue, all samples were taken from the left ventricular wall as seen in Figure 2.1. Samples were excised from the mid myocardial layer and both the epicardium and endocardium were removed to ensure that only the functional muscle tissue was tested. This anatomical location of the heart has the thickest amount of muscle allowing for the greatest number of samples to be dissected. Moreover, sampling a specific section of the myocardium helps to limit the variation in the orientation of muscle fibers. As the fiber orientation varies along the layers of the heart tissue (Holzapfel et al ,2009) the mid myocardial layer was targeted to reduce this variation.

Finally, to simulate in-vitro conditions, the samples were immersed for a minimum of 30 minutes to ensure saturation in Hank’s Balanced Salt Solution. The balanced salt solution is designed to mimic isotonic conditions while maintaining a pH of approximately 7.3 (7.0-7.4). All samples were then tested at 37° C. These conditions ensured that the tissue was tested in an environment mimicking its native physiological state.

2.2.2 Dynamic Mechanical Analysis

The TA Instruments DMA Q800 is a dynamic mechanical analyzer that induces oscillating strains to produce a stress response at varying frequencies. This allows for observation of the stress reaction of the material. The submerged compression clamp allows for an oscillating compressive strain to be introduced while the testing specimen is both heated and submerged in fluid. The sample can be fully submerged in saline solution throughout the test and a submerged thermo- couple verifies the temperature of the fluid (Figure 2.2) as outlined by Zhu et al (2007). The machine then measures the engineering stress based on the originally inputted undeformed area of the specimen.

Oscillating Compression Clamp

Fixed Submersion Chamber

Saline Solution

Tissue Sample

Figure 2.2: Dynamic Mechanical Analyzer Submerged Compression test setup.

2.2.3 Frequency Response

A range of 0.5Hz to 3.5 Hz was tested with 0.25Hz increments to mimic the frequency loading of the human heart. This correlates to a human heart rate of 30BPM to 210BPM to cover the range of both healthy and pathological human heart rates. The machine underwent preheating to 37°C before performing the frequency sweep. The samples were tested at a 50µm displacement which is the maximum allowable strain-induced displacement in the submerged compression clamp. Through this displacement the machine can determine the amount of force required to create the oscillating strain in order to determine the tissue stress.

Prior to each loading sequence the tissue samples were preloaded with a 0.1N force at 125% force track. This force track is an additional compressive force equal to 25% increase over the amplitude of the dynamic force. This 125% value is a default setting in the DMA Q800 and was not modified. This setting combined with the preload ensured constant contact between the compressive plate and submerged sample.

These parameters were determined utilizing machine recommended specifications as well as through various preliminary tests on ovine hearts as seen in Figure 2.3. Figure 2.3 a) demonstrates the effect that the oscillation depth and preloading force can have on the measured results. Combined with trials at a lower preload force (0.01N) as seen in Figure 2.3 b) it was determined that a 50µm oscillation depth and 0.1N preload were required to ensure sufficient contact between the clamp and the samples. Reduction of the preload and oscillation displacement below these parameters resulted in insufficient contact with the sample due to the submerged condition.

A) Effect of Preload and Oscillation Distance 0.07 0.02 0.06 0.05 0.015 0.04 0.01 0.03

0.02 0.005

0.01 Loss Modulus(MPa)

Storage Storage Modulus(MPa) 0 0 0 0.5 1 1.5 2 2.5 3 3.5 4 Frequency (Hz) 25µm, 0.01N - Storage 35µm, 0.05N - Storage 50µm, 0.1N - Storage 25µm, 0.01N - Loss 35µm, 0.05N - Loss 50µm, 0.1N - Loss

B) Effect of Insufficient Preload - 0.01N 0.07 0.02 0.06 0.05 0.015 0.04 0.01 0.03

0.02 0.005 Loss Loss Modulus(MPa)

Storage Storage Modulus(MPa) 0.01 0 0 0 0.5 1 1.5 2 2.5 3 3.5 4 Frequency (Hz) Trial 1 - Storage Trial 2 - Storage Trial 3 - Storage Trial 1 - Loss Trial 2 - Loss Trial 3 - Loss

C) Effect of Sample Deformation 15000000 1.2E+10

10000000 1E+10

5000000 8E+09

0 6E+09 0 0.5 1 1.5 2 2.5 3 3.5 4

-5000000 4E+09

Stiffness Stiffness (N/m) Stiffness (N/m) -10000000 2E+09

-15000000 0 Frequency (Hz)

Trial 1 N/m Trial 2 N/m

Finally Figure 2.3 c) demonstrates the effect that sample deformation has on the measured results. Deformation is a result of insufficient sample thickness or excessive preload forces. The DMA Q800 has a maximum allowable sample size of 10x10x5mm (width by length by thickness). Therefore, there is a need for balance between tissue thickness and surface area as reducing the surface area results in sample slippage and difficulties in sample preparation. However, a higher tissue thickness/surface area (t/A) ratio is preferable for an accurate loading profile. From the experimentation in Figure 2.3, an acceptable t/A ratio was found for a sample size of 8 x 8 x 4mm square slabs. The porcine and ovine heart samples mean and SD for both thickness and width (as an average of the two side length measurements) can be found in Table 2.1 below.

Table 2.1: Mean and Standard deviation measurements for (n=5 samples, for n=5 hearts) all 25 samples per animal (all measurements in mm).

Ovine Thickness Width Porcine Thickness Width

AVG 3.94 8.21 4.26 8.34

SD 0.36 0.71 0.45 0.33

A complete set of DMA raw sample measurements can be found in Appendix A.1. The DMA Q800 was used to automatically measure the exact thickness of each specimen prior to testing and to simultaneously calculate the values of storage modulus (E´) loss modulus (E´´), stiffness, and tan delta using information from the stress-strain relationship.

2.2.4 Static Tensile Testing

A uniaxial tensile test was performed to determine the static Young’s modulus. To our knowledge, there are no standards for tensile testing myocardial tissue therefore; the ASTM standard D3039 was used as a guideline for the geometry of the samples to attempt to standardize the results. This standard was designed for polymer matrices that have continuous or discontinuous fibers, a pattern that is analogous to heart tissue. The tests were performed using an Instron 5848 MicroTester System housed in a heated chamber. The apparatus was fabricated to cover the testing area and ensure a 37° C testing environment. The samples were gripped at both ends using a serrated grip at a distance of 20mm with a micrometer adjustment clamp. The testing was performed at 푚푚 5mm/min (−0.57 thickness strain rate), assuming a Poisson’s ratio of 0.47. (Chen et al 푚푖푛푢푡푒 ,1996). Three separate preconditioning strategies were followed: no preconditioning, a preload of 0.05N, and a cyclic preload. The cyclic preload is a 5% strain load at 10 cycles as previously described by Cheng et al (2009). Moreover, samples were prepared through the aid of a tensile testing polymer cutting die to standardize the size of each sample and ensure a proper necking region. Each sample had a necking region of 2cm by 0.5cm and a gripping region of 2cm by 1cm with a thickness of 0.4cm. During testing any samples that did not fail within this necking region were omitted from the study. A surgical dissection kit was used to perform cardiac dissection and to prepare myocardial tissue samples.

2.2.5 Statistical Analysis

Finally, a balanced ANOVA was utilized to compare the frequency response of the dynamic factors. A Welch’s one-way ANOVA test was used for statistical analysis of the tensile stress data. This analysis took into consideration the non-uniform standard of deviation between the populations and inhomogeneous population sizes. The ANOVA was performed for intra and inter species results. The Welch’s ANOVA was also used to determine any statistical significance between results from the three preconditioning settings.

2.3 Results 2.3.1 Dynamic Mechanical Analysis

Figures 2.4 – 2.6 display the results of the dynamic mechanical analysis for both porcine and ovine myocardial tissue. As expected, increasing oscillation frequency results in an observable change for all measured dynamic properties. The ANOVA test demonstrates a significant variation in frequency (p < 0.05) for both animals. Of particular note is the linear (all 푅2 values > 0.987) increase in E´ and E´´ Figure 2.4 and stiffness Figure 2.5 as frequency increases. However, the tan delta stays relatively constant indicating that the storage and loss modulus are increasing at the same proportion. The tan delta value hovers around 0.175 demonstrating the elastomeric nature of myocardial tissue. Through the dynamic properties found in Figures 2.4-2.6 it can be drawn that the ovine tissue acts as a stiffer elastomer as compared to the porcine tissue. This difference is seen through a storage modulus 0.01MPa larger, a loss modulus 0.002MPa larger, and a stiffness increase of 100N/m for ovine tissue.

A) Porcine vs. Ovine Storage Modulus 0.07 0.06 0.05 0.04 0.03 Porcine 0.02 Ovine 0.01 0 Storage Storage Modulus(MPa) 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3 3.25 3.33 3.5 Frequency (Hz)

B) Porcine vs. Ovine Loss Modulus 0.01

0.008

0.006

0.004 Porcine

0.002 Ovine Loss Loss Modulus(MPa) 0 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3 3.25 3.33 3.5 Frequency (Hz)

Figure 2.4: Storage (a) and Loss Modulus (b) of porcine and ovine tissue with varying frequency. A linear increase is observed as frequency increases. Storage/Loss Modulus 푅2 values for

Ovine tissue are 0.9943/0.9965 and for Porcine tissue are 0.9873/0.993.

Porcine vs. Ovine Stiffness 1200 1000 800 600 Porcine 400 Ovine Stiffness Stiffness (N/m) 200 0 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3 3.25 3.33 3.5 Frequency (Hz)

Porcine vs. Ovine Tan Delta 0.25

0.2

0.15

0.1 Porcine TanDelta 0.05 Ovine

0 0.5 0.75 1 1.25 1.5 1.75 2 2.25 2.5 2.75 3 3.25 3.33 3.5 Frequency (Hz)

Figure 2.6: Tan Delta of porcine and ovine tissue with varying frequency.

2.3.2 Tensile Testing

The results of the tensile testing can be seen in Figures 2.7-2.9. The range of modulus values in porcine myocardial tissue varies from 0.012 MPa to 0.198 MPa, and from 0.019MPa to 0.192MPa for ovine tissue varying based on the loading condition. In addition, Table 2.2 outlines the mean (0.047 - 0.087 MPa) and standard deviation (0.023 - 0.05 MPa) for each loading condition and heart (n=3- 5). The one-way anova test demonstrates several sources of significance. There is no significant difference between intra species hearts for all three testing conditions in both species. From this understanding, all of the samples (n=3-5, per heart), from all five hearts for both species were compiled into the average groups of data as seen in Table 2.2. Raw data for each individual test can be found in Appendix A.2

For the ovine myocardial tissue there is a significant difference between the cyclic and standard loading condition (p=0.023, p < 0.05), Figure 2.7. For porcine heart tissue, the significant difference is found between the preloading and standard conditions (p=0.011, p < 0.05), Figure 2.8. Moreover, it is readily apparent that the cyclic loading condition results in a reduction in the standard deviation leading to more accurate and consistent results (Table 2.2). However, both the preloading and cyclic conditions show a significant difference between the ovine and porcine hearts (p=0.008 and 0.04 respectively, p < 0.05) as shown in Figure 2.9. However, this is not true for the standard no preloading condition (p =0.121).

Ovine Tissue Comparison 0.2

0.15

0.1

0.05 Young's Young's Modulus(MPa) 0 Cyclic Preload Standard Loading Method

Figure 2.7: Comparison of Modulus values between the three preconditioning parameters: Standard –No load, Preload of 0.05 N and Cyclic Load for ovine myocardial tissue.

Porcine Tissue Comparison 0.2

0.15

0.1

0.05 Young's Young's Modulus(MPa)

0 Cyclic Preload Standard Loading Method

Figure 2.8: Comparison of Modulus values between the three preconditioning parameters: Standard –No load, Preload of 0.05 N and Cyclic Load for porcine myocardial tissue.

A) Porcine vs. Ovine Cyclic Comparison B) Porcine vs. Ovine Preload Comparison 0.2 0.2

0.15 0.15

0.1 0.1

0.05 0.05

Young's Young's Modulus(MPa) Young's Young's Modulus(MPa) 0 0 Porcine Ovine Porcine Ovine

Figure 2.9: Comparison of Modulus values between porcine and ovine myocardial tissue for cyclic (a) and preloading (b) conditions.

Heart # Cyclic (MPa) Preload (MPa) Standard (MPa)

Ovine Porcine Ovine Porcine Ovine Porcine

1 0.047 0.064 0.057 0.081 0.068 0.095

2 0.038 0.05 0.035 0.062 0.068 0.03

3 0.036 0.063 0.072 0.13 0.054 0.045

4 0.068 0.055 0.066 0.068 0.103 0.038

5 0.045 0.085 0.047 0.091 0.052 0.037

Average 0.047 0.063 0.055 0.087 0.069 0.049

SD 0.023 0.026 0.03 0.05 0.04 0.04

2.4 Discussion

The overall goal of this study is to quantify the static mechanical and dynamic properties of myocardial tissue with methodologies that allow for standardization of testing in future works. This standardization is important for the correct interpretation of myocardial tissue response to varying test conditions. It has been previously noted that experimental testing is the best way to study the physiological parameters of biological tissue (Stemper et al., 2007). Currently, however, there is only limited work related to the standardized testing of biological tissues and a particular lack of experimental testing for myocardial tissue. This leads to gaps in knowledge about this important heart tissue (Zhu et al., 2014).

The dynamic mechanical analysis demonstrates that myocardial tissue stiffness increases linearly with an increase in frequency in the physiological range of the heart. This stiffening is coupled with an increase in the elasticity (storage modulus, E´) and heat dissipation (loss modulus, E´´) of the tissue. Interestingly, the material’s energy efficiency, (tan delta) hovers around 0.175 which is in the range of soft rubbers such as PDMS (Geethamma et al., 2005; Miller et al., 2016). These are useful parameters for researchers who wish to develop an analogue material to myocardial tissue. Moreover, these results are of physiological relevance, as they allow for a deeper understanding of myocardial tissue’s response to various heart rates. The ability to detect linear stiffening of several 100N/m over the frequency range is of particular importance. It has been shown that abnormally high myocardial stiffness is indicative of pathology, including diabetes and chronic pressure overload (Phan et al., 2012). However, these effects are not well understood, and DMA provides the potential to test both healthy and diseased human hearts to better quantify and understand myocardium behaviors.

One of the major benefits of utilizing DMA for biological tissue testing is the ability to simultaneously calculate varying types of dynamic mechanical data. This includes, but is not limited to, the following properties: stiffness; static and dynamic forces; dynamic and complex compliances; and complex, storage, and loss moduli. Existing mechanical testing technology can also operate with frequency, stress, or temperature sweeps to simulate a variety of complex physiological parameters under in-vitro conditions. DMA is also a nondestructive testing methodology, as long as the test is maintained in the elastic region and does not induce any plastic or permanent deformation. This allows for multiple tests of each individual sample, further

36 reducing testing variations. In addition, as shown in Figures 2.4, 2.5, and 2.6 the results are accurate and reproducible; this helps to eliminate some uncertainty when testing soft biological tissues.

The flexibility of DMA to easily change various testing parameters as well as its high level of accuracy make it more robust than other methodologies such as atomic force microscopy (AFM). Results from AFM can be affected by the interaction between the type of indenter utilized and the stiffness of the tested biological tissue. AFM also tests on the microscale and does not provide the same bulk mechanical properties analysis that DMA gives. Due to DMA’s widespread use in polymer testing, comparisons between biological tissues and their analogue materials can be readily performed. If AFM testing is utilized results would require significant interpretation. It is the authors’ recommendation that further work on soft biological tissues be performed utilizing this type of apparatus building on the work of Zhu et al (2007).

The tensile testing provides important insights into the effects of preconditioning on fibrous myocardial tissue. Although definitive conclusions regarding cyclic and preloading conditioning are difficult to determine, there is a reduction in the standard deviation of measured values. Following cyclic preloading, a reduction is observed of 0.014/0.017 MPa (porcine/ovine) as compared to the standard loading method (Table 2.2). Moreover, if no preloading is performed no significant difference in Young’s modulus is noted between the ovine and porcine myocardium. These observations demonstrate the significant impact that preconditioning can have on the testing of soft biological tissue. However, further studies are required to optimize the cyclic loading conditions, such as the number of preloading cycles and percent strain load for the various types of soft biological tissue. Cheng et al (2009) demonstrated that varying the pre-test strain has a profound impact on the results. Having shown the effectiveness of cyclic loading for myocardial tissue in this report, the authors recommend that future work should aim to understand the optimal testing conditions to allow for the further standardization of results. If future work is performed a limitation of the tensile testing should be addressed. Samples were placed in a saline solution prior to testing, but the testing chamber itself was not submerged. A lack of saline submersion can significantly affect the recorded modulus as the myocardial tissue dries and stiffens considerably over time. In this study these effects were minimized by performing the testing in less than five minutes for each sample. However, improvements of this methodology could provide an increased reduction in the standard deviation of measured values.

In this study, the range of myocardial tissue modulus values varies from 0.02MPa to 0.2MPa. Although this seems to be a large range of values, the range is infact narrower than currently reported values of human myocardial tissue which varies from 0.02MPa to 0.5MPa (Nagueh et al., 2004; Nakano et al., 1990; Watanabe et al., 2006). This discrepancy primarily stems from the difference between the current study’s experimental testing and the mathematical relationships studied in literature. As a consequence of these results, one of the most commonly utilized myocardial tissue analogues, Poly(Glycerol-Sebacate) (PGS), is found to have moduli values varying from 0.04 – 1.2 MPa (Rai et al., 2012). PGS has been created with a significantly larger range of moduli values to accommodate the variation of values in literature. Because of this, future work is needed that will focus the development of materials such as PGS to the mechanical properties of specific areas or regions of the heart.

To achieve this goal, this work aims to standardize results of myocardial tissue analysis through testing equivalent sized samples taken along the longitudinal axis and from the mid wall of the left ventricle. Ultrasound testing by Lee et al (2012) has shown that the majority of myocardial fibers are oriented along this axis in the mid-wall. However, it should be noted that there are fibers oriented at various other angles to promote the physiological squeezing mechanism of the heart. Testing along only the longitudinal axis limits the variation between tests and attempts to standardize results. Moreover, if the conclusions of this study are utilized to develop a potential myocardial analogue material, the “maximum” tensile modulus along the direction that contains the most fibers would be an accurate target point. This being said, there is still significant scientific value in understanding the properties of myocardial tissue in multiple directionalities. Due to the anisotropic nature of heart tissue, the specific fiber orientation and number of fibers per sample is unknown, and could therefore be a source of further work. Focused ultrasound testing performed on each individual sample prior to testing could provide a mathematical relationship between the amount and direction/angles of fibers, the matrix, and the experimentally determined bulk modulus. This could help to explain the wide variety of values that are observed both in this work and the published literature and further help to develop future materials that aim to mimic specific regions of the heart. No comparison can be done on the dynamic mechanical analysis as to the author’s knowledge and as noted by, Y Zhu et al (2014) and Holzapfel et al (2009), there is a considerable lack of viscoelastic data for myocardial tissue in current literature.

2.5 Conclusion

In summary this work aims to quantify the mechanical properties of myocardial tissue in porcine and ovine tissue while outlining methodologies that can help to standardize soft biological tissue testing. Both the dynamic mechanical analysis and tensile testing methodologies were performed on five porcine and five ovine hearts. The tensile testing investigates the effect on observed results of three preconditiong settings: a standard no preload, a 0.05N static force preload and a cyclic preload. All of the testing is performed at 37°C and the dynamic mechanical analysis is swept at frequencies from 0.5 to 3.5Hz.

From the testing the storage modulus (E´), loss modulus (E´´) and stiffness follow a linear increase in relation to frequency. Additionally, tan delta values are approximately 0.175, demonstrating the materials energy efficiency. Moreover, the tensile testing modulus values range from 0.02 to 0.2 MPa, which is considerably lower than those found in current literature. In addition, preconditioning and precycling improve the accuracy of the results and demonstrate the importance of pre-test conditions when standardizing biological tissue testing. These results provide a platform for further work to be undertaken in standardized tensile testing with a wider utilization of dynamic mechanical analysis for soft biological tissues testing.

2.6 References

Abramowitch, S.D., Zhang, X., Curran, M. and Kilger, R., 2010. A comparison of the quasi-static mechanical and non-linear viscoelastic properties of the human semitendinosus and gracilis tendons. Clinical Biomechanics, 25(4), pp.325-331.

Bartkowiak-Jowsa, M., Będziński, R., Kozłowska, A., Filipiak, J. and Pezowicz, C., 2013. Mechanical, rheological, fatigue, and degradation behavior of PLLA, PGLA and PDGLA as materials for vascular implants. Meccanica, 48(3), pp.721-731.

Carew, E.O., Barber, J.E. and Vesely, I., 2000. Role of preconditioning and recovery time in repeated testing of aortic valve tissues: validation through quasilinear viscoelastic theory. Annals of biomedical engineering, 28(9), pp.1093-1100.

Carew, E.O., Garg, A., Barber, J.E. and Vesely, I., 2004. Stress relaxation preconditioning of porcine aortic valves. Annals of biomedical engineering, 32(4), pp.563-572.

Chen, Q.Z., Bismarck, A., Hansen, U., Junaid, S., Tran, M.Q., Harding, S.E., Ali, N.N. and Boccaccini, A.R., 2008. Characterisation of a soft elastomer poly (glycerol sebacate) designed to match the mechanical properties of myocardial tissue. Biomaterials, 29(1), pp.47-57.

Chen, E.J., Novakofski, J., Jenkins, W.K. and O'Brien, W.D., 1996. Young's modulus measurements of soft tissues with application to elasticity imaging. IEEE Transactions on ultrasonics, ferroelectrics, and frequency control, 43(1), pp.191-194.

Cheng, S., Clarke, E.C. and Bilston, L.E., 2009. The effects of preconditioning strain on measured tissue properties. Journal of biomechanics, 42(9), pp.1360-1362.

Chow, M.J. and Zhang, Y., 2011. Changes in the mechanical and biochemical properties of aortic tissue due to cold storage. Journal of Surgical Research, 171(2), pp.434-442.

Claes, E., Atienza, J.M., Guinea, G.V., Rojo, F.J., Bernal, J.M., Revuelta, J.M. and Elices, M., 2010, August. Mechanical properties of human coronary arteries. In Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE (pp. 3792-3795). IEEE.

Claridge, M.W., Bate, G.R., Hoskins, P.R., Adam, D.J., Bradbury, A.W. and Wilmink, A.B., 2009. Measurement of arterial stiffness in subjects with vascular disease: Are vessel wall changes more sensitive than increase in intima–media thickness?. Atherosclerosis, 205(2), pp.477-480.

Fung, Y.C., 2013. Biomechanics: mechanical properties of living tissues. Springer Science & Business Media.

Geethamma, V.G., Kalaprasad, G., Groeninckx, G. and Thomas, S., 2005. Dynamic mechanical behavior of short coir fiber reinforced natural rubber composites. Composites part A: applied science and manufacturing, 36(11), pp.1499-1506.

Gupta, K.B., Ratcliffe, M.B., Fallert, M.A., Edmunds, L.H. and Bogen, D.K., 1994. Changes in passive mechanical stiffness of myocardial tissue with aneurysm formation. Circulation, 89(5), pp.2315-2326.

Holzapfel, G.A. and Ogden, R.W., 2009. Constitutive modelling of passive myocardium: a structurally based framework for material characterization. Philosophical Transactions of the

Royal Society of London A: Mathematical, Physical and Engineering Sciences, 367(1902), pp.3445-3475.

Karimi, A., Navidbakhsh, M., Shojaei, A. and Faghihi, S., 2013. Measurement of the uniaxial mechanical properties of healthy and atherosclerotic human coronary arteries. Materials Science and Engineering: C, 33(5), pp.2550-2554.

Kofidis, T., Akhyari, P., Boublik, J., Theodorou, P., Martin, U., Ruhparwar, A., Fischer, S., Eschenhagen, T., Kubis, H.P., Kraft, T. and Leyh, R., 2002. In vitro engineering of heart muscle: artificial myocardial tissue. The Journal of Thoracic and Cardiovascular Surgery, 124(1), pp.63- 69.

Lang, N., Pereira, M.J., Lee, Y., Friehs, I., Vasilyev, N.V., Feins, E.N., Ablasser, K., O’Cearbhaill, E.D., Xu, C., Fabozzo, A. and Padera, R., 2014. A blood-resistant surgical glue for minimally invasive repair of vessels and heart defects. Science translational medicine, 6(218), pp.218ra6- 218ra6.

Lee, W.N., Pernot, M., Couade, M., Messas, E., Bruneval, P., Bel, A., Hagege, A.A., Fink, M. and Tanter, M., 2012. Mapping myocardial fiber orientation using echocardiography-based shear wave imaging. Medical Imaging, IEEE Transactions on, 31(3), pp.554-562.

Li, Y., Cook, W.D., Moorhoff, C., Huang, W.C. and Chen, Q.Z., 2013. Synthesis, characterization and properties of biocompatible poly (glycerol sebacate) pre‐polymer and gel. Polymer International, 62(4), pp.534-547.

Mano, J.F., Reis, R.L. and Cunha, A.M., 2002. Dynamic mechanical analysis in polymers for medical applications. In Polymer based systems on tissue engineering, replacement and regeneration (pp. 139-164). Springer Netherlands.

Miller, A.T., Safranski, D.L., Smith, K.E., Guldberg, R.E. and Gall, K., 2016. Compressive cyclic ratcheting and fatigue of synthetic, soft biomedical polymers in solution. Journal of the mechanical behavior of biomedical materials, 54, pp.268-282.

Nagueh, S.F., Shah, G., Wu, Y., Torre-Amione, G., King, N.M., Lahmers, S., Witt, C.C., Becker, K., Labeit, S. and Granzier, H.L., 2004. Altered titin expression, myocardial stiffness, and left ventricular function in patients with dilated cardiomyopathy. Circulation, 110(2), pp.155-162.

Nakano, K., Sugawara, M., Ishihara, K., Kanazawa, S., Corin, W.J., Denslow, S., Biederman, R.W. and Carabello, B.A., 1990. Myocardial stiffness derived from end-systolic wall stress and logarithm of reciprocal of wall thickness. Contractility index independent of ventricular size. Circulation, 82(4), pp.1352-1361.

Phan, T.T., Shivu, G.N., Abozguia, K., Sanderson, J.E. and Frenneaux, M., 2012. The pathophysiology of heart failure with preserved ejection fraction: from molecular mechanisms to exercise haemodynamics. International journal of cardiology, 158(3), pp.337-343.

Pinto, J.G. and Patitucci, P.J., 1980. Visco-elasticity of passive cardiac muscle. Journal of biomechanical engineering, 102(1), pp.57-61.

Rai, R., Tallawi, M., Grigore, A. and Boccaccini, A.R., 2012. Synthesis, properties and biomedical applications of poly (glycerol sebacate)(PGS): a review. Progress in polymer science, 37(8), pp.1051-1078.

Sacks, M.S., 1999. A method for planar biaxial mechanical testing that includes in-plane shear. Journal of biomechanical engineering, 121(5), pp.551-555.

Sommer, G. and Holzapfel, G.A., 2012. 3D constitutive modeling of the biaxial mechanical response of intact and layer-dissected human carotid arteries. Journal of the Mechanical Behavior of Biomedical Materials, 5(1), pp.116-128.

Song, B., Chen, W., Ge, Y. and Weerasooriya, T., 2007. Dynamic and quasi-static compressive response of porcine muscle. Journal of Biomechanics, 40(13), pp.2999-3005.

Stemper, B.D., Yoganandan, N., Stineman, M.R., Gennarelli, T.A., Baisden, J.L. and Pintar, F.A., 2007. Mechanics of fresh, refrigerated, and frozen arterial tissue. Journal of Surgical Research, 139(2), pp.236-242.

Stolz, M., Raiteri, R., Daniels, A.U., VanLandingham, M.R., Baschong, W. and Aebi, U., 2004. Dynamic elastic modulus of porcine articular cartilage determined at two different levels of tissue

42 organization by indentation-type atomic force microscopy. Biophysical journal, 86(5), pp.3269- 3283.

Sverdlik, A. and Lanir, Y., 2002. Time-dependent mechanical behavior of sheep digital tendons, including the effects of preconditioning. Journal of biomechanical engineering, 124(1), pp.78-84.

Troyer, K.L., Estep, D.J. and Puttlitz, C.M., 2012. Viscoelastic effects during loading play an integral role in soft tissue mechanics. Acta biomaterialia, 8(1), pp.234-243.

Valtorta, D. and Mazza, E., 2004. Dynamic measurements of soft tissue viscoelastic properties with a torsional resonator device. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2004 (pp. 284-292). Springer Berlin Heidelberg.

Watanabe, S., Shite, J., Takaoka, H., Shinke, T., Imuro, Y., Ozawa, T., Otake, H., Matsumoto, D., Ogasawara, D., Paredes, O.L. and Yokoyama, M., 2006. Myocardial stiffness is an important determinant of the plasma brain natriuretic peptide concentration in patients with both diastolic and systolic heart failure. European heart journal, 27(7), pp.832-838.

Woo, S.L., 1982. Mechanical properties of tendons and ligaments. I. Quasi-static and nonlinear viscoelastic properties. Biorheology, 19(3), p.385.

Yeh, W.C., Li, P.C., Jeng, Y.M., Hsu, H.C., Kuo, P.L., Li, M.L., Yang, P.M. and Lee, P.H., 2002. Elastic modulus measurements of human liver and correlation with pathology. Ultrasound in medicine & biology, 28(4), pp.467-474.

Zhu, H., Bhatia, S. and Chauhan, A., 2007. Dynamic mechanical properties of porcine lacrimal canaliculus. Current eye research, 32(10), pp.829-835.

Zhu, Y., Luo, X.Y., Gao, H., McComb, C. and Berry, C., 2014. A numerical study of a heart phantom model. International Journal of Computer Mathematics, 91(7), pp.1535-15

2.7 Appendix A Table A.1 –DMA raw sample thickness and width measurements for both Ovine and Porcine hearts (all measurements in mm). Ovine H1 t w Porcine H1 t w 1 4.0166 9.35 1 4.5537 8.375 2 4.2739 8 2 3.694 8.915 3 3.329 7.86 3 3.5531 8.42 4 4.477 9.32 4 4.549 8.39 5 3.6223 7.99 5 3.935 8.15 Ovine H2 Porcine H2 1 3.902 8.15 1 4.7455 8.305 2 3.4336 7.53 2 3.9187 8.55 3 3.7278 9.55 3 3.5643 8.04 4 4.053 9.24 4 3.8134 8.365 5 4.2954 7.9 5 4.9864 8.05 Ovine H3 Porcine H3 1 3.6516 7.95 1 4.6035 8.935 2 3.994 8.05 2 4.4694 8.595 3 3.445 7.5 3 4.6839 8.23 4 4.034 6.75 4 4.6727 8.96 5 4.243 7 5 3.9769 8.133 Ovine H4 Porcine H4 1 4.243 8.5 1 4.5138 7.87 2 3.5826 8 2 3.8957 7.99 3 3.3106 8.4 3 3.8915 7.9 4 3.7154 7.75 4 4.0214 8.26 5 3.7858 7.6 5 3.9761 7.93 Ovine H5 Porcine H5 1 4.3049 8.9 1 4.0139 8.035 2 3.9434 8.34 2 4.2203 8.35 3 4.5448 8.3 3 4.759 8.32 4 4.208 8.75 4 5.143 8.955 5 4.3392 8.6 5 4.4215 8.5 AVG 3.94 8.21 4.26 8.34 SD 0.36 0.71 0.45 0.33

Table A.2 – Complete Set of tensile data for both Ovine and Porcine hearts at varying preconditioning settings.

Cyclic Preload Standard H# Ovine H Porcin H Ovine H Porcine H# Ovine H Porcin # e # # # e 1 0.065 1 0.077 1 0.030 1 0.118 1 0.070 1 0.096 0.077 0.079 0.035 0.051 0.096 0.037 0.039 0.097 0.060 0.057 0.066 0.166 0.027 0.039 0.028 0.103 0.042 0.037 0.026 0.027 0.132 0.076 0.138 2 0.038 2 0.062 2 0.028 2 0.048 2 0.104 2 0.026 0.043 0.029 0.025 0.031 0.060 0.023 0.041 0.069 0.042 0.024 0.033 0.034 0.028 0.038 0.040 0.039 0.074 0.036 0.039 0.039 0.170 3 0.019 3 0.060 3 0.053 3 0.152 3 0.036 3 0.022 0.041 0.030 0.034 0.198 0.035 0.055 0.036 0.107 0.129 0.148 0.029 0.012 0.059 0.065 0.118 0.118 0.092 0.024 0.056 0.035 4 0.103 4 0.046 4 0.073 4 0.039 4 0.192 4 0.034 0.106 0.041 0.059 0.068 0.082 0.044 0.038 0.049 0.103 0.048 0.123 0.037 0.055 0.059 0.040 0.061 0.083 0.040 0.080 0.055 0.126 0.037 5 0.071 5 0.133 5 0.047 5 0.156 5 0.045 5 0.022 0.028 0.079 0.037 0.066 0.083 0.025 0.026 0.052 0.056 0.114 0.052 0.034 0.061 0.092 0.048 0.050 0.030 0.025 0.037 0.069 0.049 0.072 0.080 AVG 0.047 0.064 0.054 0.087 0.071 0.051 STD 0.023 0.026 0.030 0.050 0.040 0.041

Chapter 3

Development of a myocardial and soft biological tissue analogue

In this work a synthetic myocardial tissue analogue material was developed. When working towards this biological tissue analogue several properties were considered. First the material must possess the same static tensile and dynamic (viscoelastic) mechanical properties of myocardial tissues. Secondly the material should possess a CT attenuation approaching that of myocardial tissue. Finally, the material must be easily processable so that it can be casted into various geometries. To achieve these parameters a low molecular weight poly(vinyl) chloride material was selected as the base thermoplastic. A plasticizer, dioctyl phthalate, was then added to weaken the matrix structure to obtain values closer to that of myocardial tissue. These parts were selected as their combined density and molecular weight approached the expected CT attenuation properties for that of myocardial tissue. This material is then manufactured through a heating process which bonds the two parts and then liquefies them to be poured into a mold. As the material is a thermoplastic this heating to solid cycle does not damage the properties. The material was made with varying compositions of Plasticizer: PVC to observe the effects on the mechanical and CT properties. Tensile testing and dynamic mechanical analysis then validated that the 80:20 weight % ratio closely matched the young’s modulus, storage/loss modulus, and tan delta of myocardial tissue. A CT scan was then performed which confirmed the correct CT attenuation. In summary, a simple to manufacture material was developed which mimics the properties of myocardial tissue.

The content of this chapter has been submitted to the Journal of Biomedical Materials Research Part A.

3 DEHP- Polyvinyl Chloride synthetic analogues 3.1 Introduction

There is increasing interest in characterizing the mechanical properties of soft biological tissues. This work includes both modelling and experimental studies on; coronary arteries (Karimi et al 2013), myocardial tissue (Ramadan et al 2017) and non-load bearing structures such as the brain, liver and lung (Miller et al 2000; Mendis et al 1995).

Understanding the static and viscoelastic properties of these materials allows for greater insight into the structure and function of various tissues. This work also invites the opportunity to research and develop analogue biomedical materials that are subsequently utilized for a wide variety of applications throughout biomedical engineering (Wang et al, 2002; Lang et al 2014; Rai et al 2010).

However, despite the clear benefits of biomaterials they can be challenging to manufacture. Poly Glycerol Sebacate (PGS), a promising myocardial tissue analogue with a wide array of uses, is a key example of this. Previous work by (Li et al 2003; Engelmayr et al 2008; and Chen et al 2008) has demonstrated that fabrication of reproducible PGS samples with consistent mechanical/geometrical properties is difficult and subject to varying factors.

Another type of commonly used biomedical material is silicone (El-Naggar 2014). Silicone analogues are simple to manufacture yet lack the flexibility required of a soft tissue analogue. Moreover, they do not mimic the Computed Tomography (CT) attenuation of biological tissues due to the large atomic weight of silicone. This makes silicone unusable for imaging research of muscles (Palchesko et al 2012).

There are many applications for a simple biomedical material that can act as a soft biological tissue analogue for example in robotics and haptic feedback devices. These applications require materials that mimic the static elastic and dynamic viscoelastic properties of soft biological tissues, such as myocardial tissue, for accurate surgical training procedures (Miller et al 2000; Brett et al 1995; Burdea et al 1996). Medical phantoms also require tissues that precisely mimic the structure and function of various organs including the liver, brain, lungs, and heart. These phantoms are prominent in cardiovascular imaging research where complex mechanical

47 pumping systems are combined with 3D heart phantom models to optimize imaging parameters for CT systems (Ursani et al 2015.). An effective heart phantom must mimic the mechanical, geometrical, and CT properties of the heart (Boltz et al 2010)). However, a material that can mimic these properties while also being easily mouldable in the shape of a heart is not known to the authors.

This work aims to create a simple highly modifiable material that will act primarily as a myocardial tissue analogue with potential for broader application as a soft biological tissue analogue. First (1) the material is desired to be simple in manufacturing to allow for hospital and healthcare institutions to easily fabricate the material without the need for a complicated laboratory set up. Next (2) the material is desired to have good processability to allow for molding into various shapes to mimic biological structures. Then (3) the processing/composition of the material should be modifiable to achieve a satisfactory range of mechanical and CT properties. This work also aims (4) to provide a platform of optimizable equations to allow researchers to easily tailor the material to their application and need.

To achieve this, a polyvinyl chloride plasticizer blend material is investigated. These materials have been previously utilized as elastomers for biomedical applications because of their wide range of mechanical properties (Yoda et al 1998). They have also seen use in various non biomedical fields such as the automotive, aerospace, and food packaging industries (Murphy et al, 2001; Haim 1995; Gachter et al, 1990; Krauskopf, 1988).

In current research Bis(2-ethylhexyl) phthalate (DEHP) plasticizers are recognized to be inexpensive with excellent compatibility to PVC. They also impart flexibility when added to polyvinyl chloride (Mustafizur et al 2004). In this work a DEHP–PVC material is investigated as a myocardial and soft biological tissue analogue through targeted testing and analysis of its dynamic viscoelastic, static tensile, and CT attenuation properties. The Young’s modulus and viscoelastic properties (Storage and Loss modulus, Tan Delta, Stiffness) are functions of the stress-strain and oscillating stress-strain results respectively. Both properties are fundamental in understanding the mechanical behavior of a material (Forsell et al 2013, Flugge et al 2013; Hosseini et al 2014). Moreover, the CT attenuation is primarily a function of a material’s linear attenuation, namely the fraction of an x-ray that is scattered or absorbed. This can be related to material characteristics through the electron density of a material. More broadly, this can be

48 found through a combination of the density and atomic weight of the scanned material. (Watanabe et al 1999; Bai et al 2003).

To achieve the final goal of this study the percentage DEHP content will be utilized as an optimization parameter. This will allow for the varying level of mechanical properties required for soft muscle and biological tissues to be achieved (Puskas, J.E,2003).

3.2 Methodology

3.2.1 Sample Preparation

푔 푔 Both DEHP ≥99.5% Bis(2-ethylhexyl) phthalate (Density = 0.985 , 푀 = 390.56 ) and 푚푙 푤 푚표푙 푔 푔 Low Molecular Weight PVC (Density = 1.4 , 푀 = 48000 ) were purchased from Sigma 푚푙 푤 푚표푙 Aldrich. To produce the flexible DEHP-PVC hybrid all samples were fabricated following binding and melting recommendations previously outlined by Daniels et al 2009. A Corning PC 420D hot plate was utilized to simultaneously provide heating and stirring of the samples. First samples were mixed in varying parts of DEHP – PVC (75% -25%, 80% -20%, and 85% - 15%) and stirred for 5 minutes at 700 RPM. These percentages were utilized to achieve a high level of plasticization and flexibility to appropriately mimic the soft structural nature of myocardial tissue (Wypych et al 2004). After sufficient mixing the samples were heated to 110°C at 700RPM. This allowed for sufficient heat to bind the plasticizer and PVC. Finally, the DEHP-PVC mixture was heated to 150° to melt the DEHP-PVC blend and allow for the mixture to be poured into a mold.

3.2.2 Fourier Transform Infrared Spectroscopy

To study the bonding nature of the fabricated DEHP –PVC blend a Bruker Platinum Alpha ATR FTIR machine was utilized.

3.2.3 Static Tensile Testing

To perform the mechanical characterization an Instron Microtester 5848 machine was used for uniaxial tensile testing. Testing was performed following ASTM Standard D412 Standard Test Methods for Vulcanized Rubber and Thermoplastic Elastomers—Tension. All testing was 푚푚 performed at a strain rate of 5 . It is important to note that for all testing the DEHP-PVC 푚푖푛

49 blend aims to mimic myocardial tissue and its application in either surgical training or phantom scanning would be performed at room temperature. To this end all testing was performed at room temperature. Each DEHP-PVC ratio (n=3) was tested (n=5) as outlined by the corresponding ASTM standard.

3.2.4 Dynamic Mechanical Analysis

To study the viscoelastic properties of the DEHP-PVC material a TA Instruments DMA Q800 Dynamic Mechanical Analyzer was utilized. To perform the testing ASTM standard D5992 was utilized and consideration was also given to manufacturer specifications and recommendations. A submerged compression clamp was utilized as outlined by Ramadan et. al 2017 and Zhu et al 2014. This clamp induces an oscillating strain on a sample at a set frequency and measures the resulting oscillating stress. This allows for a stress-strain frequency dependant curve to be collected. From this data key viscoelastic properties such as the tan delta, storage/loss modulus, and stiffness of the tested sample can be collected. The frequency range that was utilized varied from 0.5Hz to 3.5 Hz in 0.25Hz increments to mimic physiological pumping and heart rates of 30BPM to 210BPM. Additionally, the samples were submerged in a 14:1 mixture of Water: Iodinated contrast agent (Iodixanol, 320mg/ml, GE Healthcare) to mimic the fluid conditions that the analogue would be exposed to if utilized as an imaging phantom. However, an alternate set of DMA results for samples submerged in water can be found in Appendix B.1. All (n=5) samples for the different DEHP-PVC ratios (n=3) that were tested were 8mm by 8mm by 4mm square slabs.

3.2.5 Computed Tomography

All samples were scanned in a wide volume calibrated CT (320 MDCT AquilionONE; Toshiba Medical Systems, Japan). Scanning conditions were set at a gantry rotation of 0.5 seconds with a display field of view of 32cm.

The tube voltages and current values were as follows: 135kVp/50mA, 120 kVp/75mA, 100kVp/100mA, and 80 kVp/150mA. The x-ray tube exposure indices were adjusted to ensure a fixed radiation dose of 5 mGy. The images were reconstructed at 0.5mm sections and 0.5mm intervals using a single filter kernel FC04.

3.2.6 Optimization

To increase the versatility of this material for current biomedical researchers an optimization algorithm was defined (Coello et al 2009). The optimization was based on the percentage DEHP content that was used and is the optimized parameter. Results from the tensile and CT testing as well as significant frequency ranges of 60, 75, 90, and 105 BPM were utilized for the stiffness, storage/loss, and tan delta values. From this data curves of best fit were obtained and utilized as optimization equations in a generalized reduced gradient nonlinear equation solver with a central derivative solution. A desired set of porcine values based on literature values from Ramadan et. al 2017 was utilized for the objective values. Each of the optimized parameters was weighted to the same order of magnitude to equally value all of the individual results. The optimization worked to set the difference between the porcine and best fit values to zero by varying the DEHP percentage value.

3.3 Results

3.3.1 Fourier Transform Infrared Spectroscopy

The effects of DEHP plasticizer on the PVC matrix is seen in Figure 3.1 a-c. The presence of absorbance peaks in the carbonyl and C-Cl regions demonstrate plasticizer compatibility and bonding as outlined by Gonzalez et al, 2006, Guarotxena et al, 1998, and Martinez et al, 1988. These results validate that despite the high level of plasticizer utilized in this study there is sufficient binding between the DEHP and PVC molecules. This creates the indicative carbonyl and C-Cl absorbance spectra seen in Figure 3.1.

3.3.2 Static Tensile Testing

The tensile testing results can be seen in Table 3.1 and Figures 3.2-3.4, The stress-strain curves (n=5) for each ratio can be found in Figure 3.2. Increasing the amount of DEHP plasticizer results, as expected, in significant reduction for both stress and strain prior to sample failure. Additionally, this translates to a reduction in the percent elongation from 134% to 107% to 57% of the samples (Figure 3.3) as the percentage of DEHP is increased; this is reflected in a reduction of the Young’s modulus (Figure 3.4) from 0.182MPa to 0.069 MPa to 0.02MPa. These results all follow plasticizer mechanics as the DEHP softens the PVC matrix.

Tensile strain at Tensile stress at Tensile stress at Tensile strain at

Ratio Break Break Modulus Yield Yield

%DEHP (mm/mm) (MPa) (MPa) (MPa) (mm/mm)

75 1.438 0.0057 0.174 0.175 1.263

80 1.081 0.00469 0.0686 0.0521 0.990

85 0.585 0.00112 0.0192 0.00793 0.434

DEHP-PVC - Stress/Strain 0.25

0.2

0.15

0.1 Stress Stress (MPa)

0.05

0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Strain (mm/mm)

DEHP-PVC - % Elongation 180.0 160.0 135.6 140.0 120.0 107.2 75% DEHP 100.0 80% DEHP 80.0 57.0 85% DEHP

% Elongation % 60.0 40.0 20.0 0.0

DEHP-PVC- Young's Modulus

0.200 0.182 0.180 0.160 0.140 0.120 75% DEHP 0.100 80% DEHP 0.080 0.069 85% DEHP 0.060 0.040 Young's Young's Modulus(MPa) 0.019 0.020 0.000

3.3.3 Dynamic Mechanical Analysis

The dynamic mechanical analysis of the DEHP-PVC material can be seen in Figures 3.5-3.7. The storage/loss modulus, stiffness, and tan delta all increase with frequency. This is expected as the increase in strain frequency results in a larger oscillating stress. The storage/loss moduli increase by an order of magnitude based on the 10% change in DEHP plasticizer content. Moreover, this trend is also found in the stiffness of the material and this demonstrates the softening affect of the plasticizer. The tan delta however, is quite similar between the three ratios as the storage/loss moduli shifts by the same relative amount between the samples.

a) DEHP-PVC - Storage Modulus 0.2

0.15

0.1

0.05

0 0 0.5 1 1.5 2 2.5 3 3.5 4

Storage Storage Modulus(MPa) Frequency (Hz)

75% DEHP 80% DEHP 85% DEHP

b) DEHP-PVC - Loss Modulus 0.025 0.02 0.015 0.01 0.005 0 Loss Loss Modulus(MPa) 0 0.5 1 1.5 2 2.5 3 3.5 4 Frequency (Hz)

75% DEHP 80% DEHP 85% DEHP

DEHP-PVC - Stiffness 2000 1500 1000 500

Stiffness Stiffness (N/m)) 0 0 0.5 1 1.5 2 2.5 3 3.5 4 Frequency (Hz)

75% DEHP 80% DEHP 85% DEHP

DEHP-PVC - Tan Delta

0.3

0.2

TanDelta 0.1

0 0 0.5 1 1.5 2 2.5 3 3.5 4 Frequency (Hz)

75% DEHP 80% DEHP 85% DEHP

3.3.4 Computed Tomography

Computed Tomography (CT) scanning was performed to ensure that the synthesized material can be utilized as a myocardial/soft tissue analogue for computed tomography imaging applications. This can be seen in Figure 3.8. It is important to remember that chlorine has a significantly higher molecular weight than the DEHP plasticizer; this is relevant as CT attenuation increases primarily by density and molecular weight. This leads to an increase in the observed CT attenuation as the percentage of plasticizer used is reduced (increasing PVC content). Ursani et al 2015 found that for soft biological tissues (i.e. muscle) the CT attenuation is approximately 60 HU units, which corresponds to the 50-70HU attenuation values found for the DEHP-PVC material.

DEHP-PVC - CT Attenuation 200

150

100

50 CT CT Attenuation(HU) 0 80 kVp 100 kVp 120 kVp 135 kVp Tube Voltage (kVp)

75% DEHP 80% DEHP 85% DEHP

Figure 3.8: Computed Tomography scans at tube voltages and currents of 135kVp/50mA, 120 kVp/75mA, 100kVp/100mA, 80 kVp/150mA were performed on each of the three DEHP-PVC ratios.

3.3.5 Optimization Parameters

The results from Figure 3.9 are used to create an optimization algorithm to complete Table 3.2. It can be seen that the optimized parameter, as compared to literature porcine values from Ramadan et al 2017, was a DEHP percentage ratio of 80.17%. The optimization findings indicate that the 80% ratio results in a mechanical analogue that is strongly related to myocardial tissue. The range of results also demonstrate that with small adjustments the DEHP-PVC material could be utilized more broadly as a soft biological tissue analogue. The provided curves of best fits allow for certain parameters to be prioritized or omitted in a weighted function to suit research needs in mimicking a wide range of soft biological tissues including the heart, lung and liver.

CT Attenuation Tensile Modulus 100 0.2 80 0.15 60 0.1 40

20 0.05 Modulus(MPa) CT CT Number (HU) 0 0 75% 80% 85% 75% 80% 85% % DEHP % DEHP

Storage Modulus Stiffness (60 - 105 BPM) (60 - 105 BPM) 2000 0.14 0.12 1500 0.1 0.08 1000 0.06

0.04 500 Stiffness Stiffness (MPa) 0.02

Storage Storage Modulus(MPa) 0 0 75% 80% 85% 75% 80% 85% % DEHP % DEHP

Loss Modulus (60 - 105 BPM) 0.016 0.014 0.012 0.01 0.008 0.006 0.004 0.002 Loss Loss Modulus(MPa) 0 75% 80% 85% % % DEHP

Optimization – At 0.8017 DEHP-PVC Porcine Equation Objective Values 75% 80% 85% 2 CT Attenuation - 120 kVp y = 5660x - 9403x + 3954.9 60 54.32 5.68 DMA - 60 BPM Storage y = 11.067x2 - 18.779x + 7.9728 0.0222 0.0307 -0.0084 Loss y = 1.0065x2 - 1.7196x + 0.7351 0.0037 0.0034 0.0003 2 Stiffness y = 92600x - 161700x + 70674 452.412 554.8366 -102.4246 75BPM Storage y = 11.217x2 - 19.031x + 8.0783 0.0233 0.0305 -0.0072 Loss y = 1.0333x2 - 1.7704x + 0.759 0.0039 0.0038 0.0001 2 Stiffness y = 94340x - 164652x + 71922 474.908 554.5510 -79.6430 90 BPM Storage y = 11.396x2 - 19.333x + 8.2056 0.0243 0.0308 -0.0065 Loss y = 1.0734x2 - 1.8446x + 0.7931 0.0041 0.0042 -0.0001 Stiffness y = 96140x2 - 167732x + 73239 495.724 559.2101 -63.4861

105 BPM Storage y = 11.509x2 - 19.525x + 8.2869 0.0254 0.0308 -0.0054 Loss y = 1.095x2 - 1.8852x + 0.812 0.0042 0.0044 -0.0002 Stiffness y = 97140x2 - 169473x + 73996 516.96 563.1690 -46.2090 Tensile Modulus (MPa) y = 12.904x2 - 22.278x + 9.6325 0.0640 0.0659 -0.0019

3.4 Discussion

This work demonstrates the versatility and simple manufacturing of a DEHP-PVC soft biological tissue analogue material. In this work DEHP content of 75%, 80%, and 85% is chosen to create a sufficiently soft material that will allow for replication of soft biological tissues.

Due to its simplicity, this material exhibits consistent mechanical testing results. This is apparent through the static tensile testing and dynamic mechanical analysis. From the tensile testing the Young’s modulus varies from 0.02MPa to 0.182MPa as seen in Figure 3.4. However, this variation comes at the cost of a reduction in percent elongation from 135 % to 57% (Figure 3.3). This wide range occurs because increasing the quantity of plasticizer softens and elongates the matrix. This results in a weaker material that requires less force to deform as seen from the stress-strain curves found in Figure 3.2. Moreover, this phenomenon is further represented by a reduction in the viscoelastic properties by an order of magnitude from the 75% DEHP to 85% DEHP ratios. This dramatic change in mechanical properties from a 10% change in plasticizer content demonstrates the impact of DEHP content in softening the matrix.

In terms of the dynamic mechanical analysis several key parameters are seen. The storage and loss modulus of the DEHP - PVC compound varies by an order of magnitude at all ratios (Figure 3.5). The obtained storage modulus for the three ratios varies from 0.007 to 0.125 MPa, whereas the loss modulus varies from 0.0005 to 0.018MPa. Moreover, there is a linear stiffening of the material as frequency increases for all DEHP-PVC ratios from 96 to 1529 N/m (Figure 3.6). This is to be expected as increasing the frequency of force results in less time for stress relaxation and recovery of the material. The linear increase of loss modulus with a relatively small change in storage modulus results in a reduction of the material’s energy efficiency. Figure 3.7 demonstrates this change through an increase in the tan delta at higher frequencies as more energy is dissipated. The tan delta varies from 0.085 to 0.27 for the three ratios.

The CT results follow a similar pattern albeit based on a different material phenomenon. The 푔 PVC material has a molecular weight of 48000 whereas the plasticizer is much lower at 푚표푙 푔 390.56 . Due to this difference, increasing the amount of DEHP results in a lower average 푚표푙 molecular weight for the DEHP-PVC compound. Consequently, this results in a decrease in

62 measured CT attenuation from 51.7(75% DEHP) to 86.4 HU (85% DEHP) at the most commonly clinically used x-ray tube voltage setting of 120 kVp. This difference is also seen at the 80, 100, and 135 kVp settings tested.

These mechanical and CT results are to be expected based on the mechanism of DEHP plasticization. This involves the softening of the PVC matrix through an increase in the distance between molecules (Edenbaum, 1992). Generally, this softening effect is limited by utilizing a maximum of 50%-50% DEHP-PVC ratio in common applications. (Yoda et al, 1998). This is due to the inherent matrix instability introduced by the interaction between the plasticizer and the PVC molecules. This instability can cause an increased possibility of heat aging, yellowing of the material, leeching of the plasticizer and weakening of the structure. However, due to the unique nature of soft biological tissues, it is actually beneficial to exceed the 50-50% threshold of proportions. The desired applications require lower than the typical mechanical values needed in thermoplastics. Additionally, the stability of the matrix is addressed through the FTIR testing performed in Figure 3.1 which finds the presence of critical bonds required for any PVC- plasticizer matrix. There are also many benefits of using DEHP due to this mechanism of plasticization. Because non polar DEHP preferentially targets the polymer matrix, while not affecting ionic interactions, (Wypych et al, 2004) the proposed DEHP-PVC material can be easily modified with a variety of additives. These additives can range from fillers, curing agents, accelerators, fibers, stabilizers and pigments. The additives listed can be utilized to modify a wide variety of processing, thermo, mechanical, electrical, and chemical properties (Lambert et al 1991).

In order to compare to current literature, the mechanical (static and viscoelastic) properties as well as the CT attenuation must be considered. Within the current literature, the commonly quoted values for the tensile properties of myocardial tissue are 0.02 – 0.5 MPa (Watanabe et al 2006; Riley et al, 1992; Ghista et al 1975). More recently, Ramadan et al 2017 have shown a lower range of values from 0.02 - 0.2 MPa. These values correlate with the range of DEHP-PVC values of 0.02 to 0.18 MPa found in Figure 3.4. Additionally, this lower range of values allows for replication of the elasticity of many soft biological tissues within the modulus range of 20kPa to 100kPa. However, due to matrix instability when plasticizer content is increased beyond 85% DEHP the proposed DEHP-PVC material would be unable to mimic tissues in the range of 1 to

20 kPa (Engler et al 2006; Chowdhury et al 2010; Fioretta et al 2012; Shebanova et al; Charest et al 2012; Jiang et al 2008).

In terms of the viscoelastic properties, the DEHP-PVC matrix demonstrates a significant similarity to the porcine and ovine tissue testing performed previously by Ramadan et al 2017; which demonstrated that the range of myocardial tissue values linearly increased with frequency across a 0.5Hz to 3.5Hz testing region. The storage modulus varied from 0.02 to 0.04 MPa, the loss modulus varied from 0.003 to 0.008 MPa, the stiffness varied from 400 to 800 N/m, and the Tan Delta stayed constant at 0.175. These values are all within the range of the current findings for the DEHP-PVC material utilized. Specifically, the correlation between values is readily apparent in the 80% DEHP ratio (Figures 3.5-3.7). The largest source of deviation from Ramadan et al 2017 is found in the tan delta properties at low frequencies (30 to 60 BPM). In the current study the DEHP-PVC ratio is found to have an increasing tan delta whereas the myocardial tissue stays constant at increasing frequency. However, at the higher frequencies that are more common in healthy physiology, the results overlap significantly. Although the DMA results in Figures 3.5,3.6,3.7 demonstrate a remarkable similarity to myocardial tissue, there is currently no comparable set of results for other soft biological tissues. This being said, as noted earlier, the modulus of many soft tissues fall in the 1kPa to 100kPa range of moduli values. Because storage modulus and Young’s modulus are intrinsically related it can be preliminarily concluded that the DEHP-PVC material, which effectively mimics the Young’s modulus of soft biological tissues, could also be utilized as an analogue for dynamic applications. However, further testing would need to be done to prove this correlation.

This trend of soft biological tissue imitation extends to the results of the CT attenuation found in Figure 3.8. The CT attenuation values vary from 50 HU to 76.5 HU for the optimal 80% DEHP ratio. As tested by Ursani et al 2015 soft biological tissues such as myocardial tissue has a CT attenuation of approximately 60 HU. Although the PVC results deviate slightly from 60HU, the 10 HU difference in the DEHP-PVC mixture is minor as compared to the HU difference between soft tissue (50-60HU) and bone (1000HU).

A comparison should also be drawn between the DEHP-PVC material and current analogue materials in literature. Commonly used materials include poly-glycerol sebacate (PGS), silicones and its derivatives. The myocardial tissue analogue PGS is of particular interest. When

64 manufacturing this material, changes in processing temperature allow for a modulus range of 0.13 to 1.33 MPa to be obtained (Li et al 2015). Additionally, based on the low molecular weight and density of the material it is expected that PGS would have a CT number between 0- 100 HU. PGS, unlike DEHP-PVC, has also been noted to be biocompatible and is used in a variety of in-vivo applications. These factors, combined with its versatility, give PGS a wide variety of applications. However, as noted by Li et al 2015, there is a significant amount of uncontrollable evaporation that occurs when manufacturing PGS, which makes fine-tuning and control of mechanical properties difficult. Moreover, this evaporation makes it difficult to mold PGS into complex shapes such as body organs. These factors combine to make PGS a difficult material to work with. Silicone, on the other hand, is an easy to manufacture material that is also commonly used in biomedical applications (Palchesko et al 2012). By combining silicone elastomers with silicone gels, the modulus of these materials can be tuned from 5kPa to 1.72 MPa. However, this range of mechanical values comes at the cost of a percent elongation below 50%. Additionally, the molecular weight of silicones such as PDMS can be in excess of 2500 푔 . This leads to high CT attenuation values exceeding 100HU. The complexity of PGS and 푚표푙 limitations of silicones leads to a niche ex-vivo application for the DEHP-PVC material. As seen in this work, the DEHP-PVC compound can mimic both the mechanical nature of soft tissues while possessing a high percent elongation, desired CT attenuation values, and simple processing/molding.

To further increase the practicality of this material, an optimization scheme is devised as seen in Figure 3.9 and Table 3.2. This scheme uses data from the results to create equations with plasticizer content as the primary variable. As seen in Figure 3.9 every property in the figure follows a parabolic curve of best fit with respect to plasticizer content. As the plasticizer becomes the primary component in the mixture, diminishing effects on material properties are seen in the majority of the tested parameters. These curves of best fit can be found in Table 3.2 where they were set against an objective value. This will allow researchers with specific viscoelastic, CT and modulus values to determine the exact quantity of DEHP plasticizer that is needed to achieve their desired values. From this work (Table 3.2) it is seen that an 80% DEHP ratio will provide an optimal analogue to myocardial tissue.

The proposed DEHP-PVC material meets all of the desired parameters needed in an ex-vivo soft biological tissue analogue. The material is simple to manufacture, versatile, and can be modified with a variety of additives. It is also seen to mimic the CT and mechanical properties of myocardial tissue. The range of mechanical values observed with the 75% to 85% DEHP ratios also gives the material a strong basis to mimic other soft biological tissues. Although the DEHP plasticizer and PVC are commonly used, to the author’s knowledge, the combination of both materials in such high plasticizer content and the striking mimicking of the mechanical properties of soft biological tissues has not been fully explored. This material can meet the current need in robotics and phantom applications as a flexible biomedical tissue analogue material. Ultimately, this material is targeted as a myocardial tissue analogue but could potentially serve as a general soft biological tissue analogue.

3.5 Conclusion

Overall this work aims to outline a DEHP plasticizer – PVC material which can act as an effective myocardial and soft biological tissue analogue. Three different ratios of the material were tested: 75%, 80%, and 85% DEHP plasticizer to 25%, 20%, and 15% PVC respectively. The ratios were then tested utilizing FTIR, tensile testing, dynamic mechanical analysis, and computed tomography scans. From the FTIR testing key C-CL and carbonyl bonds are observed indicative of a stable PVC-plasticizer matrix. The tensile testing finds a modulus range from 0.02 to 0.18 MPa. This reduction in modulus is accompanied by a decrease in percent elongation from 136% to 57%. Moreover, dynamic mechanical analysis was performed at a frequency range of 0.5 to 3.5Hz and finds a linear increase in the viscoelastic properties. The storage and loss modulus vary by an order of magnitude, the storage modulus from 0.006 to 0.12 MPa and the loss modulus from 0.0005 to 0.018 MPa. Finally, the computed tomography scans demonstrate a varying CT attenuation from 55 HU to 144 HU that encompasses the desired range for soft biological tissues. These properties demonstrate the applicability of this, simple to manufacture, material to mimic the mechanical and CT properties found in many soft biological tissues.

3.6 References

Bai, C., Shao, L., Da Silva, A.J. and Zhao, Z., 2003. A generalized model for the conversion from CT numbers to linear attenuation coefficients. IEEE Transactions on Nuclear Science, 50(5), pp.1510-1515.

Blais, P, J. Vinyl Technol. 11 (2), 71 (1989).

Boltz, T.F., Pavlicek, W., Paden, R., Renno, M., Jensen, A. and Akay, M., 2010. An anthropomorphic beating heart phantom for cardiac X-ray CT imaging evaluation. Journal of Applied Clinical Medical Physics, 11(1).

Brett, P.N., Fraser, C.A., Henningan, M., Griﬃths, M.V., Kamel, Y., 1995. Automatic surgical tools for penetrating ﬂexible tissues. IEEE Engineering Medicine and Biology 264–270

Burdea, G.C. and Brooks, F.P., 1996. Force and touch feedback for virtual reality (pp. 3-4). New York: Wiley.

Chen QZ, Bismarck A, Hansen U, Junaid S, Tran MQ, Harding SE, Ali NN, Boccaccini AR. Characterisation of a soft elastomer poly(glycerol sebacate) designed to match the mechanical properties of myocardial tissue. Biomaterials 2008;29:47–57.

Coello Coello, C.A. and Becerra, R.L., 2009. Evolutionary multiobjective optimization in materials science and engineering. Materials and manufacturing processes, 24(2), pp.119-129.

Charest, J.M., Califano, J.P., Carey, S.P. and Reinhart‐King, C.A., 2012. Fabrication of substrates with defined mechanical properties and topographical features for the study of cell migration. Macromolecular bioscience, 12(1), pp.12-20.

Chowdhury, F., Li, Y., Poh, Y.C., Yokohama-Tamaki, T., Wang, N. and Tanaka, T.S., 2010. Soft substrates promote homogeneous self-renewal of embryonic stem cells via downregulating cell- matrix tractions. PloS one, 5(12), p.e15655.

Daniels, P.H., 2009. A brief overview of theories of PVC plasticization and methods used to evaluate PVC‐plasticizer interaction. Journal of vinyl and additive technology, 15(4), pp.219- 223.

Edenbaum, J., 1992. Plastics additives and modifiers handbook. Van Nostrand Reinhold Company.

Engelmayr GC, Cheng M, Bettinger CJ, Borenstein JT, Langer R and Freed LE, Nature Mater 7:1003–1010 (2008).

Engler, A.J., Sen, S., Sweeney, H.L. and Discher, D.E., 2006. Matrix elasticity directs stem cell lineage specification. Cell, 126(4), pp.677-689.

Fioretta, E.S., Fledderus, J.O., Baaijens, F.P. and Bouten, C.V., 2012. Influence of substrate stiffness on circulating progenitor cell fate. Journal of biomechanics, 45(5), pp.736-744.

Flügge, W., 2013. Viscoelasticity. Springer Science & Business Media.

Forsell, C., Eriksson, P., France-Cereceda, A. and Gasser, C., 2013. Failure properties for the thoracic aneurysm wall: Differences between BicuspidAortic Valve (BAV) and Tricuspid Aortic Valve (TAV) patients.

Gachter R, Muller H, editors. Plastics additives handbook. 3rded. New York: Carl Hanser; 1990.

Ghista, D.N., Vayo, W.H. and Sandler, H., 1975. Elastic modulus of the human intact left ventricle—determination and physiological interpretation. Medical and biological engineering, 13(2), pp.151-161.

González, N. and Fernández‐Berridi, M.J., 2006. Application of Fourier transform infrared spectroscopy in the study of interactions between PVC and plasticizers: PVC/plasticizer compatibility versus chemical structure of plasticizer. Journal of applied polymer science, 101(3), pp.1731-1737.

Guarrotxena, N.; Tiemblo, P.; Martıńez, G.; Go´mez-Elvira, J. M.; Millań, J. L. Eur Polym J 1998, 34 (5/6), 833. 26. Martıńez, G.; Mijangos, C.; Millań, J. L. J Polym Sci Part A: Polym Chem 1988, 26, 1629.

Haim J, Hyatt D, editors. International plastics handbook. 3rd ed. New York: Carl Hanser; 1995.

Jiang, F.X., Yurke, B., Firestein, B.L. and Langrana, N.A., 2008. Neurite outgrowth on a DNA crosslinked hydrogel with tunable stiffnesses. Annals of biomedical engineering, 36(9), pp.1565- 1579.

Krauskopf LG. Plasticizers: types, properties and performance.In: Nass LI, Heiberger CA, editors. Encyclopedia of PVC, 2nd ed, vol. 2. New York: Marcel Dekker; 1988.

J. M. Lambert, M.-S. Lee, R. A. Taller and D. D. Solomon, J. Vinyl Technol. 13, 204 (1991).

Li, X., Hong, A.T.L., Naskar, N. and Chung, H.J., 2015. Criteria for Quick and Consistent Synthesis of Poly (glycerol sebacate) for Tailored Mechanical Properties. Biomacromolecules, 16(5), pp.1525-1533.

Liu, Q., Jiang, L., Shi, R. and Zhang, L., 2012. Synthesis, preparation, in vitro degradation, and application of novel degradable bioelastomers—A review. Progress in polymer science, 37(5), pp.715-765.

Mendis, K.K., Stalnaker, R.L., Advani, S.H., 1995. A constitutive relationship for large deformation ﬁnite element modeling of brain tissue. Transactions of ASME, Journal of Biomechanical Engineering 117, 279–285

Miller, K., 2000. Constitutive modelling of abdominal organs. Journal of biomechanics, 33(3), pp.367-373.

Miller, K., Chinzei, K., 2000. New UWA Robot – possible application to neurosurgey, Technical Papers of ISA, Biomedical Sciences Instrumentation, vol. 36, pp. 135–140.

Murphy J. Additives for plastics handbook, 2nd ed. New York: Elsevier; 2001.

El-Naggar, M.A., 2014. An Investigation on Silicone and Silicone Rubber Stents in Comparison with Stainless Steel Stents: Introducing a Double Layer Stent Model. British Journal of Applied Science & Technology, 4(15), p.2206.

Palchesko, R.N., Zhang, L., Sun, Y. and Feinberg, A.W., 2012. Development of polydimethylsiloxane substrates with tunable elastic modulus to study cell mechanobiology in muscle and nerve. PloS one, 7(12), p.e51499.

Puskas, J.E. and Chen, Y., 2004. Biomedical application of commercial polymers and novel polyisobutylene-based thermoplastic elastomers for soft tissue replacement. Biomacromolecules, 5(4), pp.1141-1154.

Rahman, M. and Brazel, C.S., 2004. The plasticizer market: an assessment of traditional plasticizers and research trends to meet new challenges. Progress in Polymer Science, 29(12), pp.1223-1248.

Rai R, Keshavarz T, Roether JA, Boccaccini AR, Roy I. Medium chain length polyhydroxyalkanoates, promising new biomedical materials for the future. Mat Sci Eng R 2010;72:29–47.

Riley, W.A., Barnes, R.W., Evans, G.W. and Burke, G.L., 1992. Ultrasonic measurement of the elastic modulus of the common carotid artery. The Atherosclerosis Risk in Communities (ARIC) Study. Stroke, 23(7), pp.952-956.

Ramadan, S., Paul, N. and Naguib, H.E., 2017. Standardized Static and Dynamic Evaluation of Myocardial Tissue Properties. Biomedical materials (Bristol, England). DOI: 10.1088/1748- 605X/aa57a5

Shebanova, O. and Hammer, D.A., 2012. Biochemical and mechanical extracellular matrix properties dictate mammary epithelial cell motility and assembly. Biotechnology journal, 7(3), pp.397-408.

Ursani, A., Hoy, C., Moghe, S. and Paul, N.S., 2015. Characterization of Vulnerable Plaque with Dual-Energy during CT Coronary Angiography: A Phantom Study. In World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada (pp. 91-94). Springer International Publishing.

Ursani, A., Rice, M., Sajja, S., Ursani, F. and Paul, N., 2015. Development of Dynamic Anthropomorphic Heart Phantom (DHAP). In World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada (pp. 85-90). Springer International Publishing.

Wang, Y., Ameer, G.A., Sheppard, B.J. and Langer, R., 2002. A tough biodegradable elastomer. Nature biotechnology, 20(6), pp.602-606.

Watanabe, Y., 1999. Derivation of linear attenuation coefficients from CT numbers for low- energy photons. Physics in medicine and biology, 44(9), p.2201.

Wypych, G., 2004. Plasticizers use and selection for specific polymers (pp. 273-379). ChemTec Publishing: Toronto, Canada.

Yoda, Ryuichiro. "Elastomers for biomedical applications." Journal of Biomaterials Science, Polymer Edition 9.6 (1998): 561-626.

Zhu, H., Bhatia, S. and Chauhan, A., 2007. Dynamic mechanical properties of porcine lacrimal canaliculus. Current eye research, 32(10), pp.829-835.

3.7 Appendix B

a) DEHP-PVC (Water Submerged) - Storage Modulus 0.2

0.1 (MPa) 0 0 0.5 1 1.5 2 2.5 3 3.5 4 Storage Storage Modulus Frequency (Hz) 75% DEHP 80% DEHP 85% DEHP

0.04 b) DEHP-PVC (Water Submerged) - Loss Modulus

0.02

0 0 0.5 1 1.5 2 2.5 3 3.5 4

Loss Loss Modulus(MPa) Frequency (Hz) 75% DEHP 80% DEHP 85% DEHP c) DEHP-PVC (Water Submerged) - Stiffness 2000

1000

Stiffness Stiffness (N/m) 0 0.5 1 1.5 2 2.5 3 3.5 4 Frequency (Hz) 75% DEHP 80% DEHP 85% DEHP d) DEHP-PVC (Water Submerged) - Tan Delta 0.4

0.2 TanDelta 0 0 0.5 1 1.5 2 2.5 3 3.5 4 Frequency (Hz) 75% DEHP 80% DEHP 85% DEHP

Figure B.1: Storage Modulus (a), Loss Modulus (b), Stiffness (c), and Tan Delta (d) from the dynamic mechanical analysis results for the DEHP – PVC samples submerged in a water solution.

Chapter 4

4 Validation of a Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom

In this work improvements and validation of TGH’s Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP) was performed. A synthetic heart model was developed utilizing the PVC: Plasticizer material developed in Chapter 3 of this work. The model was then integrated with the current phantom apparatus and coronary artery trees were added to the model. The phantom was then run at a heart rate of 60 BPM and a pressure of 60mmHg while a CT scan was performed. The scanning protocol and phantom mimic the full cardiac cycle of the heart from 0 to 95% throughout systole and diastole. From this scan the effectiveness of the phantom was measured through three key parameters: myocardial and heart visualization, plaque analysis, and coronary artery motion. Firstly, the phantom demonstrated proper automatic recognition of the synthetic myocardial tissue, embedded plaques, and blood pools. This allows for proper visualization and analysis of the two chambers and arterial trees. Secondly an intra-luminal diagnosis of the coronary artery trees demonstrated the phantoms ability to detect both soft and hard plaques. Finally, the phantom mimicked the motion profile of the coronary arteries, which allows for replication of physiological scanning conditions. These results demonstrate the phantoms ability to act as a platform for further studies that can work towards optimizing the scanning protocols utilized for computed tomography coronary angiography scans.

The content of this chapter has been submitted to IEEE Transactions on Medical Imaging

4.1 Introduction

There is increasing interest in reducing radiation dose from Computed Tomography (CT) as CT scans are commonly performed and are responsible for more than two thirds of all radiation exposure in medical imaging [Kalra et al 2004, Linton et al 2003]. The challenge with radiation dose reduction is to ensure that the lowest radiation dose is tailored to the individual characteristics of each patient while maintaining diagnostic image quality [Hara et al 2009]. In current radiological practice, there are a range of scanning protocols and settings which can be utilized to limit radiation exposure and maximise image quality during a CT scan. The image reconstruction slice thickness, the duration of x-ray exposure, the X-ray tube exposure settings, CT scan range and number of phases; and reconstruction algorithms all play a complex and intertwined role in the CT image quality and patient radiation dose. [Nakayama et al 2006]. This has led to a significant amount of effort to understand methodologies which can lower patient radiation dose through refined reconstruction algorithms [Marin et al 2009, Heyer et al 2007, Nakayama et al 2005, Schindera et al 2008] and CT scanner technological advancements such as automatic exposure control [Siegal et al 2004, McCollough et al 2006]. Another common approach is to prioritise low X-ray tube voltage settings in smaller patients, especially when intravenous iodinated contrast medium has been used during the CT scan. [Funama et al 2005]. Essentially these approaches entail improving the image processing, reducing image noise, and improving contrast sensitivity.

The testing of novel and aggressive radiation dose lowering protocols is important prior to validation in human subjects. Most commonly, static inserts or “phantoms” are utilized [Siegal et al 2004]. These inserts have known electron densities and CT attenuations that mimic the human condition and allow CT scanner settings to be modified and the effect on various tissue types, geometries, and thicknesses can be observed [Paul et al 2010, Caon et al 1997, Zankl et al 1988.]. This work provides valuable information and can be used as a foundation for more comprehensive work that uses anthropomorphic (“life-like”) phantoms that simulate the shape and the X-ray absorption of human subjects. However, both static conventional and anthropomorphic phantoms are unsuitable as a research tool for dynamic imaging in body regions where motion is unavoidable and motion artifacts provide significant challenges to CT image quality [Boas et al 2012]. To combat these artifacts faster gantry rotations, multiple X-ray sources, or advanced reconstruction techniques can be utilized; and these are particularly

74 effective for CT scanning of the heart [Fleischmann et al 2011, Yu et al 2007]. These advancements have allowed for cardiac CT scans to be performed within a single heartbeat; with minimization of cardiac motion artifact and robust evaluation of the coronary arteries [Leshka et al 2009].

The initial focus of cardiac CT was to evaluate the severity of coronary artery occlusions due to Coronary Artery Disease (CAD) using Computed Tomography Coronary Angiography (CTCA). However, there is more recent focus on the use of CTCA to characterize CAD into non-calcified (lipid rich, fibrous, mixed) and calcified intimal plaque [Bamberg et al 2008]. This characterization requires ultra-high resolution CT scans with minimal motion artifact at acceptable patient radiation dose. There are significant challenges for CTCA as the coronary arteries taper from a maximum diameter of 4mm at the origin to less than 1mm distally. In addition, the coronary arteries are in constant motion, each coronary artery experiences different ranges of displacement during the cardiac cycle and the velocity of motion varies with the cardiac frequency. These challenges have resulted in relatively high patient radiation dose with associated increased risk of radiation induced carcinogenesis in patients [Einstein et al 2007].

Novel reconstruction algorithms and faster CT image acquisition protocols have been developed to address these challenges. Ursani et al 2015, developed a prototype dynamic anthropomorphic heart phantom (DAHP) to facilitate testing of these novel developments. The initial prototype was modified to more accurately simulate the in-vivo coronary artery environment. The modifications to the original DAHP resulted in development and utilization of more realistic synthetic tissue and cardiac motion resulting in a Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP); the phantom’s utility will be validated in this study. The TREAD-CAP consists of an ECG gating simulator which is used as the driving electrical signal for a mechanical pumping system. This allows for the system to replicate the cardiac cycle from systole to diastole with a wide range of pre-programmable cardiac rhythms. The mechanical system pumps contrast agent identical to that used during a CTCA. Finally, the contraction of the tissue realistic heart model simulates that of a human heart. This heart model has attached (hollow) synthetic epicardial coronary arteries for simulation of CAD at various severity levels of occlusion and using different types of synthetic plaque.

Validation of the TREAD-CAP as an effective imaging research tool for CAD requires that the phantom must be able to perform several key functionalities. (1) The phantom must possess the correct CT structure and attenuation of myocardial tissue to be automatically recognized as heart tissue by a widely used and commercially available post-processing workstation and dedicated cardiac software. (2) The phantom must replicate the contractile pattern of the heart. This aim can be analysed by evaluation of the volume expansion/contraction in the fluid chambers and the displacement of the synthetic epicardial coronary arteries. (3) The phantom must allow for visualization of embedded plaque representing the spectrum of intimal disease from lipid rich to calcified plaque. If these parameters are met the TREAD-CAP will enable future research studies and improvements to CTCA acquisition methodologies under dynamic conditions. This controllable experimental apparatus can have substantial implications in the development of ultra-high resolution ultralow dose CTCA scan protocols.

4.2 Methodology 4.2.1 TREAD-CAP Setup

4.2.1.1 TREAD-CAP Mechanical Setup

The TREAD-CAP consists of several key components that are critical to its functionality as seen in figure 4.1 A and B. An ECG monitor is connected to the input/output module which is in turn connected to the controller laptop. This allows for the ECG trace to be recorded and run through a LabVIEW simulation as previously described in Ursani et al 2015. The system is then connected to the mechanical air pump system. The air cylinder utilizes the ECG trace to pump in synchronization with the patient’s ECG signal. The air output then pushes the fluid contrast (Iodixanol 320mg/ml, GE Healthcare) through the fluid to air buffer. The fluid output is pumped into two diaphragms (“the cardiac ventricles”) within the heart as seen in figure 1 B. Overall this synthetic heart phantom replicates cardiac motion through a two-pronged system. First, the heart is mounted on a manifold which allows for rotation of the heart. The rotational mechanism is then synced, utilizing a secondary motor, with the expansion and contraction of the fluid containing chambers. These combined motions replicate the physiological twisting and squeezing of a human heart. The mathematical basis and algorithms behind the simulation software (NI Lab VIEW Control Design and Simulation Module) and cardiac motion profile has been previously described by Ursani et al 2015. In brief, based on this work the phantoms’

76 rotational motion, twist, is defined as the relative rotation between the apex and the base of the heart model. The ECG simulator then utilizes a proportional – integral (PI controller) in conjunction with a DC motor to drive the rotational displacement.

Finally, the heart is surrounded by an anthropomorphic chest phantom (Kyoto Kagaku Co., LTD, Japan) intended to simulate physiological conditions of X-ray beam hardening and CT attenuation.

The system in action can be seen in Supplementary File 1.

4.2.1.2 Fabrication of the Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP)

The synthetic heart was manufactured in collaboration with the Smart and Adaptive Polymers Laboratory (SAPL) at the University of Toronto. The heart module contains two 130cc chambers that house a diaphragm within each chamber (“ventricle”). The phantom was manufactured utilizing a combination of two materials. First a PVC-plasticizer compound [DEHP ≥99.5% 푔 푔 Bis(2-ethylhexyl) phthalate (Density = 0.985 , 푀 = 390.56 ) and Low Molecular Weight 푚푙 푤 푚표푙 푔 푔 PVC (Density = 1.4 , 푀 = 48000 ), Sigma Aldrich] was manufactured. This material was 푚푙 푤 푚표푙 designed to mimic the mechanical properties of myocardial tissue as described in Ramadan et al

2017, as well as the CT attenuation of myocardium (40 - 100 HU) as described in Ursani et al 2015. The critical properties include a Young’s modulus of .069 MPa, percent elongation > 70%, and Storage/Loss modulus (viscoelastic properties) from =0.02/0.003 MPa at 0.5Hz (30 BPM) to 0.04/0.008 MPa at 3.5Hz (210 BPM). In combination, the static parameters (Young’s modulus, percent elongation) are important in ensuring that the ventricles expand and contract with sufficient motion. The viscoelastic properties ensure that the heart material recovers and is elastic enough to track the dynamic heart motion at various human heart rates. The heart module was then coated with a thin polyurethane layer (Reoflex 20, Smooth On) to increase the longevity of the phantom. This polyurethane has a CT Attenuation of approximately 40HU to ensure it will not interfere with CT image quality.

4.2.1.3 Coronary Artery Design and Development

The TREAD-CAP also possesses two epicardial coronary arteries. These can be visualized in figure 4.2. Each coronary artery was manufactured to have two 2mm, one 3mm, and one 4mm branch. These branches enable the insertion of synthetic plaques into the phantom. Plaques of different CT attenuation and length were inserted into the phantom prior to CT scanning. The details of the CT attenuation and location of the plaques can be seen in figure 4.2 and table 4.1. The purpose of these plaques is to simulate both lipid rich (1,6), fibrous and calcified plaque (2,3,4,5) through a CT attenuation range of 40 – 800HU. The synthetic heart and arteries in combination allow for analysis of coronary artery motion.

Table 4.1: CT attenuation values for plaques inserted into the Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom. Plaque 1* was tapered down from 4-3-2 mm throughout its length.

Plaque Hounsfield Value (HU) 1* 40 2 400 3 200 4 400 5 800 6 40

Finally, iodinated contrast agent was inserted into various locations in the phantom to prepare the phantom prior to the CT scan. Ultrasound gel (Wavelength CL, National Therapy Products) with

CT attenuation of 40HU was inserted within the heart chambers to remove air and to provide a visual appearance similar to that of myocardial tissue. Iodinated contrast agent (Iodixanol 320mg/ml) was inserted into system to provide flow of fluid within the diaphragms and mechanical apparatus. Finally, the coronary arteries were filled with a combination of iodinated contrast agent and Ultrasound gel to mimic a CT attenuation of approximately 500HU.

4.2.2 CT Acquisition

In this study the efficacy of the phantom was analyzed at a heart rate of 60 BPM and a pressure range from 60 – 80 mmHg. These preliminary settings were utilized as a measure of the phantoms effectiveness at settings where complications for the system are at a minimum. The scan was performed utilizing a third-generation wide volume CT (Aquilion One Genesis, TMSC, Ottawa) at an X-ray tube potential of 120kVp, a tube current of 250mA and 320x0.5mm detector array with a gantry rotation of 275msec. X-ray exposure was used for a full cardiac cycle (0 to 95%); this allowed for analysis of cardiac function through systole and diastole. A full summary of settings utilized can be found in table 4.2. Table 4.2: Computed tomography settings and scan protocols. CT scans were performed utilizing a third-generation wide volume CT (Aquilion One Genesis, TMSC, Ottawa). The protocol utilized captured the simulated cardiac cycle. CT Scanning Parameters Value Detector configuration 320 x 0.5mm Tube Voltage 120kVp Tube Current 250mA Exposure window 0 – 95% of the Cardiac Cycle Pressure 60 mmHg – 80mmHg Heart Rate 60BPM Sinus rhythm Scan and Display Field of View 32cm Slice Thickness 0.5mm Exposure Time 275 milliseconds Kernel FC04 Coronary Artery Contrast Ultrasound Gel + Iodixanol 320mg/ml Fluid Contrast Iodixanol 320mg/ml (14:1 water)

4.2.3 Data Collection Methods A combination of software was utilized to compile the results. First, a commercially available 3D post processing workstation with dedicated coronary imaging software (Vitrea FX, Vital Images, Minn) was utilized for segmenting the CT images of the phantom and for visualizing the coronary plaque. Secondly, 3D Slicer (https://www.slicer.org/, Fedorov et al, 2012) was utilized to create volumetrically accurate 3D models (.stl’s) of the chambers throughout the cardiac cycle. This enabled the measurement of cardiac volumes throughout the cardiac cycle. Finally, both Radiant DICOM viewer and the Vitrea Fx workstation were utilized to create segmented CT images of the phantom and to measure the motion of the phantom and coronary artery displacement. Measurements were performed by fusing images at different points in the cardiac cycle and measuring displacement values. 4.3 Results 4.3.1 CT Attenuation and Plaque Analysis

To validate the visual accuracy of the phantom several key measurements are seen in table 4.3. The essential measurements include the base of the phantom material and the inner and outer layers of polyurethane. It can be seen that each layer measures within the targeted Hounsfield Unit (HU) values for myocardial tissue. Additionally, the myocardium gel utilized to fill the phantom model measures 30 - 50 HU. Finally, the contrast agents used to fill both the coronary arteries and the fluid chambers measure 440 - 480 HU values which meet the clinical need.

Table 4.3: CT attenuation values of key phantom components.

Phantom Components CT attenuation (HU) Standard Target Value (HU) Deviation

PVC-Plasticizer Base 79 7.2 40-100

Inner Polyurethane Coating 37 18.98 40-60

Outer Polyurethane Coating 41 12.79 40-60

Myocardium Mimicking Gel 35 10.96 40-60

Fluid Contrast 448 22.83 ≈500

Artery Contrast 470.5 27.58 ≈500

A) B)

Figure 4.3: An intra-luminal diagnostic of the coronary arteries. A) A 3D render of the selected artery branch can be seen for the selected arterial branches with embedded plaque. The 3mm Vessel 1 is highlighted here as outlined in figure 2. B) An occlusive 400 HU plaque can be seen here. The phantom demonstrates the ability to replicate a variety of plaque conidtions with a visual representation similar to that of live patients.

Following satisfactory contrast enhancement of the cardiac chambers and coronary arteries a visual analysis of the arteries was performed. As seen in figure 4.3 A and B the phantom demonstrates the ability to simulate coronary plaque conditions. Figure 4.3 A displays the accurate visual processing of the arteries as well as the proper identification of the synthetic myocardial tissue through an automatic recognition software. In figure 4.3 B the 3mm vessel 1 can be seen. This vessel had an embedded 400HU occlusive plaque, that can be clearly visualized within the artery. Overall this figure is meant to demonstrate the visual aspect of the synthetic coronary arteries as well as the ability for controlled plaque analysis. This could allow for further studies on various phenomena such as the partial volume effect (Paul et al 2010).

4.3.2 Qualitative analysis of the Dynamic Phantom Motion

Following the validation of CT attenuation values in the TREAD-CAP, the phantom was scanned through a full cardiac cycle, whilst the phantom was beating. In figure 4.4 a multi view image of the phantom can be seen in addition to a 3D surface shaded volumetric rendered image. Full videos demonstrating the coronal, axial and sagittal views of the CT scan can be seen in supplementary videos 2, 3, 4. As demonstrated the phantom exhibits the correct pixel density for both the synthetic myocardial tissue and the contrast enhanced blood pool. In addition, there is realistic reproduction of the low attenuation (40HU) epicardial tissues that support the native coronary arteries in-vivo. Finally, the 3D surface shaded rendered image allows for better appreciation of the geometry of the coronary artery around the heart structure. In figure 4.5 as well as supplementary Video 5 the coronal CT reconstruction of the phantom throughout the cardiac cycle can be seen. Both the video and image demonstrate the volume change and displacement of the blood pool throughout the cardiac cycle in 5% increments from 0 to 95%. The overall time for the cycle is approximately 1 second, corresponding to 60 BPM. A complete volume and displacement analysis can be seen in section 3.3.

In figure 4.6 key qualitative measures can be seen. Through fusion of images at different stages of the cardiac cycle the maximum blood pool displacement and rotation can be visualized. In figure 4.6 A) superimposition of ventricular CT at maximum contraction (end systole) and maximum expansion (end diastole) provides an estimate of cardiac output (ejection fraction) between the 30% to 75% phases. Additionally, in figure 4.6 B the maximum cardiac rotation can be seen from the 0% to 30% phases. These images demonstrate that the heart is simulating the contraction and rotation of the in-vivo heart to the fully contracted state at the 30% phase corresponding to the end of the systolic phase.

Figure 4.5: Coronal views of the TREAD-CAP as it replicates the contraction and relaxation cycle of the in-vivo heart from 0 to 95% of the cardiac cycle.

Finally, Figure 4.7 illustrates an important aspect of how image quality changes throughout the cardiac cycle. As the phantom contracts between the 0 and 30% phases of the cardiac cycle significant increases in the velocity of ventricular contraction result in motion artifacts. The artifact can be seen on the periphery of the CT image, in the region of the coronary arteries. This motion blur is significant as it can cause significant difficulty in accurate analysis of the coronary artery plaque. Motion artifacts are a significant cause of image degradation in clinical coronary CT angiography and replication of this feature in the TREAD-CAP is a desired feature as it provides a direct analogue to issues encountered when scanning live patients.

4.3.3 Quantitative analysis of the DAHP volume displacement and motion

A quantitative analysis of ventricular volume and fluid displacement was performed to validate the qualitative evaluation of the TREAD-CAP. Figure 4.8 displays the change in the ventricular blood pool volume over time. The blood volume is minimal at 30% of the cardiac cycle which corresponds to the end of the systolic phase. The blood pool is of maximal volume at the 75% phase, which corresponds to mid/late diastole. In addition, the fluid displacement from the left ventricle is 50 to 80 ml compared to the right ventricle which is 60 to 80 ml. The overall effectiveness of the simulated left ventricle can be seen in table 4.4. Key values are compared with clinical reference ranges based on work by Prokop et al 2003. The systolic volume, cardiac output, and myocardial mass are as expected based on the specifications of the phantom. However, the stroke volume and ejection fraction are lower than that typically seen in patients as the phantom was operated at a pressure maximum of 80mmHg; this is considerably lower than physiological systolic blood pressure which typically ranges from 90-120mmHg during CTCA.

Table 4.4: TREAD-CAP left ventricular output as compared to reference ranges from Prokop et al 2003. Deviations occur due to a lower pressure utilized in the current phantom setup.

Parameter Measured Values Reference Range

Ejection Fraction 37.7 62 - 72 (%)

End Diastolic Volume 80.1 87 - 155 (ml)

End Systolic Volume 49.9 26 - 54 (ml)

Stroke Volume 30.2 59 - 105 (ml)

Cardiac Output 1.8 - (L / min)

Myocardial Mass 174 119 - 177 (g)

The overall motion profile of the coronary arteries can be found in figures 4.9 and 4.10. Measurements are normalized to the 30% cycle as this marked the end of systole, the most contracted state for the phantom. In figure 4.9 the coronary artery displacement oscillates throughout the cardiac cycle for each arterial tree. The maximum displacements are as follows: 13.5mm/9.3 mm in the anterior\posterior directions and 5.6mm/6.7mm in the right\left directions for the left/right coronary arteries respectively. There is additional apical (z direction) motion of 1.5mm that occurs due to the outward expansion of the model. In figure 4.10 the net vectorial displacement of the arteries is displayed. The maximum displacement is approximately 13.9mm whereas the maximum rotation is 30.8 degrees. The maximum rotation and displacement are in synchronization as the rotation causes significantly more motion than the expansion/contraction of the model. This is due to compression of the soft myocardial layer as expansion occurs.

Figure 4.10: Net displacement of the coronary arteries throughout the cardiac cycle from 0 to 95%. Minimum displacement and rotation occur at the end of diastole as seen at 30% of the cardiac cycle.

Arterial Velocity 60

20 Velocity (mm/s) 10

0 0 15 30 45 60 75 90 Cardiac Cycle (%)

Left Arterial Velocity Right Arterial Velocity

Figure 4.11: Velocity profile of the left and right coronary arteries. The maximum velocity corresponds with the end of the systolic phase between 25 and 30% of the cardiac cycle.

Finally, the arterial velocity was calculated utilizing the displacements throughout the cardiac cycle and the overall scan time of 1 second (.05 seconds per 5% phase). A maximum velocity is observed between the 25 and 30% phases. This demonstrates the increase in the phantoms rotation and contraction as the heart reaches the end of the systolic phase and corresponds with the lowest volume of the blood pool. Furthermore, the left coronary artery experienced a higher displacement and consequently a higher net velocity.

4.4 Discussion

This work demonstrates the versatility and functionality of the developed Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom (TREAD-CAP). The first requirement of the phantom is to simulate the appearance of the in-vivo heart on computed tomography coronary angiography scans. This is demonstrated on figures 4.3, 4.4, and table 4.3. The post processing cardiac segmentation and coronary artery software is able to identify and present the synthetic myocardial tissue; probe and display the coronary arteries. This is achieved through matching the CT attenuation (40-100 HU) of the synthetic materials to that of normal myocardial and coronary tissues as seen in table 4.3. The accuracy of these components greatly simplifies image analysis and visualization as limited post processing of the image is required for automatic cardiac

92 analysis. This is evident through the luminal plaque analysis demonstrated in figure 4.3. Moreover, the visualization of the cardiac chambers and of the coronary arteries flow is aided by enhancing these to ~500 HU to simulate the appearance on clinical CTCA studies. These combined results achieve the first goal of this phantom, realistic cardiac visualization.

The second major functionality of the TREAD-CAP requires the ability to replicate cardiac motion throughout the cardiac cycle. As seen in figure 4.5 and supplementary Video 5 the TREAD-CAP cycles through systole and diastole with a profile similar to that of in-vivo cardiac motion at 60BPM. For the TREAD-CAP, the simulated ventricular systole lasts approximately 300ms from the onset of contractile motion, as there is no atrial systolic phase the diastolic phase is lengthened to the remaining 700ms of the 1000ms cycle. Using this cardiac cycle, a cardiac output of 1.8 L/min is achieved as well as an ejection fraction of 38% as seen in figure 4.8 and table 4.4. This compares to a resting cardiac output of ~ 5l/min and a left ventricle ejection fraction of 55-60%. The sub-par cardiac hemodynamics from the TREAD-CAP are a current limitation of the system and a source of future work. However, for the purposes of coronary artery analysis, the most important feature is the arterial motion that results from the expansion and contraction of the phantom. In figure 4.6, the maximum volume displacement and rotational displacements can be seen. From these two synchronized motions the maximum observed displacement is approximately 14mm measured from the end systolic state as a reference point. Additionally, the arterial displacement oscillates with a maximum anterior/posterior displacement of 13.5mm for the left coronary artery and 9.3mm for the right coronary artery. The maximum right/left motion is 5.6mm and 6.7mm for the left and right coronary arteries respectively. Moreover, as seen in figure 4.11 the maximum velocity was 55mm/s for the left coronary artery and 40.7mm/s for the right coronary artery. These velocities correspond to the range of volumetric changes seen previously by Schechter et al 2005 and 2006.

Schechter studied in-vivo 3D displacement of coronary arteries on coronary angiograms and noted a mean and maximum displacement of 14.4mm and 23.2mm of the right coronary artery respectively. The mean and maximum displacement of the left anterior descending coronary artery was 12.1 and 14.5mm respectively. Not surprisingly, the steepest increase in coronary artery displacement occurred during the systolic phase. This corresponds with the motion profile for the TREAD-CAP where coronary artery displacement approached 13mm and peaked at the end of the systolic phase. The profile is further validated through evaluation of the arterial

93 velocities. Schechter et al, demonstrated arterial velocities varying from 30 to 100 mm/s for the right (mean = 69.8 mm/s) and left (mean = 34.5 mm/s) coronary arteries. This agrees with the 5.7 to 55 mm/s produced by the TREAD-CAP as demonstrated in figure 4.11. An important note for this comparison is that the evaluated TREAD-CAP setting was a cardiac frequency of 60 BPM and a pressure output of 60 mmHg. The patient heart rate was not reported in Schechter’s study; the average patient age was 66 years old. The overall profiles of the TREAD-CAP simulate the results of in-vivo coronary artery motion.

The third requirement for the cardiac phantom is the utility of the TREAD-CAP to provide a realistic platform for coronary plaque analysis, and this requires replication of physiological scanning conditions. The cardiac phantom was demonstrated to mimic the coronary artery displacement profile and the motion artifacts that occur at peak velocities. This can be visualized in figure 4.7. Motion blur is an important feature for the TREAD-CAP to demonstrate, as it allows for controlled replication of challenges that radiologists encounter when performing clinical CTCA procedures. As noted by Ritchie et al 1992, Roos et al 2002, Hong et al 2001 motion blurring reduces the clinical accuracy of CAD diagnoses utilizing CTCA. The importance of this blurring becomes especially clear when attempting to characterize coronary plaque as seen in figure 4.3. The plaque analysis in figure 4.3 was performed at the 70% phase of the cardiac cycle, where coronary artery velocity was significantly reduced from the maximum. In figure 4.3 B the effect of non-calcified plaque is particularly clear as it causes significant luminal stenosis. However, it is challenging to draw this conclusion from the image reconstructions at the 25% phase due to motion artifacts causing blurring of the image. The TREAD-CAP can be utilized to develop in-depth knowledge pertaining to the interaction between cardiac motion, plaque composition and assessment of lumen occlusion, the third goal of this study.

The versatility of the TREAD-CAP is apparent when compared to current commercially available dynamic cardiac phantoms. A cardiac dynamic phantom developed by Boltz et al 2009 follows a similar ECG tracing approach as the TREAD-CAP. This phantom however is tailored more towards CT and x ray evaluation of cardiac exams. Extensive work has been performed by this group, however, their phantom is not intended for replication of coronary arterial motion. The niche application of dynamic phantoms is further seen through works by Zhu et al 2014, Vannelli et al 2015 where phantoms were designed with goals such as fluid flow simulation or

MRI evaluation. From these descriptions, it is apparent that the TREAD-CAP is unique as it is specifically designed for the evaluation and improvement of CTCA techniques. The TREAD- CAP allows for replication of a patient’s cardiac rhythm through a mechanical/electrical system for both sinus rate and rhythm and for abnormal heart patterns. The phantom can be used to mimic the heart rate / rhythm for patients who require CTCA but who may have abnormal patterns of cardiac contraction and who need determination of the optimal exposure parameters to minimize radiation dose and maximize diagnostic information. In addition, the phantom can assist in evaluating the spatial and temporal resolution, and image contrast of novel image reconstruction algorithms. This study provides a proof of concept to test the utility of a Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom to achieve three main goals that are crucial in providing an effective research tool for image optimization. These goals are: accurate CT attenuation, coronary motion, and plaque visualization. Ultimately this is a step forwards towards ultra-high resolution, ultra-low dose computed tomography coronary angiography.

4.5 Conclusions

In summary, a proof of concept test was run utilizing a Tissue Realistic Anthropomorphic Dynamic Coronary Artery Phantom. The phantom consists of a mechanical pumping system coupled to a tissue realistic heart and coronary artery model. An ECG trace was utilized to drive the mechanical system at a heart rate of 60BPM and the phantom was run at 60 mmHg. The TREAD-CAP was found to have a maximum rotation of 30 degrees, displacement of 14mm, and cardiac output of 1.9 L/min. Additionally, the profile of cardiac motion corresponded to that of physiological conditions with a peak velocity in the systolic phase of 55 mm/s. Finally, the phantom demonstrated an ability to mimic the CT attenuation of plaque, myocardial tissue, coronary arteries, and contrast enhanced blood pools. Future work entails testing at increased pressures and heart rates to demonstrate an ability to simulate a wider cohort of patient parameters.

4.6 References

Bamberg, F., Dannemann, N., Shapiro, M.D., Seneviratne, S.K., Ferencik, M., Butler, J., Koenig, W., Nasir, K., Cury, R.C., Tawakol, A. and Achenbach, S., 2008. Association between cardiovascular risk profiles and the presence and extent of different types of coronary

95 atherosclerotic plaque as detected by multidetector computed tomography. Arteriosclerosis, thrombosis, and vascular biology, 28(3), pp.568-574.

Boas, F.E. and Fleischmann, D., 2012. CT artifacts: causes and reduction techniques. Imaging in Medicine, 4(2), pp.229-240.

Caon, M., Bibbo, G. and Pattison, J., 1997. A comparison of radiation dose measured in CT dosimetry phantoms with calculations using EGS4 and voxel-based computational models. Physics in medicine and biology, 42(1), p.219.

Einstein, A.J., Henzlova, M.J. and Rajagopalan, S., 2007. Estimating risk of cancer associated with radiation exposure from 64-slice computed tomography coronary angiography. Jama, 298(3), pp.317-323.

Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M. and Buatti, J., 2012. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magnetic resonance imaging, 30(9), pp.1323- 1341.

Fleischmann, D. and Boas, F.E., 2011. Computed tomography—old ideas and new technology.

Funama, Y., Awai, K., Nakayama, Y., Kakei, K., Nagasue, N., Shimamura, M., Sato, N., Sultana, S., Morishita, S. and Yamashita, Y., 2005. Radiation dose reduction without degradation of low-contrast detectability at abdominal multisection CT with a low–tube voltage technique: phantom study 1. Radiology, 237(3), pp.905-910.

Hara, A.K., Paden, R.G., Silva, A.C., Kujak, J.L., Lawder, H.J. and Pavlicek, W., 2009. Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study. American Journal of Roentgenology, 193(3), pp.764-771.

Heyer, C.M., Mohr, P.S., Lemburg, S.P., Peters, S.A. and Nicolas, V., 2007. Image quality and radiation exposure at pulmonary CT angiography with 100-or 120-kvp protocol: prospective randomized study 1. Radiology, 245(2), pp.577-583.

Hong, C., Becker, C.R., Huber, A., Schoepf, U.J., Ohnesorge, B., Knez, A., Bruning, R. and Reiser, M.F., 2001. ECG-gated Reconstructed Multi–Detector Row CT Coronary Angiography: Effect of Varying Trigger Delay on Image Quality 1. Radiology, 220(3), pp.712-717.

Kalra, M.K., Maher, M.M., Toth, T.L., Hamberg, L.M., Blake, M.A., Shepard, J.A. and Saini, S., 2004. Strategies for CT radiation dose optimization 1. Radiology, 230(3), pp.619-628.

Leschka, S., Stolzmann, P., Desbiolles, L., Baumueller, S., Goetti, R., Schertler, T., Scheffel, H., Plass, A., Falk, V., Feuchtner, G. and Marincek, B., 2009. Diagnostic accuracy of high-pitch dual-source CT for the assessment of coronary stenoses: first experience. European radiology, 19(12), p.2896.

Linton, O.W. and Mettler Jr, F.A., 2003. National conference on dose reduction in CT, with an emphasis on pediatric patients. American Journal of Roentgenology, 181(2), pp.321-329.

Marin, D., Nelson, R.C., Schindera, S.T., Richard, S., Youngblood, R.S., Yoshizumi, T.T. and Samei, E., 2009. Low-tube-voltage, high-tube-current multidetector abdominal ct: Improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm—initial clinical experience 1. Radiology, 254(1), pp.145-153.

McCollough, C.H., Bruesewitz, M.R. and Kofler Jr, J.M., 2006. CT dose reduction and dose management tools: overview of available options 1. Radiographics, 26(2), pp.503-512.

Nakayama, Y., Awai, K., Funama, Y., Liu, D., Nakaura, T., Tamura, Y. and Yamashita, Y., 2006. Lower tube voltage reduces contrast material and radiation doses on 16-MDCT aortography. American Journal of Roentgenology, 187(5), pp.W490-W497.

Paul, N.S., Blobel, J., Kashani, H., Rice, M. and Ursani, A., 2010. Quantification of arterial plaque and lumen density with MDCT. Medical physics, 37(8), pp.4227-4237.

Prokop, M. and Galanski, M., 2003. Spiral and multislice computed tomography of the body. Thieme.

Ramadan, S., Paul, N. and Naguib, H.E., 2017. Standardized Static and Dynamic Evaluation of Myocardial Tissue Properties. Biomedical Materials.

Ritchie, C.J., Godwin, J.D., Crawford, C.R., Stanford, W., Anno, H. and Kim, Y.O.N.G.M.I.N., 1992. Minimum scan speeds for suppression of motion artifacts in CT. Radiology, 185(1), pp.37- 42.

Roos, J.E., Willmann, J.K., Weishaupt, D., Lachat, M., Marincek, B. and Hilfiker, P.R., 2002. Thoracic Aorta: Motion Artifact Reduction with Retrospective and Prospective Electrocardiography-assisted Multi–Detector Row CT 1. Radiology, 222(1), pp.271-277.

Shechter, G., Resar, J.R. and McVeigh, E.R., 2006. Displacement and velocity of the coronary arteries: cardiac and respiratory motion. IEEE Transactions on Medical Imaging, 25(3), pp.369- 375.

Shechter, G., Resar, J.R. and McVeigh, E.R., 2005. Rest period duration of the coronary arteries: implications for magnetic resonance coronary angiography. Medical physics, 32(1), pp.255-262.

Schindera, S.T., Nelson, R.C., Mukundan Jr, S., Paulson, E.K., Jaffe, T.A., Miller, C.M., DeLong, D.M., Kawaji, K., Yoshizumi, T.T. and Samei, E., 2008. Hypervascular Liver Tumors: Low Tube Voltage, High Tube Current Multi–Detector Row CT for Enhanced Detection— Phantom Study 1. Radiology, 246(1), pp.125-132.

Ursani, A., et al. "Characterization of Vulnerable Plaque with Dual-Energy during CT Coronary Angiography: A Phantom Study." World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada. Springer International Publishing, 2015.

Yu, H. and Wang, G., 2007. Data consistency based rigid motion artifact reduction in fan-beam CT. IEEE transactions on medical imaging, 26(2), pp.249-260.

Zankl, M., Veit, R., Williams, G., Schneider, K., Fendel, H., Petoussi, N. and Drexler, G., 1988. The construction of computer tomographic phantoms and their application in radiology and radiation protection. Radiation and environmental biophysics, 27(2), pp.153-164.

Zhu, Y., Luo, X.Y., Gao, H., McComb, C. and Berry, C., 2014. A numerical study of a heart phantom model. International Journal of Computer Mathematics, 91(7), pp.1535-1551.

Chapter 5

5 Conclusion 5.1 Concluding Remarks

In summary, this work aimed to expand on imaging research methodologies through a material based approach. A comprehensive analysis of myocardial tissue allowed for a synthetic analogue material to be developed. Additionally, when analyzing the myocardial tissue, methodologies were outlined to standardize both tensile and dynamic mechanical testing of soft biological tissues. A cyclic preloading condition was found to create a known set of reproducible testing conditions between each tensile test. This in turn reduced the deviation of results and significantly narrowed the obtained young’s modulus values as compared to literature. Furthermore, a dynamic mechanical analysis procedure was developed to determine the viscoelastic properties of soft biological tissues. These standardization methodologies will allow for further works which will improve the accuracy and validity of experimentally obtained mechanical values for soft tissues such as: tendons, ligaments, myocardium, and skeletal tissue. Moreover, accurate mechanical properties allow for the development of synthetic tissue analogue materials. In the second focus of this work a synthetic myocardial mimicking material was investigated. A dioctyl phthalate plasticizer and low molecular weight PVC compound were selected as the two material components. These parts combine to form a soft and flexible thermoplastic. The material was then investigated for three key features. First, the material was scanned under a CT scanner at TGH to confirm the expected attenuation properties. The molecular weight and density of the components allowed for a CT attenuation approaching that of myocardial tissue to be obtained. Secondly the amount of plasticizer was varied to determine the optimal ratio of components to achieve the soft yet flexible properties of myocardial tissue. The accuracy of the synthetic analogue was validated through identical mechanical testing procedures that were used to quantify the myocardial tissue properties. Finally, as the material is a thermoplastic it is easily processable which allows for the material to be molded, a key parameter if it will be integrated with the Toronto General Hospital dynamic phantom. An 80% plasticizer: 20% PVC ratio was determined to best mimic the CT and mechanical properties of myocardial tissue.

100

Utilizing the PVC-Plasticizer material, a synthetic heart and coronary artery model were developed that integrated with the TREAD-CAP. The efficacy of this phantom was then validated by performing a full cardiac cycle scan under a CT scan. The dynamic phantom system replicates cardiac motion through a mechanical and electrical system. First an ECG trace of the heart saved from a patient file is utilized to drive a mechanical air pump. The integration of these components allows for the cardiac rhythm of any patient to be simulated. The air pump drives the flow of a fluid contrast agent which is pumped into the synthetic heart model. The heat model simulates cardiac motion through the simultaneous expansion/contraction and rotation of the model. Additionally, the synthetic coronary arteries had plaques embedded in them. The phantoms efficacy was then validated by performing a CT scan while replicating a full cardiac cycle at 60BPM, from systole to diastole. The obtained motion and volume values from the scan were compared to human physiology and the phantom achieved its three main imaging goals: cardiac visualization, plaque analysis, and coronary artery motion.

In conclusion, the analysis and development of a myocardial tissue analogue allowed for an improved synthetic heart model to be integrated with the TREAD-CAP. This phantom replicates the conditions of a CTCA procedure on a human patient through a controllable: heart rate, pressure, and motion profile. Ultimately, this model can be utilized to perform future studies that can help to optimize CTCA procedures through the development of CT scanning protocols. This work is just one step forward towards ultra-high resolution, ultra-low dose clinical CTCA procedures.

5.2 Future Work

There are several potential sources of future work and project expansions. These expansions have been separated to two main categories, material and imaging research.

5.2.1 Materials Projects 1) Improvements to the synthetic myocardial material

There is still significant work that can be done to improve the mechanical properties of the synthetic myocardial material. Specifically work can be done to improve the total percent elongation and shear strength of the material. This will allow it to be a more durable material which will improve the longevity of the developed heart phantom model

101

2) Mimicking of other soft biological tissues (endocardium, pericardium, coronary arteries)

Although the focus of this work was on the development of a myocardial tissue analogue, similar work could be done on other biological tissues. Mimicking the endocardium and pericardium of the heart would allow for a more tissue realistic phantom to be developed. Additionally, a synthetic coronary artery material could further improve the accuracy of the phantoms motion profile.

5.2.2 Imaging Projects

1) Further validation and improvements to the dynamic heart phantom

There is still considerable work to be done on the dynamic heart phantom. The phantom needs to be modified to replicate a larger range of heart rates and pressures. There are mechanical limitations with the current pump setup and overcoming these would allow for a larger range of patient conditions to be replicated.

2) Lung Motion and the Respiratory cycle

Another important parameter in mimicking coronary artery motion is motion caused by the respiratory cycle. Developing an addition to the phantom which considers respiratory motion would better simulate the motion of the coronary arteries in the chest cavity.

3) Clinical CTCA study utilizing the dynamic heart phantom

Finally, once the phantom is fully completed a clinical study can be performed. Testing the phantoms efficacy at reducing patient radiation dosage while improving obtained image quality is the goal of this research. Performing a clinical study where the phantom is connected to patients and utilized to develop scanning protocols is an important step to achieving this goal.