by

Carlton F. O. Hoy

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science

Graduate Department of Mechanical and Industrial Engineering University of Toronto

© Copyright by Carlton Hoy 2015

Cardiovascular Computed Tomography Phantom Fabrication and Characterization through the

Tailored Properties of Polymeric Composites and Cellular Foams

ii

Abstract

Cardiovascular Computed Tomography Phantom Fabrication and Characterization through

the Tailored Properties of Polymeric Composites and Cellular Foams

Carlton F. O. Hoy

Master of Applied Science and Engineering

Graduate Department of Mechanical & Industrial Engineering

University of Toronto

2015

The overall objective of this thesis was to control the fabrication technique and relevant

material properties for phantom devices designated for computed tomography (CT) scanning.

Fabrication techniques using polymeric composites and foams were detailed together with

parametric studies outlining the fundamentals behind the changes in material properties

which affect the characteristic CT number. The composites fabricated used polyvinylidene

fluoride (PVDF), thermoplastic polyurethane (TPU) and polyethylene (PE) with

hydroxylapatite (hA) as additive with different composites made by means of different

weight percentages of additive. Polymeric foams were fabricated through a batch foaming

technique with the heating time controlled to create different levels of foams. Finally, the

effect of fabricated phantoms under varied scanning media was assessed to determine

whether self-made phantoms can be scanned accurately under non-water or rigid

environments allowing for the future development of complex shaped or fragile material

types.

iii

Acknowledgments

I would like to thank both Professor Hani Naguib and Dr. Narinder Paul for being attentive,

encouraging and knowledgeable supervisors throughout my MASc research. Their guidance

and support has helped me in countless ways through my studies and has been illustrated in

the following thesis.

I would also like to thank all of my fellow lab mates and colleagues in SAPL for aiding me

through their technical knowledge, equipment experience, and insight through ideas and

input on my research. I am thankful for the help that Reza Rizvi, Shahrzad Ghaffari, Farooq

Al Jahwari, Eunji In, Janice Song, Terence Lee, Mohamad Kshad, Kyle Eastwood, Shahriar

Ghaffari, Gary Sun, Nazanin Khalili, Muhammad Anwer, and Harvey Shi had provided the

past 2 years. I would further like to thank my undergraduate students Pranav Kadhiresan,

Kate Lonergan, Yuyang Pang, and Sherif Ramadan for their tireless help, incredible work

ethic and enthusiasm.

For their encouragement, belief and love and I would like to thank my family, Vance,

Hiroko, Vanessa and Jon. Without your support I would not be where I am now.

Last but not least I would like to dedicate this research to Ashton Hong. Your love and

endless support through countless nights of worry has lead me to be able to complete this

research thesis.

iv

ABSTRACT .......................................................................................................................................... II  

ACKNOWLEDGMENTS ................................................................................................................. III  

TABLE OF CONTENTS ................................................................................................................... IV  

LIST OF FIGURES .......................................................................................................................... VII  

LIST OF TABLES ............................................................................................................................. IX  

CHAPTER 1 INTRODUCTION ......................................................................................................... 1  1.1   PREAMBLE ................................................................................................................................. 1  1.2   THESIS OBJECTIVES ................................................................................................................... 2  

1.3   THESIS ORGANIZATION ............................................................................................................. 5  

CHAPTER 2 BACKGROUND AND LITERATURE SURVEY ..................................................... 6  

2.1   COMPUTED TOMOGRAPHY MEDICAL IMAGING PHANTOMS ..................................................... 7  2.2   COMPUTED TOMOGRAPHY MATHEMATICAL MODELING ......................................................... 8  

2.2.1   Computed Tomography Numbers - Hounsfield Units ....................................................... 9  2.2.2   Attenuation Coefficient & Electron Density ...................................................................... 9  

2.2.3   Effective Photon Energy & Atomic Number ................................................................... 12  

2.2.4   Bilinear Scaling ................................................................................................................ 13  2.3   MATERIAL FABRICATION ........................................................................................................ 15  

2.3.1   Polymeric Composite Materials ....................................................................................... 16  

2.3.2   Microcellular Foam Materials .......................................................................................... 17  2.4   COMPUTED TOMOGRAPHY PHANTOM STUDIES ...................................................................... 19  

2.4.1   Gammex 467 – Commercial CT Phantom ....................................................................... 19  2.4.2   Polymeric Phantom Design .............................................................................................. 19  

2.4.3   Coronary Artery Phantom Design .................................................................................... 21  2.5   SUMMARY ................................................................................................................................ 23  

Table of Contents

v

CHAPTER 3 POLYMERIC COMPOSITES FOR CARDIAC CT PHANTOM

APPLICATIONS ................................................................................................................................. 25  3.1   MOTIVATION ............................................................................................................................ 26  

3.2   MATERIALS AND METHODS ..................................................................................................... 28  3.2.1   Polymeric Matrices & Filler ............................................................................................. 28  

3.2.2   Phantom Material Processing ........................................................................................... 28  

3.2.3   Computed Tomography Number ..................................................................................... 29  3.2.4   Physical Properties ........................................................................................................... 30  

3.3   RESULTS .................................................................................................................................. 30  

3.3.1   Effect of Hydroxyapatite Content on CT Number ........................................................... 30  3.3.2   Density & Elemental Composition Values ...................................................................... 32  

3.3.3   Effect of Linear Attenuation Coefficient on CT Number ................................................ 33  3.4   DISCUSSION ............................................................................................................................. 36  

3.5   SUMMARY ................................................................................................................................ 37  

CHAPTER 4 POLYMERIC CELLULAR FOAMS FOR LOW DENSITY COMPUTED

TOMOGRAPHY PHANTOM APPLICATIONS ............................................................................ 39  

4.1   MOTIVATION ............................................................................................................................ 40  4.2   MATERIALS AND METHODS ..................................................................................................... 43  

4.2.1   Foaming Procedure .......................................................................................................... 43  4.2.2   Material Fabrication ......................................................................................................... 44  

4.2.3   Computed Tomography Number ..................................................................................... 46  4.2.4   Foam Characterization Methods ...................................................................................... 47  

4.3   RESULTS .................................................................................................................................. 48  

4.3.1   Effect of Cell Morphology on CT Number ...................................................................... 50  4.3.2   Effect of Cell Size on CT Number ................................................................................... 52  

4.3.3   Effect of Density on CT Number ..................................................................................... 52  

4.4   DISCUSSION ............................................................................................................................. 53  

4.5   SUMMARY ................................................................................................................................ 56  

CHAPTER 5 EFFECT OF SCANNING MEDIUM ON THE IN-HOUSE FABRICATED

POLYMERIC COMPOSITE CT PHANTOM DEVICES ............................................................. 58  5.1   INTRODUCTION ........................................................................................................................ 58  

5.2   MATERIALS AND METHODS ..................................................................................................... 61  5.2.1   Material Selection & Processing ...................................................................................... 61  

vi

5.2.2   Computed Tomography Scanning Conditions ................................................................. 62  

5.2.3   Physical Properties ........................................................................................................... 63  5.2.4   Data Analysis ................................................................................................................... 63  

5.3   RESULTS .................................................................................................................................. 64  5.3.1   CT Number and Material Property Measurements .......................................................... 64  

5.3.2   CT Number Comparison .................................................................................................. 66  

5.3.3   Material Property Comparisons ....................................................................................... 66  5.3.4   Linear Attenuation Coefficient to CT number Comparisons ........................................... 68  

5.4   DISCUSSION ............................................................................................................................. 71  

5.4.1   CT Number Comparison .................................................................................................. 71  5.4.2   Material Property Comparisons – Linear Attenuation Coefficient .................................. 72  

5.5   SUMMARY ................................................................................................................................ 74  

CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS .................................................... 75  

6.1   CONCLUSION ............................................................................................................................ 75  6.2   LIMITATIONS ............................................................................................................................ 77  6.3   RECOMMENDATIONS ............................................................................................................... 78  

BIBLIOGRAPHY ............................................................................................................................... 81  

vii

FIGURE 1–1. RANGES OF CT NUMBER DESIRED FOR CARDIAC CT PHANTOM FABRICATION. ....................... 4

FIGURE 2–1. FLOWCHART ILLUSTRATING THE EFFECT OF MATERIAL PROPERTIES ON CT NUMBER

THROUGH ELECTRON DENSITY AND ATTENUATION COEFFICIENT. EXPLANATION DETAILING THE

METHOD OF CALCULATION AND SUBSCRIPTS ARE PRESENTED IN SECTION 2.2.2. ............................... 8

FIGURE 3–1. SCHEMATIC REPRESENTATION OF THE COMPOUNDING PROCESSING TECHNIQUE UTILIZED

TO FABRICATE THE POLYMERIC COMPOSITES. .................................................................................... 27

FIGURE 3–2. RELATIONSHIP BETWEEN CT NUMBER AND HA% FOR COMPOSITE SAMPLES AT 100KVP.

HA WAS ADDED AT 0, 2.5, 5, 10, 15, 20, AND 25 WEIGHT PERCENT FOR PE AND TPU AND 0, 2.5,

5, AND 20 WEIGHT PERCENT FOR PVDF. ............................................................................................ 31

FIGURE 3–3. COMPARISON OF TPU/HA AND PE/HA COMPOSITES IN TERMS OF MEASURED CT

NUMBER AND LINEAR ATTENUATION COEFFICIENT COMPARED TO THE BILINEAR TREND EXHIBITED

BY THE GAMMEX 467 PHANTOM WITH VALUES OBTAINED AT 100KVP. 35

FIGURE 4–1. FOAMING TECHNIQUE SETUP WITH REFERENCE TO JACOBS ET AL. [112] BUT

CONTROLLED FOR OUR PURPOSES OF FOAMING TPU. ........................................................................ 43

FIGURE 4–2. CT SCANNING ARRANGEMENT OF TPU FOAM SETS A THROUGH E. INCLUDED ARE

TPU100 SAMPLES ACTING AS PURE TPU FOR REFERENCE DURING SCAN. LABELING FROM A

THROUGH E REPRESENT TPU SETS A THROUGH E RESPECTIVELY. ................................................... 46

FIGURE 4–3. COMPARISON OF TPU FOAM SETS FOR CHANGING KVP VALUES TO CT NUMBER. ................ 49

FIGURE 4–4. RELATIONSHIP EXHIBITED BETWEEN CT NUMBER (HU) AND POROSITY (%), CELL

DENSITY (CELLS/µM2), AND AVERAGE CELL SIZE. AN INCREASE IN POROSITY, CELL DENSITY AND

AVERAGE CELL SIZE ALL SAW GENERAL TRENDS OF DECREASING CT NUMBER. THE POINTS

REPRESENT THE MEAN WHERE AS THE ERROR BARS REPRESENT THE MAXIMUM AND MINIMUM

MEASURED CT VALUES ...................................................................................................................... 51

FIGURE 5–1. TREND ANALYSIS AND COMPARISON BETWEEN CT NUMBER AND ITS CORRESPONDING

CALCULATED LINEAR ATTENUATION COEFFICIENT OF SCANS COMPLETED IN A WATER BATH

(YW), SALINE SOLUTION (YS), AND AIR ENVIRONMENTS (YA) AT 80KVP. ........................................ 69

List of Figures

viii

FIGURE 5–2. TREND ANALYSIS AND COMPARISON BETWEEN CT NUMBER AND ITS CORRESPONDING

CALCULATED LINEAR ATTENUATION COEFFICIENT OF SCANS COMPLETED IN A WATER BATH

(YW), SALINE SOLUTION (YS), AND AIR ENVIRONMENTS (YA) AT 100 KVP ...................................... 69

FIGURE 5–3. TREND ANALYSIS AND COMPARISON BETWEEN CT NUMBER AND ITS CORRESPONDING

CALCULATED LINEAR ATTENUATION COEFFICIENT OF SCANS COMPLETED IN A WATER BATH

(YW), SALINE SOLUTION (YS), AND AIR ENVIRONMENTS (YA) AT 80KVP. ........................................ 70

FIGURE 5–4. TREND ANALYSIS AND COMPARISON BETWEEN CT NUMBER AND ITS CORRESPONDING

CALCULATED LINEAR ATTENUATION COEFFICIENT OF SCANS COMPLETED IN A WATER BATH

(YW), SALINE SOLUTION (YS), AND AIR ENVIRONMENTS (YA) AT 80KVP. ........................................ 70  

ix

TABLE 2-1. LIST OF ATTENUATION COEFFICIENT CONSTANT VALUES FOR A, B, M, N, K, AND L AS

NOTED BY WATANABE ET AL. [10] ..................................................................................................... 11

TABLE 2-2. SUMMARY OF MEASURED CT NUMBERS UTILIZING 80, 100, 120 AND 135KVPS.

FURTHERMORE, ILLUSTRATED ARE KEY MATERIAL PROPERTIES: MASS DENSITY, ZT AND ZR. ......... 19

TABLE 3-1. CT NUMBER VALUES OF PE, TPU, & PVDF COMPOSITES AT 80, 100, 120 AND 135KVP. ....... 30

TABLE 3-2. MATERIAL PROPERTIES OF FABRICATED PE, TPU & PVDF COMPOSITES OF EFFECTIVE

ATOMIC NUMBERS CALCULATED BY EQN. 3 WITH M AND N VALUES AT 3.4 AND 1.5 FOR ZT AND

ZR RESPECTIVELY. DENSITY IS CALCULATED BY MEANS OF EQN. 6. .................................................. 32

TABLE 3-3. ELEMENTAL COMPOSITION OF VARIED PE, TPU AND PVDF COMPOSITES ORDERED BY

ADDED HA WEIGHT PERCENT. ............................................................................................................. 33

TABLE 3-4. COMPARISON OF LINEAR ATTENUATION COEFFICIENT VALUES (µ) THROUGH A MATERIALS

AND CT SCAN APPROACH AS CALCULATED BY EQUATIONS (1) AND (2). MEASUREMENTS WERE

TAKEN AT 100KVP. ............................................................................................................................. 34

TABLE 4-1. PURE TPU MATERIAL PROPERTIES ILLUSTRATED AS ATOMIC COMPOSITION AND PHYSICAL

PROPERTIES. ........................................................................................................................................ 44

TABLE 4-2. OUTLINE OF PROCESSING TECHNIQUE UTILIZED FOR TPU FOAM SETS A THROUGH E. THE

VARIATION IN PROCESSING CONDITION IS BASED UPON THE LENGTH OF TIME UPON WHICH THE

TPU SAMPLES ARE INTRODUCED INTO THE HEATED WATER BATH. ................................................... 47

TABLE 4-3. CT NUMBER VALUES MEASURED IN HOUNSFIELD UNITS (HU) FOR SOLID TPU AND

SUBSEQUENT TPU FOAM SAMPLES. ADDITIONAL DATA OF MEASURED DENSITY THROUGH THE

ASTM STANDARD .............................................................................................................................. 50

TABLE 5-1. WEIGHT PERCENTAGES OF H, C, N, O, F, P, AND CA WITHIN POLYMERS AND POLYMERIC

COMPOSITES FABRICATED FOR CT PHANTOM STUDIES. MEASURED DENSITIES ARE ILLUSTRATED

FOR EACH POLYMERIC COMPOSITES. EFFECTIVE ATOMIC NUMBERS (ZƮ AND ZR) AS WELL AS

RELATIVE ΡE WERE CALCULATED BY MEANS OF EQUATIONS (2) THROUGH (5). ................................. 65

List of Tables

x

TABLE 5-2. COMPARISON OF MEASURED CT NUMBERS FOR THE SAME POLYMERIC COMPOSITES

MEASURED UNDER SEPARATE CONDITIONS OF AIR, SALINE SOLUTION AND WATER BATH

ENVIRONMENTS. ................................................................................................................................. 65

TABLE 5-3. COMPARISON OF DIFFERENCES IN CT NUMBER BETWEEN THE REFERENCED WATER BATH

MEASUREMENTS AND SALINE SOLUTION OR AIR. DIFFERENCES IN CT NUMBER ARE

ILLUSTRATED FOR ALL POLYMERIC COMPOSITE CT PHANTOMS FABRICATED AT 80, 100, 120

AND 135KVPS. .................................................................................................................................... 66

TABLE 5-4. COMPARISON OF ASSESSED EEFF VALUES THROUGH WATER BATH, SALINE SOLUTION AND

AIR ENVIRONMENTS AT 80, 100, 120, AND 135 KVPS. ........................................................................ 67

TABLE 5-5. COMPARISON OF U VALUES AS CALCULATED THROUGH EQUATION (2). COMPARISONS ARE

MADE AS A % DEVIATION FROM WATER BATH ENVIRONMENT SCANS AND IS CONDUCTED FOR

BOTH SALINE SOLUTION AND AIR ENVIRONMENTS AT 80, 100, 120 AND 135KVPS. .......................... 68

1

Cardiovascular disease is one of the leading causes for mortality within Canada, most

prominently with respect to coronary artery disease (CAD) [1]. The current gold standards

for CAD diagnostic, including catheter coronary angiography and intra-vascular ultrasound

are limited in imaging capability, invasive, and time intensive [2], [3].

Recently, Computed Tomography Coronary Angiography (CTCA) has been noted as

an effective imaging technique to confirm CAD in patients with minimal to moderate disease

[4], [5]. However, CTCA is still limited in such that there is still the need for a more accurate

evaluation of well established and advanced coronary disease evaluation [4], [6]. To improve

detection of CAD through CTCA in those patients, CT limitations such as resolution, post

processing, and calcified plaque characterization must be addressed through the optimization

of image acquisition parameters and post-processing techniques [4]–[7].

Chapter 1 Introduction

1.1 Preamble

2

To overcome these limitations, CT medical phantom devices are leveraged. A CT

phantom acts as an anthropomorphic, or life-like, model that can be used as a consistent

testing method to introduce new imaging techniques and algorithms to improve CT imaging

techniques. This eliminates the need for human tissue and with the control and understanding

of material properties; the CT properties can be tailored to match that of the desired tissue or

system [8].

Current commercial CT medical phantoms, including the Gammex 467 phantom – a

common utilized phantom product, are listed per tissue, however are limited in tissue variety

and specificity. Many systems, particularly that of the cardiac system, require a wide range of

tissue properties and are exhibited as such under the CT scanner. Coronary plaque is a key

example of variation that, under the CT scanner, requires a high level of accuracy and could

potentially have a wide range of CT property variation due to various types of plaque and

levels of plaque calcification.

The main points of motivation are listed as follows:

1. To overcome the mortality of CTCA, improved scanning techniques are necessitated,

in which CT medical phantoms act as a synthetic means for modeling purposes

2. Current commercial CT phantoms are costly, do not illustrate a consistent means of

fabrication method, and lack tissue variety and specificity

3. The focus can be aimed towards the cardiac system, as its complexity and variation in

CT properties necessitates CT phantoms with these features

1.2 Thesis Objectives

3

The overall objective of this research is to detail polymeric fabrication techniques for

the development of CT phantom devices. The novelty and main contribution of this work

towards the field of CT imaging and materials engineering is describing the fine-tuning of

CT number by means of selected polymeric fabrication techniques. We aim to create a bridge

between materials and clinical engineering specifically to improve CT imaging techniques by

means of CT phantom fabrication. Current CT phantoms are limited to commercial means or

have described studies to which materials are roughly selected to match the need of the

specified study.

From this, we aim to have CT phantoms can be fabricated with controlled CT

properties thus accurately mimicking any tissue under CT scanning. The specific aim is to

mimic the various constituents around the coronary artery under CT imaging techniques. To

do so, the material must attenuate identically to that of the desired constituent. Attenuation is

measured, through the CT scanner, by means of a loss of x-ray energy due to electron scatter

or photoelectric absorption and is represented most commonly quantitatively as its CT

number in Hounsfield units (HU) [9], [10]. This value is a function of primarily the electron

density, or mass density, and effective atomic numbers of the material being scanned [9]–

[11]. Therefore, for the phantom, these material properties must be controlled to attenuate

with likeness to that of the native coronary artery. As such synthetic polymers are ideal

materials for phantom device fabrication due to their varying electron densities with diverse

compositions of molecular weights, mass densities, and effective atomic numbers [12].

Polymers can also undergo highly controlled material processing conditions, allowing for the

fine-tuning of attenuation values as well as creating consistent conditions for product

fabrication [12]. Studies have continued to be developed based upon polymer composites for

4

CT phantom purposes particularly the use of hydroxyapatite (hA) [4], [13], [14].

Additionally, foamed CT phantom are also often considered for low density tissue mimics

and is, thus, also considered as a key fabrication process potential for CT phantom use [15].

The three key objectives for the research involve the following:

1. To fabricate polymeric composites through controlled twin screw compounding

technique thus creating ranged but controlled properties capable of mimicking mid to

high density cardiac and plaque components  

2. To fabricate polymeric cellular foams through a batch foaming process thus creating

ranged but controlled properties capable of mimicking low density cardiac and

adipose tissue environments  

3. To investigate ideal scanning methods and environments for said fabricated

polymeric CT phantom devices through the modeling of material properties to the CT

scanner characteristic CT number  

With reference to CT numbers, the desired goal is ranged to mimic: adipose / fatty plaque (-

300 to -50HU), soft tissues (0 to 100HU), and finally ranges and variations of coronary

plaque (0 to 1000HU).

Figure 1–1. Ranges of CT number desired for cardiac CT phantom fabrication.

5

The body of this thesis is comprised of four chapters followed by concluding remarks and

suggestions for further research. A background a literature review is presented in Chapter 2

which illustrates: the physical and mathematical concepts behind CT imaging, the numerical

goals desired for cardiac tissue imaging, compilation of CT phantom studies, and the

fabrication concepts and techniques behind both polymeric composites and cellular foams.

Chapter 3 and 4 then outline the fabrication and characterization of polymeric composite and

polymeric cellular foam CT phantoms respectively. Chapter 3 describes specifically the

effect of hydroxyapatite (hA) as an additive to select polymers as a parametric study

considering mass density, atomic composition, and CT numbers. Chapter 4 then describes the

effect of select parameters controlling polymeric cellular foaming with a comparison

between mass density, cell properties and CT numbers. Chapter 5 then presents studies

concerning the scanning conditions and environments ideal for fabricated CT phantoms. A

comparison between scans completed in the typically used water bath is contrasted against

enclosed saline solution and air environments. The studies are compared such that Chapter 3

and 4 relate to the CT control to range between the CT numbers necessary for a cardiac

system. Chapter 5 then studies the samples fabricated within studies 3 and 4 to determine an

alternative scanning media that can accurately scan these fabricated phantoms, necessary for

cardiac CT phantom scanning. Lastly, the conclusion presents an overview of complete

research and current studies, as well as suggestions for continuation in this field of study.

1.3 Thesis Organization

6

As the purpose and motivation of this research is aimed towards the fabrication,

characterization and analysis of CT medical phantoms, it is pertinent to define concepts

involved with such. This section, first, introduces a definition of CT phantoms and its

mechanism with respect to the CT scanner. Continuing from this, a numerical methodology

is introduced where properties expected to affect the CT number are defined. This includes

defining key concepts involved in CT scanning including Hounsfield Units (HU),

attenuation, electron density, effective atomic number and effective energy. Continuing from

definitions and key concepts, the selected materials and fabrication technique and

mechanisms behind such are illustrated. Polymeric bases suitable for CT phantom processing

are introduced together with the additive necessary for composite processing. Both polymer

composite processing through twin screw compounding and foam processing are introduced

as key methods to which the CT properties may be controlled. Lastly, included is a section

illustrating current studies within the field to which an extended motivation to this research

can apply to.

Chapter 2 Background and Literature Survey

7

As such, this review aims to review the topics of CT scanning, medical imaging

phantoms and its implementation with respect to the coronary artery. Furthermore, this

review will present and assess recent studies relating to mathematical modeling techniques

for CT scanning and polymer processing techniques, both of which will be integral in

justifying the approach for the present CT coronary artery phantom study.

In terms of medical imaging, a CT scanner is an x-ray based radiological device which will

take specified cross sections of a given element and will produce 2D slice images which can

further be stacked to create the 3D element volume. Each slice is represented as an image

based upon measured x-ray attenuation by the CT scanner per pixel. This value, in turn, can

be represented then as a CT number that is roughly the average relative linear attenuation of a

voxel, or a volume element pixel. This CT number scale, which will be explained further in

the following sections, is a relative value measured in Hounsfield units (HU) and is based

upon water having a CT number of 0 HU [16], [17].

CT phantoms are an ideal anthropomorphic device that will mimic the desired tissue

based upon its CT properties which is mainly that of its CT number. They can be leveraged

in place of live or cadaveric tissue for calibration and imaging studies. Since any native

human tissue is limited in resource and, depending on the tissue, can carry inconsistencies

between individuals, having an imaging phantom can be beneficial for a variety of purposes.

Device calibration, theoretical testing and staff training may require phantom devices with a

high level of accuracy compared to that of native tissue. With this mind, the phantom must

then be identified as anthropomorphic to that of the native tissue, with respect to the imaging

2.1 Computed Tomography Medical Imaging Phantoms

8

process used. In terms of CT scanning, the phantom should represent the native tissue by

means of primarily electron density and mass density, both of which relating to the

attenuation coefficient. This is further detailed in the following sections [18]–[20].

The overall objective of this research is to fabricate phantom devices with the ability to

mimic the various constituents around the coronary artery under CT imaging techniques. To

do so, the material must attenuate identically to that of the desired constituent. Attenuation is

measured, through the CT scanner, by means of a loss of x-ray energy due to electron scatter

or photoelectric absorption and is represented most commonly quantitatively as its CT

number in Hounsfield units (HU) [9], [10]. This value is a function of primarily the mass

density and electron densities of the material [11]. As such the following flow chart

illustrates the modeling process in which the materials properties can be correlated to CT

number through a number of formulaic interpretations.

Figure 2–1. Flowchart illustrating the effect of material properties on CT number through electron density and attenuation coefficient. Explanation detailing the method of calculation and subscripts are presented in Section 2.2.2.

2.2 Computed Tomography Mathematical Modeling

9

Figure 2-1 is a flow chart describing how to ascertain a prediction of the CT number

from basic materials properties. Formulae were used in its simplest forms based primarily on

related studies [21], [22]. Here, within Ng, NA refers to Avogadro’s number, MWTot the total

molecular weight, ni the number of ith atoms/molecule and Zi being its corresponding atomic

number. Within the electron density formulae, ρ is the mass density and Ng,w being the

electrons/gram of water. Within the attenuation coefficient formula, µw refers to the relative

linear attenuation coefficient with respect to water while µw refers to the linear attenuation

coefficient of water. As will be described in the subsequent sections, added complexity will

be incorporated based upon scanner conditions and material choice.

Given the desired coronary artery and its environment, an appropriate range of CT numbers

would be between -300 to +700 HU which would encompass fully that of adipose tissue,

fatty plaque, vulnerable plaque and various levels of calcified plaque [23], [24]. Included is

the control of CT numbers at discrete CT tube voltages. Varying the tube voltage, or photon

energy, can cause the phantom to attenuate at different values [25]. As such discrete tube

voltages at which CT imaging is typically done are considered [10]. With select polymeric

systems, this range of appropriate CT numbers for the coronary artery environment can be

obtained. Given the control of relevant material properties, most importantly that of mass

density and effective atomic numbers, desired CT numbers can be obtained.

The CT number is primarily based upon the average relative linear attenuation of a voxel at a

specified x-ray energy level. Essentially, a CT number takes a relative value, based upon the

2.2.1 Computed Tomography Numbers - Hounsfield Units

2.2.2 Attenuation Coefficient & Electron Density

10

attenuation of water. Attenuation is a value based upon its attenuation coefficient which,

through specific material properties, will change. The material properties we are concerned

with are primarily electron density and mass density, both of which can affect how the

material attenuates from the exposed x-ray [10], [16].

In theory CT numbers can be derived through attenuation coefficient numerically

from a set of equations as originally described by McCullough et al. [21] and also by

Rutherford et al. [26]. Firstly, CT numbers can be determined through a ratio between linear

attenuation coefficients of the scanned material and water:

𝐶𝑇  𝑁𝑢𝑚𝑏𝑒𝑟 =  1000  𝜇 − 𝜇!𝜇!

(Eqn. 1)

where the CT number is measured in Hounsfield Units (HU) and µ and µw are the linear

attenuation coefficients of the material being scanned and water respectively. Watanabe et al.

[10] described how the linear attenuation coefficient (µ) can be described in terms of the sum

of photon effects of photoelectric absorption, coherent (Rayleigh) scattering and incoherent

(Compton) scattering terms. Attenuation coefficients are typically described by both mass

and linear attenuation coefficients with the latter having material mass density incorporated.

In its simplest form, linear attenuation coefficients can be almost proportional to the electron

density, which therefore allows many to consider using relative electron densities to calculate

the CT numbers. However, as described in equation 2, the linear attenuation coefficient

theoretically considers small values based upon x-ray – material interactions which can affect

the linear attenuation coefficient [10], [27]:

11

𝜇(𝐸) = 𝜌! 𝑎𝑍!!

𝐸! + 𝑏𝑍!!

𝐸! + 𝑐(𝐸) (Eqn. 2)

E refers to the energy in keV. To calculate the linear attenuation coefficient as such,

the energy value in keV relative to the tube voltage applied is needed. Therefore, based on

the tube voltage applied (80, 100, 120 and 135kVp), a non-linear regression analysis was

completed through the SigmaPlot program (SPSS Inc., Chicago, IL, USA) between CT

number and the material properties to find the effective energy value in keV. Then a, b, m, n,

k, and l are fitting constants defined which give the best fit to the actual attenuation. Initial

estimates of constants and further iterations through non-linear regression modeling will

determine the values and can vary by means of the number of iterations done and initial

estimates chosen. ZT and ZR are the effective atomic numbers for photoelectric absorption

and coherent scattering respectively and are defined as:

𝑍!,! = 𝑛!𝑍!!,!! !,!

(Eqn. 3)

where ni and Zi are the electron fraction and atomic number respectively of the ith element

being described. A study conducted by Watanabe et al.[10] compares various studies

including that of McCullough et al. [21] and Rutherford et al. [26] that outline the most

accurate approach to determining the fitting constants through an in-depth phantom analysis.

Table 2-1. List of attenuation coefficient constant values for a, b, m, n, k, and l as noted by Watanabe et al. [10]

Variable Value a 2.30E-23 b 1.70E-24 m 3.40 n 1.50 k 3.20 l 1.60

12

The c(E) term described in equation (2) is the Compton scattering effect and can be

described mathematically by means of the Klein-Nishina formula:

𝑐(𝐸) =8𝜋3 𝑟!! 1− 2

𝐸𝑚!𝑐!

+ 5.2𝐸

𝑚!𝑐!!

− 13.3𝐸

𝑚!𝑐!!

(Eqn. 4)

where me and c are the electron mass and velocity of light respectively and ro = 2.818*10-13

cm. Lastly, ρe as described in equation (2) is the electron density of the material. This can be

numerically determined by equation (5):

𝜌! = 𝜌  𝑁!   𝑛!𝑍!𝑀𝑊!"#

(Eqn. 5)

where ρ is the mass density, NA is Avogadro’s number at 6.02*10-23, ni and Zi are the

electron fraction and atomic number of the ith element respectively, and MWTot being the

total molecular weight. These formulae encompass the relationship between key material

properties and CT number.

The database used which has compiled the photon effects or ‘photon cross-sections’

is known as the XCOM: NIST database [28] which has considered both theoretical and

experimental work to create a wide energy and atomic number ranged database of mass

attenuation coefficients [29]–[32]. The database has also considered mixtures and

compounds at which all of the effects are taken into consideration.

The measured HU of a given element can vary significantly with changing x-ray energies. By

revisiting equation 2, it can be seen that altering the x-ray energy will affect the various

photon cross-sections but in varied ways. However, also referenced to equation 2, the extent

2.2.3 Effective Photon Energy & Atomic Number

13

to which x-ray energy will affect the element is highly dependent on the effective atomic

number of the material as well.

An example of a study related to this was conducted by Ebert et al. [33] where

experiments were conducted on a CIRS phantom containing various electron density plugs.

CT scans were taken at discrete x-ray energies of 80, 100, 120, 140 kV. With increasing

electron density we will see an increase in atomic number and, thus, have taken this into

consideration for the following trend.

The notable trend is that with as energy increases, the CT numbers tend to decrease at

high electron densities. As well, this decrease in CT number with increasing energy becomes

more prominent as electron density increases. However, there is a notable trend occurrence at

lower electron densities.

Below a relative electron density of 1.0, the CT number values tend to marginally

increase with increasing energies. However at relative electron densities above 1.0, the trend

alters to that of decreasing CT number with increasing energies. This phenomenon can

further be described by means of NIST compiled data.

Multiple studies have described a phenomenon in modeling the linear attenuation coefficient

against the CT number in which a bilinear relationship is illustrated. Typically, an analysis

can be made to calculate the linear attenuation coefficient (µ) by experimentally finding the

CT number. Equation 1 illustrates the formula to do so, however, by collected data, it has

been studied that this equation is only representative for a CT number roughly less than 0

HU.

2.2.4 Bilinear Scaling

14

In a study conducted by Bai et al. [34] and further illustrated by Saw et al. [35] and Brown et

al. [36], materials can be described as belonging in two distinct regions. Below an HU of 0, a

‘water-air assumption’ is made in which the trend between linear attenuation coefficient and

CT number is as in equation 1. Above an HU of 0, a ‘water-bone assumption’ is made in

which the reference taken is not between that of air and water but now water and that of

highly attenuating bone. The basis of this work, conducted by Blankespoor et al. [37]

considered a piecewise bilinear fitting technique was utilized to fit the attenuation against CT

number to a best fit. All of these studies have experimentally illustrated the bilinear

relationship exhibited between linear attenuation coefficient and CT number. This

phenomenon can be described through a number of different effects.

The first potential effect may be described through the act of beam hardening of the

incident polychromatic CT x-ray beam. Beam hardening is the effect of high-density

materials absorbing lower energy beams.

Another possible effect has noted that CT images can accurately estimate the

attenuation coefficients for muscle and soft tissue, however as the tissue density increases

towards that of bone, larger margins of error are noticed. As such, with higher density

tissues, there is an inaccurate measure of attenuation coefficients from CT scans [38]. To

further emphasize this point, Kinahan et al. [39]–[41] have described how the mass

attenuation coefficients of water and most soft tissues are relatively similar.

This can be attributed to equation 2 with density removed. The basis of mass

attenuation coefficients is the atomic number related to its photon effect and thus, we can

stipulate that the effective atomic number is similar between water and most soft tissues. On

15

the contrary, it can be said other tissues, most importantly bone, will have a noticeably high

mass attenuation coefficient. Since soft tissue is organized primarily of organic elements and

bone is, alternatively, composed primarily of calcium based molecules the effective atomic

number of bone will be significantly higher, thus having a much higher mass attenuation

coefficient than soft tissue or water [42].

Also to be noted is the effect of increasing photon energy on the bilinear trend. At

high photon energies, as described above, the mass attenuation coefficient of bone reaches

the same values as that of muscle and air. This can also be referenced back to equation 2 and

it can be noted that the photon effects of photoelectric absorption and coherent scattering

become negligible, thus the mass attenuation coefficient becoming dominated by the

incoherent scattering effect.

Further studies conducted have begun to describe a tri-linear scaling in which CT

numbers higher than that of 1000 HU belong to a different scaling relation between linear

attenuation coefficient and CT number [43], [44]. Again, a similar principle applies to the

bilinear relationship; however, an added region is necessary due to the CT numbers

extending higher than the previously attenuating bone tissue.

The materials proposed for the phantom device must be controlled to attenuate with likeness

to that of the native coronary artery. As such synthetic polymers are ideal materials for

phantom device fabrication due to their varying electron densities with diverse compositions

of molecular weights, mass densities, and effective atomic numbers [12]. Polymers can also

undergo highly controlled material processing conditions, allowing for the fine-tuning of

2.3 Material Fabrication

16

attenuation values as well as creating consistent conditions for product fabrication [12].

Furthermore, the use of polymeric materials allows for a high degree of property consistency

and long life stability of material thus allowing for the samples to undergo repeated

consistent scans. A limitation may involve degradation at high temperatures or ultraviolet

(UV) environments, however in a clinical setting we expect this to be minimal at best.

To adequately mimic the entire range of CT numbers necessary for the coronary artery and

its environments, a wide array of phantoms must be fabricated. Further, the phantoms must

be controlled by its elemental composition with respect to the previous section. Polymer

composites serve as the ideal classification of materials due to its fabrication process control,

composition control, and a number of different choices of polymer and composite

combinations. The latter point requires a look at current phantom devices and its material

composition, thus providing a basis on which composites may be most beneficial.

Depending on the desired tissue to be mimicked, the phantom material chosen can

vary. Bone, other mineralized tissues and calcified plaque tend to be composed of calcium

carbonate (CaCO3) or other calcium based products such as hA [10], [18], [42]. These

materials will tend to attenuate with a CT number roughly around 500 to 1500 HU depending

on its density and photon energy used. Soft tissues at higher densities, such as the artery wall

have been mimicked by a variety of polymeric materials due to its low attenuation. The CT

number rests between 0 and 200 depending again on density and photon energy. Typical

polymers used and studied as phantoms have been poly(methyl methacrylate) (PMMA),

polyurethane (PU), acrylonitrile butadiene styrene (ABS) or polyethylene (PE) [42], [45].

2.3.1 Polymeric Composite Materials

17

Lastly, to mimic the various adipose low density soft tissue surrounding the arteries,

polymethysilane (PMS), polyethylene (PE), and nylon, as examples, have been studied [42],

[46], [47].

To further fine tune the attenuation to a desired HU value together with a consistent

polymer base, a polymer composite can be fabricated. Polymer composites for biomedical

applications, or biocomposites, have become common place and are often sought out for

studies for varied applications such as implants and tissue scaffolds [48]. As such it is

pertinent to understand how to properly fabricate polymer composites for its given purpose.

One of the main concerns of polymer-composites is its characteristic consistency. The

additives, when blended with the polymer, based on the application will likely benefit most if

the additives are well or uniformly dispersed within the polymer matrix. A number of studies

have been conducted on the use of a twin screw compounder to uniformly disperse nano-

particles within a polymeric matrix [49]–[52]. Pertaining to our research, it has been

demonstrated that polymer-clay nanocomposites can adequately compound with certain

polymers such as PMMA, PU, and PE [53]–[55], as well as carbon nanotubes with

poly(lactic acid) (PLA) [49].

To further control the CT number of the element by material properties, microcellular

processing can prove to be highly beneficial. Microcellular processing essentially can create

a foam from a polymeric based material. This condition allows for a porous structure to be

fabricated, thus decreasing the overall density of the material. By this, a polymer can be

2.3.2 Microcellular Foam Materials

18

controlled to have a lower CT number by means of its given density depending on the

porosity of the sample.

There are a variety of methods to fabricate a microcellular network from a polymer or

polymer composite. Some methods include additives such as chemical blowing agents which

are compounded with the polymer and further degraded so that they may enter a volatile state

thus escaping the matrix and leaving pores. However, most beneficial to our studies, we

considered batch foaming processes with a physical blowing agent [56]–[59].

The use of physical blowing agents, as opposed to chemical blowing agents, typically

leads to a greater degree of porosity and potentially more consistency with controlled

foaming conditions [60]. The process behind the use of physical blowing agents, typically

that of nitrogen (N2) or oxygen (O2) gas is exposing the polymeric sample to a supercritical

pressure of that gas. At this stage, the gas interacts with the polymer as a liquid and thus can

diffuse into the polymer. Depending on the polymer, the amount of time it takes for the gas

to saturate within the sample will vary. Additionally, depending also on the supercritical gas

used, the saturation point will vary. An ideal amount of time must be assessed based on the

polymer and gas used which is derived experimentally through material diffusivity.

The other main factor to consider for batch foaming with physical blowing agents is

the pressure drop threshold. The rate at which the pressure drop occurs from its supercritical

state to standard pressure will significantly affect the cell structure that is produced. What we

aim to create is a thermodynamic instability in which cell nucleation will occur by means of

this rapid pressure drop [61].

19

To better define the standard of current commercial CT phantoms, a commercial and mature

CT phantom available is the Gammex 467. This phantom mimics a variety of different

tissues through cylindrical inserts interchangeable within a PMMA block. Listed below are

the measured CT numbers of the inserts along with material properties: mass density, ZƮ and

ZR.

Table 2-2. Summary of measured CT numbers utilizing 80, 100, 120 and 135kVps. Furthermore, illustrated are key material properties: mass density, ZT and ZR.

Sample CT Number (HU) Density

Measured (g/cm3)

ZT ZR 80kVp 100kVp 120kVp 135kVp

LN-300 -717.3 -712.3 -710.0 -708.5 0.294 7.60 6.61 LN-450 -571.6 -567.4 -565.6 -562.8 0.454 7.60 6.61 Adipose -104.9 -92.2 -85.8 -83.3 0.956 6.23 5.74 Breast -47.8 -41.0 -37.9 -36.3 0.998 7.00 5.99 Solid Water 2.7 0.3 -1.6 -2.8 1.025 7.83 6.32 Brain 4.4 12.3 18.4 21.6 1.060 6.11 5.59 True Water 0.3 0.9 1.3 0.6 1.000 7.49 6.95 Liver 89.3 82.5 80.3 78.7 1.114 7.84 6.32 Inner Bone 359.0 261.2 215.5 194.1 1.160 10.55 7.94 B-200 379.5 283.0 233.8 210.2 1.172 10.56 7.94 CB2 - 30% 771.7 624.7 535.4 485.4 1.342 11.04 8.19 CB2 - 50% 1473.6 1185.9 1014.7 913.7 1.565 12.67 9.71 Cortical Bone 2169.7 1759.3 1506.8 1360.5 1.829 13.76 10.92 Polypropylene -115.2 -100.2 -92.1 -88.1 0.905 5.52 5.03

2.4 Computed Tomography Phantom Studies

2.4.1 Gammex 467 – Commercial CT Phantom

2.4.2 Polymeric Phantom Design

20

It can be noted that with the processing techniques for fabrication both microcellular and

composite materials, it is possible to create CT phantoms highly anthropomorphic to the

desired tissue. As a reference, previous analyses have illustrated that PE and TPU will have

CT numbers of approximately -100 and 100 HU respectively. Furthermore, the calcium

based hA tends to mimic bone at around 1000 HU depending on the density and

concentration. As such, it can be stipulated that with discrete additions of hA to the given

base polymers, we can fabricate a phantom of finely tuned CT number between the CT

numbers of the base polymer and hA. Furthermore, by microcellular processing, the material

can decrease its density while also introducing voids not discernible to the CT scanner. As

such the CT number can be decreased to that close to air at -1000 HU while still seemingly

having a homogenous cross sectional area, as seen by the CT scanner.

There have been studies that focus on varied concentrations of hA for the assessment

of beam hardening. Since increasing hA will lead to a higher atomic number element and,

thus, will likely affect the photon interactions that occur. Deurerling et al. [13] considered the

fabrication of HDPE-hA composite phantoms with varied concentrations of hA. The

measurements of x-ray attenuation based on micro-CT measurements were analyzed. It was

evident that from the polymer-HA phantom, that increasing amounts of hA revealed a non-

linear trend with linear attenuation coefficient. As mentioned, the reason behind the non-

linearity was like beam hardening and scattering artifacts [62]–[65]. Also noted was that

following the scan a beam hardening correction algorithm was applied from the

manufacturer, however the extent to which beam hardening seems to occur at high atomic

number elements can overtake this correction.

21

Polymers and various polymeric composites have been commonly utilized as CT phantoms

due to their manufacturability and potential for wide ranged material properties [9], [66]–

[69]. However, current phantom devices are specific to the desired tissue whereas some

studies aiming to improve CT medical imaging necessitate the control of the phantom CT

characteristics to a fine degree [10], [70]. Certain tissues and structures within the human

body may differ and require a fine degree of CT number control to be anthropomorphic.

Coronary plaque is an example of such, which has ranging CT number values based upon its

composition and degree of calcification. Furthermore, the geometrical shape of these

phantoms would benefit by mimicking the native structures[4], [6], [66], [71].

In terms of arterial phantom design for CT imaging, several obstacles must be

overcome, leading to multiple studies on such. Typically CT imaging can be conducted on

coronary arteries to detect various types of plaque to determine the patient risk when

considered at low to medium risk [72], [73]. However, this procedure requires that the

processed image has high clinical accuracy, thus certain limitations of the pairing between

CT scanning and the coronary artery must be overcome [74], [75]. Considering that the

lumen opening size can be significantly small, the opening may be as large as a few pixels on

the CT image. Furthermore, with small lumen sizes and highly calcified plaque, image

artifacts may become prominent, enough to exaggerate the size of the plaque. Lastly, the

environment of the artery may have CT numbers which are very close to one another. To this

point, when imaging the arterial fluid is typically dyed with iodine and can cause the CT

numbers of both the arterial plaque and fluid to be equal, thus making any discrepancy

between the two difficult [71], [76].

2.4.3 Coronary Artery Phantom Design

22

To further our understanding on the relationship between CT imaging and the

coronary artery and its environment, coronary artery plaque phantoms and its environment

have been analyzed in multiple studies [4], [67], [77]. An analysis of the various types of

plaque that may exist within the coronary artery under CT imaging is necessary. Continuing

from the study conducted by Toepker et al. [67] with regards to varying percentages of artery

stenosis, research has also been documented for varied plaque types. Firstly, a slight

deviation of this was done with coronary artery stent imaging by Yang et al. [66] by means of

an acrylonitrile-butadiene-styrene resin (ABS) based phantom. The vessels were filled with a

contrast agent attenuating at about 300 HU at 100 and 120 kVp tube voltages. Lastly,

commercially available stents were inserted into the phantom and imaged under CT

scanning. This study provides a basis on image clarity and CT number accuracy in an arterial

environment. Similarly, we can see how a highly attenuating stent (metal alloy or steel based

materials) will affect the arterial CT imaging. Prior studies were also conducted in a similar

manner however with different materials or live patients used. Regardless of the imaging

method, the results trended towards imaging capability with limitations in possible artifacts

caused by the highly attenuating stents [78]–[80].

From this, we can consider a less attenuating, but potentially larger sized coronary

plaque with varied levels of calcification. Typical studies consider both the variation of

plaque calcification and lumen size. One study, conducted by Kristanto et al., however,

considered only non-calcified plaque with varying lumen sizes [5]. As such, varied levels of

lipid content were considered for the plaque. Alternatively, studies have been conducted to

determine the accuracy of varied levels of calcified plaque under CT imaging. One

complication of measuring different levels of calcified plaque is the classification of soft,

23

intermediate and fully calcified plaque. Analyses have been made for elemental compositions

of plaque in different patients, thus giving an estimate of a range of CT numbers to classify

the various types of plaque [24]. Studies were made in relation to this by first classifying the

type of plaque through an intracoronary ultrasound by means of its echoic behaviour. The

stronger the signal, the more dense and calcified the plaque was considered. Following this

CT scan was taken, thus measuring the CT numbers for each given classification [23], [81],

[82].

CT imaging has been considered as an effective method to determine the risk of CAD within

a patient. However, current techniques are limited in a number of ways, including resolution

and radiation dosage. To overcome these obstacles, new post-processing techniques and

analysis tools are necessary. Since cadaveric and live tissue are limited in resource, the use of

an anthropomorphic device, a CT imaging phantom device, is ideal for consistent and

repeated use. As such, it is pertinent that the imaging phantom be as closely mimicking as

possible to the desired tissue.

The targeted region of interest for this study considers the coronary artery, coronary

plaque, and its surroundings. Thus the developed phantom must be able to accurately

illustrate this area through the phantom device. The main property necessary for control to be

anthropomorphic under CT imaging is the CT number measured in HU. Given the photon

energy supplied, the CT number of the part of the device must mimic that of the desired

element. Following this, various artery designs can be constructed based upon varied lumen

sizes and plaque calcification content. Conclusively, with a successful design from a highly

2.5 Summary

24

accurate coronary artery mimicking phantom, countless tools and post-processing techniques

can be developed to better CT coronary imaging techniques.

25

This chapter investigated the fabrication and characterization of polymeric composites for

use in cardiac CT phantom applications. Biomedical phantoms are commonly used for

various medical imaging modalities to improve imaging quality and procedures. Current

biomedical phantoms fabricated commercially are high in cost and limited in its specificity of

human environments and structures that can be mimicked. This study aimed to control the

measurable computed tomography (CT) number in Hounsfield units (HU) through polymeric

biomedical phantom materials using controlled amounts of hydroxyapatite (hA). The purpose

was to fabricate CT phantoms capable of mimicking various coronary plaque types. The CT

number is tunable based on the controlled material properties of electron density and atomic

numbers. Three different polymeric matrices of polyethylene (PE), thermoplastic

polyurethane (TPU) and polyvinylidene fluoride (PVDF) were used together with additions

of hA in mass percentages of 2.5, 5, 10, and 20% hA as well as a 0% hA as a control for each

polymeric material. By adding hA to PE, TPU, and PVDF an increasing trend was exhibited

between CT number and weight percent of hA added in a controlled manner.

Chapter 3 Polymeric Composites for Cardiac CT Phantom Applications

26

Polymers and various polymeric composites have been commonly utilized as CT phantoms

due to their manufacturability and potential for wide ranged material properties [9], [66]–

[69]. However, current phantom devices are specific to the desired tissue whereas some

studies aiming to improve CT medical imaging necessitate the control of the phantom CT

characteristics to a fine degree [10], [70]. Certain tissues and structures within the human

body may differ and require a fine degree of CT number control to be anthropomorphic.

Coronary plaque is an example of such, which has ranging CT number values based upon its

composition and degree of calcification. Furthermore, the geometrical shape of these

phantoms would benefit by mimicking the native structures [4], [6], [66], [71].

This study focuses on the control of CT number through phantom fabrication with a

range of CT numbers necessary for studies for CTCA. Our aim was to mimic the CT

numbers noted by various types and ranges of coronary plaque including soft (non-calcified),

intermediate and calcified plaque in which respective ranges are between -50 to 50 HU, 50 to

150 HU, and any plaque greater than 150 HU up to 800 HU to fully encompass high density

calcified plaque exhibited clinically [4], [6], [20], [67]. Further, the accuracy of these values

between the expected and measured CT numbers have been noted in various studies varying

at most by 40 HU [4], [23], [66].

The three polymers of interest were polyethylene (PE), thermoplastic polyurethane

(TPU), and polyvinylidene fluoride (PVDF) as their density values are varied allowing for

wide ranged CT phantom applications by means of controlled additives. Three polymer types

were selected due to the theoretical density of each polymer, which is expected to directly

relate to its given CT number. PE, TPU, and PVDF each attain a low, medium and high

3.1 Motivation

27

density value as base polymers with theoretical densities at 0.922, 1.2, and 1.78 g/cm3

respectively. Furthermore, all three polymers selected are thermoplastic polymers which are

able to be processed and shaped with ease under heat without degrading the polymeric

structure, however each polymer necessitates different processing temperatures due to higher

glass transition and melting temperatures. The additive used was nano-powdered

hydroxyapatite (hA), which is a commonly used product for phantom products and as well is

comprised of elements highly resembling that of bone tissue under x-ray exposure [13].

Various tissue engineering applications have utilized systems containing a combination of

polymer and hA successfully and thus is the platform for this study [83]–[87].

The method of control is also based upon the fabrication technique of the polymeric

composites. Nano-powdered hA will be introduced to the polymer through twin screw

compounding. This technique allows for the fabrication of a polymeric composite with

uniformly dispersed hA, indiscernible under CT medical imaging due to the particle size

[88]. This method for specific polymer/hA systems has been both examined and utilized for

various biomedical applications [89]–[91].

 

Figure 3–1. Schematic representation of the compounding processing technique utilized to fabricate the polymeric composites.

28

For the purposes of this study, three polymeric matrices – low density polyethylene (LDPE),

thermoplastic polyurethane (TPU), and polyvinylidene fluouride (PVDF) were selected with

hA as the filler, or additive. The polymers selected through a screening process in which

mass density was the main property through which the selection was made. LDPE and PVDF

acted as both the low and higher end of the mass density spectrum of commonly available

polymers. TPU was selected as an average mass density polymer and was selected ahead of

other average mass density polymers through common use of polyurethane as a material for

CT phantoms in commercial and selected study use. Theoretical densities were noted as

0.922, 1.2, and 1.67 g/cm3 for PE, TPU, an PVDF respectively. hA was then selected due to

its high density as an additive, its commonplace documentation as an additive to polymeric

matrices, as well as it being commonly utilized in biomedical studies as bone and calcium

based tissue substitutes.

Low density polyethylene (LDPE; Nova Chemicals Novapol, LC-0522-A), the polyester-

type thermoplastic polyurethane (TPU; Desmopan 385E), and polyvinylidene fluoride

(PVDF; ARKEMA, Kynar 720) were used as the base polymers. Polymers were processed as

received in pellet form. The additive, hydroxyapatite (hA; 677418, Sigma-Aldrich) was a

nanopowder with controlled particle sizing of less than 200nm.

PE and TPU polymers were combined individually with hA at 0, 2.5, 5, 10, 15, 20,

and 25 weight percent of hA. PVDF was combined with hA at 2.5, 5, 10 and 20 weight

3.2 Materials and Methods

3.2.1 Polymeric Matrices & Filler

3.2.2 Phantom Material Processing

29

percent hA. Composite materials underwent microfabrication by means of a 15 mL benchtop

twin screw microcompounder (DSM 15, DSM Xplore). The polymer and hA were added

simultaneously to the rotating twin screw compounder at 130oC, 200oC, 230oC for PE TPU

and PVDF respectively. Combinations were processed at 50 rpm for 15 minutes (100 rpm

and 10 minutes for PE). The extruded polymeric composites were quenched immediately

from the outlet in room temperature water.

This means of mixing creates a homogenous like mixture between two materials;

namely that of the polymer and hA in the case of our phantoms Compounded samples were

then molded into identical disk shaped samples for CT measurement by means of extruding

the polymer, cutting the strand into pellets, and then placing the pellets into a compression

mold (12 Ton Carver Hot Press) by a disc shaped mold (12.8-cm height by 0.65-cm

diameter). The Gammex 467 phantom, used as a comparative measure, was also scanned

under identical acquisition parameters.

The phantoms were measured in a wide volume CT (320 MDCT AquilionONE; Toshiba

Medical Systems, Japan) with CT numbers measured using ImageJ on post-processed

images. The scanning conditions were set at a 0.5mm detector collimation, 0.5 second gantry

rotation time, 50mA nominal tube current and a tube potentials of 80, 100, 120 and 135kVp.

The images were reconstructed using a single filter kernel with 1mm sections at 0.5mm

intervals. Furthermore, the shape and orientation of scans were conducted under a similar

condition to the Gammex 467 phantom to which cylindrical shaped inserts were oriented in a

circular fashion with a surrounding water-equivalent media.

3.2.3 Computed Tomography Number

30

Density measurements were carried out by ASTM Standard D792 – 13 by the measurement

of plastic density through water displacement. The elemental composition was determined

based upon available data sheet information pertaining to each material.

The attenuation of each sample was averaged between 3 measurements per image with 3

images measured per sample and 3 samples per composite measured. As such, per composite

measure, per kVp, 27 measurements we made and averaged as included per CT number. CT

number measurements were made at 4 different kVp values.

Table 3-1. CT number values of PE, TPU, & PVDF composites at 80, 100, 120 and 135kVp.

Polymer hA% CT Number (HU) 80kVp 100kVp 120kVp 135kVp

PE

0.0 -147.4 -124.5 -111.7 -103.5 2.5 -109.8 -91.0 -83.6 -78.8 5.0 -69.6 -61.7 -58.0 -51.4 10.0 34.3 23.7 18.7 9.4 15.0 137.2 109.6 91.2 87.9 20.0 257.6 203.0 171.8 160.3 25.0 376.5 298.9 253.4 240.7

TPU

0.0 80.9 95.1 104.1 110.4 2.5 141.3 145.1 149.1 145.0 5.0 186.4 185.2 182.4 180.9 10.0 328.7 289.9 266.8 253.6 15.0 383.1 328.4 297.2 280.1 20.0 466.4 392.5 346.8 320.3 25.0 537.2 444.0 387.1 352.9

PVDF

0.0 634.0 591.4 551.7 509.6 2.5 669.0 612.1 564.4 541.2 5.0 723.1 642.0 582.0 546.6 10.0 889.1 765.4 689.2 641.0 20.0 1195.7 995.3 869.2 794.1

3.2.4 Physical Properties

3.3 Results

3.3.1 Effect of Hydroxyapatite Content on CT Number

31

The subsequent focus of research was aimed towards 100kVp as a commonly utilized

acquisition tube voltage for a more in-depth analysis. The following trends were exhibited by

the composites for added hA to the polymers. Through data analysis and processing, within

the range of added weight percent of hA to PE, TPU and PVDF, general trends between

weight percent hA and CT number were exhibited. Increasing the amount of hA within each

polymer described an increasing relationship of CT number as described in Figure 3-2.

 

Figure 3–2. Relationship between CT number and hA% for composite samples at 100kVp. hA was added at 0, 2.5, 5, 10, 15, 20, and 25 weight percent for PE and TPU and 0, 2.5, 5, and 20 weight percent for PVDF.

Under the given scan conditions, a representative range of -124.5 to 995.3 HU is

obtained. The overall trend of increasing CT number with increasing hA is exhibited with

slight differences in trend between the PE/hA, TPU/hA and PVDF/hA composites in terms of

the relationship between hA weight% and CT number. It can be noted that adding hA to a

given polymeric base in discrete amounts will have similar increases in CT number.

PVDF; y = 2101.3x + 563.65 R² = 0.9848

TPU; y = 1384.9x + 115.26 R² = 0.9798

PE; y = 1703.7x - 137.48 R² = 0.9965

-200

0

200

400

600

800

1000

1200

0% 5% 10% 15% 20% 25% 30%

CT

Num

ber (

HU

)

hA%

PVDF TPU PE

32

The effective atomic numbers were calculated as indicated in equation (3). Table 3-2

illustrates the mass density, photoelectric effect and incoherent scattering effective atomic

number of pure PE, TPU, PVDF and hA. These values will largely affect the linear

attenuation coefficient. Table 3-3 illustrates the elemental composition of the composites

fabricated, which dictate the effective atomic number values.

Table 3-2. Material properties of fabricated PE, TPU & PVDF composites of effective atomic numbers calculated by eqn. 3 with m and n values at 3.4 and 1.5 for ZT and ZR respectively. Density is calculated by

means of eqn. 6.

Polymer hA% ZT ZR Density (g/cm3)

PE

0.0 5.51 5.03 0.862 2.5 6.59 5.31 0.918 5.0 7.35 5.59 0.942 10.0 8.49 6.14 0.974 15.0 9.36 6.66 1.019 20.0 10.08 7.18 1.054 25.0 10.70 7.68 1.093

TPU

0.0 6.31 5.85 1.167

2.5 7.16 6.11 1.176

5.0 7.83 6.36 1.205 10.0 8.86 6.86 1.249

15.0 9.67 7.35 1.273

20.0 10.35 7.82 1.295 25.0 10.94 8.28 1.309

PVDF

0.0 7.95 7.55 1.743

2.5 8.50 7.76 1.733

5.0 8.97 7.97 1.772 10.0 9.77 8.38 1.770

20.0 11.02 9.16 1.865

3.3.2 Density & Elemental Composition Values

33

Table 3-3. Elemental composition of varied PE, TPU and PVDF composites ordered by added hA weight percent.

Polymer hA% Elemental Composition Weight% H C N O F P Ca

PE

0.0 14.37 85.63 0.00 0.00 0.00 0.00 0.00 2.5 14.02 83.49 0.00 1.04 0.00 0.46 1.00 5.0 13.66 81.35 0.00 2.07 0.00 0.92 1.99 10.0 12.95 77.07 0.00 4.14 0.00 1.85 3.99 15.0 12.25 72.78 0.00 6.21 0.00 2.77 5.98 20.0 11.54 68.50 0.00 8.28 0.00 3.70 7.98 25.0 11.54 68.50 0.00 8.28 0.00 3.70 7.98

TPU

0.0 9.07 64.35 6.00 20.57 0.00 0.00 0.00 2.5 8.85 62.74 5.85 21.09 0.00 0.46 1.00 5.0 8.63 61.13 5.70 21.61 0.00 0.92 2.00 10.0 8.19 57.91 5.40 22.66 0.00 1.85 3.99 15.0 7.74 54.70 5.10 23.70 0.00 2.77 5.98 20.0 7.30 51.48 4.80 24.74 0.00 3.70 7.98 25.0 6.85 48.26 4.50 25.78 0.00 4.62 9.97

PVDF

0.0 3.15 37.51 0.00 0.00 59.34 0.00 0.00

2.5 3.07 36.58 0.00 1.04 57.85 0.46 1.00 5.0 3.00 35.64 0.00 2.07 56.37 0.92 1.99 10.0 2.85 33.76 0.00 4.14 53.40 1.85 3.99 20.0 2.56 30.01 0.00 8.28 47.47 3.70 7.98

Linear attenuation coefficient is a measure that combines the effect of density and elemental

composition. As shown in equation (1), the linear attenuation coefficient has a direct

relationship with the CT number. Furthermore, as illustrated in equation (2), the value of the

linear attenuation coefficient is based highly upon both the density and effective atomic

numbers of the material scanned. The numerical model was verified by means of two

methods.

Firstly, the linear attenuation coefficient was calculated by means of measured

material properties, in which we will call the materials approach. This approach uses

equation (2) with measured material properties, including the mass density and effective

3.3.3 Effect of Linear Attenuation Coefficient on CT Number

34

atomic number values to determine the linear attenuation coefficient. This approach is then

compared to a theoretical approach. This method uses the measured CT number values

obtained through ImageJ analysis and equation (1) to determine the linear attenuation

coefficient. Table 3-4 then compares these values to one another to determine the level of

accuracy to which this theoretical model can be leveraged to fabricate accurate and fine tuned

CT number phantoms.

Table 3-4. Comparison of linear attenuation coefficient values (µ) through a materials and CT scan approach as calculated by equations (1) and (2). Measurements were taken at 100kVp.

Polymer hA% Materials Approach µ (cm-1)

Theoretical Approach µ (cm-1)

PE

0.0 0.164 0.171 2.5 0.177 0.177 5.0 0.184 0.183 10.0 0.196 0.200 15.0 0.211 0.217 20.0 0.223 0.235 25.0 0.237 0.253

TPU

0.0 0.216 0.214 2.5 0.221 0.223 5.0 0.230 0.231 10.0 0.246 0.252 15.0 0.257 0.259 20.0 0.269 0.272 25.0 0.280 0.282

PVDF

0.0 0.313 0.311 2.5 0.316 0.315 5.0 0.328 0.320 10.0 0.339 0.345 20.0 0.379 0.389

We then aim to compare the accuracy of a commonly used CT phantom device to that of our

fabricated phantom devices as well as our theoretical approach. Figure 3-3 illustrates the

linear attenuation coefficient values exhibited by the Gammex 467 phantom and various

tissue mimicking plugs. The phantom was scanned and measured based on its CT numbers to

35

be compared against the fabricated composite phantoms of this study. Figure 4 then also

compiles the Gammex phantom data and the fabricated PE, TPU and PVDF phantom data to

illustrate a comparison between the CT number – linear attenuation coefficient trends

between the fabricated composites (PE, TPU, & PVDF) and Gammex 467 phantom. We can

see that, in general, the material approach, through our fabricated composites, quite closely

aligns to the theoretical approach at lower CT number values. However at increasing CT

numbers, the materials approach tends to deviate from the theoretical – that of increasing hA

on both TPU and PVDF composites. However, it can also be noted that the Gammex 467

also depicts this variation to a higher degree than that of the fabricated phantoms.

 

Figure 3–3. Comparison of TPU/hA and PE/hA composites in terms of measured CT number and linear attenuation coefficient compared to the bilinear trend exhibited by the Gammex 467 phantom with values

obtained at 100kVp.

-1000

-500

0

500

1000

1500

2000

0 0.1 0.2 0.3 0.4 0.5 0.6

CT

Num

ber (

HU

)

Linear Attenuation Coefficient (cm-1)

Materials Approach - Gammex 467

Materials Approach - Composites

CT Scan Approach

36

The fabricated composites of PE, TPU and PVDF with hA demonstrated an inclusive range

of -124.5 to 995.3 HU which is representative of the range necessary to replicate various

coronary plaque types. Although, between fabricated TPU and PVDF polymeric composite,

CT numbers between about 400 to 600 HU are not illustrated, it is expected with increasing

hA amounts to TPU or, alternatively, a polymeric density between TPU and PVDF that range

can be achieved. By determining the relationship between added hA to a polymeric base, a

high degree of CT number control can be attained. By adding hA to a polymeric base in

discrete amounts, CT number values between the given range can be fabricated.

To predict how the addition of hA can affect a given polymer, a numerical approach,

given by equations (1) through (5) is illustrated. The addition of hA to a polymer contributes

both to an increase in mass density as well as an increase in the effective atomic number

values. When predicting the linear attenuation, and further the CT number through measured

material properties, the expected CT number compared to the measured CT number are

closely aligned.

Alternatively, when compared to the Gammex 467 phantom, it can be seen there is

some deviation of the CT scan approach relationship to that of the materials approach

relationship. Notably, a simple relationship between linear attenuation coefficient and CT

number typically illustrates a bilinear phenomenon. This phenomenon has been studied and

is noted as an effect of beam hardening [37]. This beam hardening effect is a result of x-ray

variation throughout the specimen due to sample size, external environment and intrinsic

attenuation coefficients. X-ray beams are typically hardening through CT scanner filters

however, due to the polychromatic nature of the CT scanner, the resulting scan will produce

3.4 Discussion

37

an x-ray spectrum. With samples at higher effective atomic number values, this beam

hardening effect is more prominent thus resulting in an increase effective energy. This

variation of effective energy thus changes our numerical approach by means of altering our

theoretical linear attenuation coefficient values. Though within this study, we utilize our

numerical approach through a single consistent effective energy value, in actuality this value

changes with changing effective atomic number.

Through this, it can be seen in figure 3-3 that there is a notable bilinear trend, though

only applicable to high photoelectric effect effective atomic numbers. As such, the fabricated

polymeric composites did not demonstrate the bilinear trend to a large degree however, in

comparison to that of the Gammex phantom, the samples which demonstrated the bilinear

trend had photoelectric effect effective atomic numbers greater than those of the polymeric

composite.

Through this study, we have demonstrated a method in which polymeric composites using

PE, TPU, and PVDF with hA in discrete weight percentages can be viewed as

anthropomorphic to various types of coronary plaque under CT scanning. By means of a

range of -50 to 800 HU, this can encompass most plaque types from fatty plaque to ranges of

calcified plaque. Through our polymeric composites we have managed to fabricate samples

which can attenuate within this range through our control of hA additions.

The numerical method illustrated in equations (1) through (5) also has a high level of

accuracy for predicting the CT number of the fabricated polymeric composites through

material properties and linear attenuation coefficients. However, when compared against a

3.5 Summary

38

Gammex 467 standard phantom, the phantom is known to deviate from the numerical method

by means of a bilinear relationship between CT number and linear attenuation coefficient.

This effect was not prominent for the fabricated composites, however, due to relatively lower

overall values of effective atomic number values.

For future considerations, we hope to encourage studies involving the fabrication of

polymeric composites for use in coronary plaque CT phantom applications. An important

aspect to study would involve developing both geometrically and CT property accurate

phantoms specifically for coronary plaque. Due to the use of thermoplastic polymers, the

phantoms developed are able to be molded to the shape desired with ease and further set in a

solid manner. To this matter, future studies would benefit by describing the ease to which

these composites can be fabricated and integrated into a full artery phantom. This would also

include developing phantoms specifically for the desired calcification amount, including that

between 400 to 600 HU, not included in this study.

39

Tissue mimicking CT phantoms have utilized various polymers and can be fabricated and

altered to have controlled elemental compositions with increasing densities depending on

mixture compositions. Their mass density currently limits polymers, as CT phantoms.

Polyethylene, a low-density polymer, will minimally attenuate at ~ -80 HU whereas the

clinical need is to simulate adipose tissues and fatty plaques for which the attenuation can

reach -200 HU or less with lung structures as low as -500HU and the lung itself nearing -

1000HU [10], [92], [93]. Our aim is to leverage studies which have been conducted on

cellular foam polymers for low density CT phantoms such as adipose and lung tissue mimics

[15], [94], [95]. The utility of mimicking low attenuating tissues under CT imaging has been

demonstrated and low-density polymeric foams are commonly used however most foams

considered are commercially made and not specified to CT phantom applications [69], [96],

[97].

Chapter 4 Polymeric Cellular Foams for Low Density Computed Tomography Phantom Applications

40

A few select techniques have demonstrated procedures in which polymeric foams can

be fabricated with controlled material properties. Notably, in these cellular foaming

techniques, the morphological (porosity, cell size, cell density) and material (density)

characteristics can be controlled and measured [98]–[101].

Polymers can be fabricated through cellular processing to create a porous network

and decrease the overall density of the polymer through void propagation [98]. Cellular

processing can be controlled to a degree in which the pores are at a micron or nano level,

potentially imperceptible to the CT scanner. This creates a seemingly homogenous phantom

that can be manipulated to present a wide range of material attenuations to mimic a diverse

array of tissue environments; from densely calcified tissue to adipose or lung tissue, without

being perceived as a porous network.

By detailing a fabrication technique in which CT phantoms can be custom-made

specific to the desired tissue, studies to improve CT imaging diagnostics can proliferate. As

well, a consistent fabrication technique will create phantoms with reliable and expected CT

values that may not be attained by native tissues due to tissue variance between patients.

The last two decades have seen a rapid increase in utilization of CT for a wider number of

clinical applications; and these have increased the need for accurate tissue characterization

and for a deeper understanding of tissue attenuation response to multispectral CT [6], [102]–

[105]. However, during diagnosis, it has been reported that a CT machine dependent variance

of 5% in attenuation measurements can occur [96]. Furthermore, the effect of reconstruction

algorithms can vary the CT attenuation by up to 9.4% [96]. As such, there is a real need to

4.1 Motivation

41

more fully understand the influence of various CT parameters on accurate tissue

characterization and this, in turn has driven the demand for anthropomorphic CT phantoms

with the ability to mimic various tissues to a high degree of accuracy. Through this, various

post-processing tools and imaging techniques can be implemented without the need for

cadaveric or live tissue specimens. Clinical applications and studies have demonstrated the

need for accurate and specific CT parameters. Examples include characteristic studies of

adrenal adenomas [102], coronary artery disease [6], and development of CT tools [106].

Furthermore, a number of studies have detailed the use of commercial phantoms or material

substitutes for CT electron density calibration and treatment planning systems. However

there are a limited number and CT characteristic ranges of phantoms available which has thus

seen an increase in studies involving low density tissue-equivalent substitutes [21], [22],

[70], [94], [95], [15] .

Accurate and specific CT numbers are essential in diagnostic imaging for tissue

characterization for example; differentiation of adrenal and renal nodules or masses[102],

characterization of non-calcified coronary artery plaque[6], and determination of the

vascularity of a non-calcified mass through measurement of tissue enhancement following

injection of intravascular iodinated contrast. Accurate CT numbers are also critical to

development of advanced CT visualization tools used in 3D applications such as virtual

applications[106] including colonoscopy, bronchoscopy and 3d angiography.

Furthermore, several studies have detailed the use of commercial phantoms or

material substitutes for CT electron density calibration and for use in treatment planning

systems. However, detailed evaluation demonstrates that there are a limited number of low-

density phantom devices available and these have a limited range of values compared to the

42

larger range in the materials manufactured in this manuscript.[22], [70], [94], [95], [15],

[107] For example, Landry et al.[108] and Bazalova et al.[109] detail the various properties

of Gammex phantom inserts. It is capable of mimicking a wide range of various mid to high

density tissues and, thus, CT numbers, however it is limited in the availability of low density

CT phantoms. As such it can be noted that specific tissues can be mimicked by means of the

Gammex phantom, however, values less than -100 HU and greater than -500 HU are not

available. This range can be important, for example, when attempting to mimic a highly

accurate adipose CT phantom. As illustrated in Kim et al.[110], the most prominent range of

adipose tissue is between -190 and -30 HU, and further values found as low as -250 HU. In

addition, lung structures have measured CT attenuation as low as -500HU and lung

parenchyma measures ~ -1000HU.[10], [92], [93]

CT imaging phantom devices have proven to be beneficial in improving CT

diagnostic techniques. Though commercial phantoms are available with tissue mimicking

properties, there is a lack of low density tissue specificity and variety. This study proposes a

method for the fabrication of various low density tissue mimicking CT imaging phantoms.

By illustrating the fabrication technique, material properties are controlled and assessed

against characteristic CT imaging properties, most particularly, the CT number in Hounsfield

Units (HU). Thus, this study can allow for highly specific CT imaging phantoms to be

fabricated and tailored matching the properties of the desired low density tissue.

The aim of this study is to utilize a batch cellular foaming technique to fabricate

polymeric CT phantoms to create specific foam material properties of porosity, average cell

size, cell density, and mass density. CT number values were assessed against these

characteristic foam material properties to determine the relationship between the two and the

43

level of consistency and accuracy. Material characteristics were examined by means of a

morphological assessment under scanning electron microscopy (SEM) and ASTM standards

for density measurements. The material characteristics of each CT phantom material were

then compared to the measured CT number using varying kVp.

The foaming technique utilized for our fabrication method was a batch foaming technique

using supercritical CO2. As described in Jacobs et al [112]. This phenomenon involves the

following setup which we have leveraged for our experimentation:

 

Figure 4–1. Foaming technique setup with reference to Jacobs et al. [112] but controlled for our purposes of foaming TPU.

Firstly, polymeric samples are placed in a high pressure environment with CO2

introduced. The high pressures allow the polymer to become saturated with CO2 as well as

decreasing the glass transition temperature (Tg). This temperature determines whether the

polymer is in a glassy, or brittle state at temperatures below Tg or in a rubbery, or soft state

4.2 Materials and Methods

4.2.1 Foaming Procedure

44

above Tg. Decreasing the Tg gives the polymer a soft state allowing CO2 to effectively diffuse

within the throughout the polymer. Following this is an induced thermodynamic instability

through rapid pressure release. This leads to an instantaneous oversaturation of CO2 within

the polymer. Further, a heated environment is often necessary to nucleate and grow cells,

then allowing CO2 to diffuse out of the polymer, if the Tg has not been reached. Once the

polymer has returned to a value below its Tg, nucleation and growth will stop.

The heated water bath had a selected temperature of 80oC based upon an optimal

temperature; low enough such that the degree of foaming can be controlled but high enough

to induce desired foam characteristics at reasonable lengths of time (0.5 min to 10 min).

The polyester-type pellet form thermoplastic polyurethane (TPU) (Desmopan 385E; Bayer

AG, Germany) was used as the base polymer for fabrication. For the foaming procedure,

TPU was selected due to the potential for a wide range of foams produced through various

studies for TPU and its ease and known conditions for batch foam processing [60], [99],

[100], [111].

Table 4-1. Pure TPU material properties illustrated as atomic composition and physical properties.

Atomic Composition (Weight %) Physical Properties H (Z = 1) C (Z = 6) N (Z = 7) O (Z = 8) ρ (g/cm3) ρe Relative ρe

9.07 64.35 6.00 20.57 1.200 3.82E+23 1.145

If the average cell size is greater than that of the limiting spatial resolution of the CT

image, the phantom will appear heterogeneous, with distinct regions of pores and solid

samples. Thus, the aim is to fabricate phantoms that are viewed as homogenous under CT

4.2.2 Material Fabrication

45

scans, with the largest cell sizes falling below the limiting spatial resolution of the CT

imaging chain defined by the defined field of view (DFOV) of each scan.

An aluminum mold with a capacity of 16 samples was used together with a Carver

Bench Top Standard Heat Press to mold the TPU to a desired sample shape. The heated

plates were set to 200oC and the TPU was exposed for 15 minutes. The heated TPU was then

compressed to 7 metric tons for another 5 minutes with the plate remaining heated.

Following compression, the samples were immediately cooled in a room temperature water

bath for 1 minute and then removed carefully from the mold. Sixteen samples were made per

batch, each cylindrical sample measured 12.5mm in diameter by 6.5mm in height.

To create the cellular foam from the molded TPU, the samples were introduced, in

sets of 4, to an aluminum pressure chamber. The batch foaming processing technique was

then completed on all the samples under different conditions. The batch foaming process

consists of: sealing the sample in an aluminum chamber under high pressures, rapidly

releasing the pressure, thus introducing a thermodynamic instability, and then immediately

placing the samples into a heated water bath. TPU samples were inserted into the pressure

chamber at 800psi for 24 hours. The samples were then exposed to a heated water bath of

80oC for either 0.5, 1, 1.5, 2, 5, or 10 minutes and then immediately placed in a room

temperature water bath to cool. The completed samples were then placed in a room

temperature vacuum chamber for 24 hours to allow for adequate drying and removal of

internal moisture. Following the foaming procedure, sample dimensions were left

unrestricted allowing for slight increases in size.

46

The phantoms were scanned in a wide volume CT (320 MDCT AquilionONE; Toshiba

Medical Systems, Japan) after calibration of the CT unit and CT numbers were measured

using ImageJ (ROI Manager, NIH, USA) on post-processed images. The scanning conditions

were set at 160 x 0.5mm detector confirmation, a gantry rotation of 0.5 second, fixed tube

current of 50mA and tube potentials of 80, 100, 120 and 135kVp. The display field of view

(DFOV) used was 30cm. The images were reconstructed using a single filter kernel (FC08,

standard for beam hardening) [113] as 1mm sections at 0.5mm intervals. Additionally, figure

4-2 presents the arrangement in which the phantoms were scanned. All phantoms were

scanned simultaneously under each of the mentioned tube potentials. Scans were completed

with samples oriented between enclosed saline solution environments.

 

Figure 4–2. CT scanning arrangement of TPU foam sets A through E. Included are TPU100 samples acting as pure TPU for reference during scan. Labeling from A through E represent TPU sets A through E respectively.

4.2.3 Computed Tomography Number

47

Table 4-2. Outline of processing technique utilized for TPU foam sets A through E. The variation in processing condition is based upon the length of time upon which the TPU samples are introduced into the heated water

bath.

Sample Foaming Chamber Water Bath Pressure (psi) Time (hrs) Temperature (oC) Time (min)

TPU Set A 800 24 80 0.5 TPU Set B 800 24 80 1 TPU Set C 800 24 80 2 TPU Set D 800 24 80 5 TPU Set E 800 24 80 10

Our aim was to correlate the measured CT number to distinct material properties which both

characterize cellular foams and affect the CT number. To direct our study, we focused on

commonly measured foam characteristics of physical density, porosity, average cell size and

cell density.

Density measurements were carried out according to the water-displacement method

(ASTM Standard D792 – 13) to characterize mass density and porosity.

To characterize the TPU foams, the samples underwent scanning electron microscopy (SEM)

(JSM 6060; JEOL, Japan) with image analysis using a common morphological characterizing

tool, ImageJ [100], [101], [114]. From this, cell density and average cell sizes were

measured.

Firstly, the porosity can then be described by Eqn. 7 [99], [101], [115]:

𝜙! = 1−  𝜌!"#$%  𝜌!"#$

∗ 100% (Eqn. 7)

where 𝜙v is the porosity, or void fraction, measured in percentage, 𝜌foam is the foamed

density measured and 𝜌solid is the solid un-foamed density of the sample.

4.2.4 Foam Characterization Methods

48

Following this, the cell density and cell sizes were interrogated through SEM imaging

and further image analysis by means of the ImageJ software (ROI Manager; NIH, USA). The

ImageJ software is able to measure the number and average size of cells within a given

image, thus giving a representation of the sample as a whole. Once analyzed, the average cell

size of the sample could be shown while the cell density of the sample was calculated as

detailed in Eqn. 8 [101], [115], [116]:

𝐶𝑒𝑙𝑙  𝐷𝑒𝑛𝑠𝑖𝑡𝑦 =𝑁𝑜. 𝑜𝑓  𝐶𝑒𝑙𝑙𝑠

𝐴𝑟𝑒𝑎

!! 𝑆𝑜𝑙𝑖𝑑  𝐷𝑒𝑛𝑠𝑖𝑡𝑦𝐹𝑜𝑎𝑚  𝐷𝑒𝑛𝑠𝑖𝑡𝑦 (Eqn. 8)

The attenuation of each TPU sample was averaged from 3 measurements per image with 3

image measured per sample. Each TPU sample set consisted of 6 samples, thus 54

measurements were averaged per CT number value and the values at different kVp. All error

bars are indicative of the maximum and minimum values measured from the data set. A

comparison was made between kVp and CT number to determine if any change in trends

were discernible with changing kVp. As illustrated in figure 4-3, the fabricated TPU foams

do not illustrate a large dependence on kVp.

4.3 Results

49

Sample CT Number (HU)

80kVp 100kVp 120kVp 135kVp TPU Set A 58.76 73.12 82.13 83.81 TPU Set B -82.73 -65.38 -57.50 -52.39 TPU Set C -171.20 -157.74 -151.15 -145.20 TPU Set D -199.78 -189.09 -182.84 -178.02 TPU Set E -225.36 -212.34 -205.44 -200.96

Figure 4–3. Comparison of TPU foam sets for changing kVp values to CT number.

The effect of increasing kVp and, thus, keV upon CT scanned specimen typically sees

an increase in CT number. However, in reference to decreasing electron and physical

densities, this variation of CT number with increasing kVp becomes less prominent. In fact,

at relative electron densities to water below 1, a reverse trend of increasing CT number with

increasing kVp is noted [33]. For our study, due to the similarity in trend, our analysis

focused on a CT number analysis at 100kVp.

-250

-200

-150

-100

-50

0

50

100

70 80 90 100 110 120 130 140

CT

Num

ber

(HU

)

Tube Voltage (kVp)

TPU Set A TPU Set B TPU Set C TPU Set D TPU Set E

50

Table 4-3. CT number values measured in Hounsfield Units (HU) for solid TPU and subsequent TPU foam samples. Additional data of measured density through the ASTM standard

TPU Set Sample CT Number 100kVp (HU)

TPU Set A 1 84.67 2 84.77 3 49.91

TPU Set B 4 -47.62 5 -97.15 6 -51.37

TPU Set C 7 -142.69 8 -145.94 9 -184.60

TPU Set D 10 -186.03 11 -194.58 12 -186.65

TPU Set E 13 -203.53 14 -226.25 15 -207.24

Due to a similarity in trend, our analysis focused on a CT number analysis at 100kVp.

The cell morphology of the various fabricated TPU foam sets was measured and calculated

by means of eqn. 1 and 2. Cell density measurements also required a morphological analysis

of the foam using the JEOL SEM to image the samples, whereas the cells were counted and

measured with the ImageJ software. An area of about 0.04mm2 was examined per image to

attain both the number of cells and average cell size, representing that of the entire sample.

4.3.1 Effect of Cell Morphology on CT Number

51

The relationships between porosity, cell density and average cell size against CT number at

100kVp were evaluated and is shown in figure 4. It can be noted that there is a negative

influence of all foam characteristics to measured CT number.

 

 

Figure 4–4. Relationship exhibited between CT number (HU) and porosity (%), cell density (cells/µm2), and average cell size. An increase in porosity, cell density and average cell size all saw general trends of decreasing

CT number. The points represent the mean where as the error bars represent the maximum and minimum measured CT values

.

-300

-250

-200

-150

-100

-50

0

50

100

150

0% 10% 20% 30% 40%

CT

Num

ber

(HU

)

Porosity (%) -300

-250

-200

-150

-100

-50

0

50

100

150

0.E+00 1.E-04 2.E-04 3.E-04 4.E-04

CT

Num

ber

(HU

)

Cell Density (cells/µm2)

-300 -250 -200 -150 -100 -50

0 50

100 150

4 5 6 7 8 9 10 11

CT

Num

ber

(HU

)

Average Cell Size (µm)

52

Figure 4-5 presents SEM images of the fabricated foam sets illustrating both the average cell

size The fabricated foams demonstrated both a maximum cell size that was smaller than the

highest spatial resolution of the CT imaging chain of 400 x 400µm.

 

Figure 4–5. SEM Images illustrate the morphological characteristics of the controlled cellular foam sets of (1) Set 1, (b) Set 2, (c) Set 3, (d) Set 4, (e) Set 5. Differences in foam characteristics are introduced by means of the

processing parameters illustrated in table 4-2.

Values of measured density are plotted against measured CT number with a positive

relationship noted between material density and CT number. A linear regression trend line

4.3.2 Effect of Cell Size on CT Number

4.3.3 Effect of Density on CT Number

53

was applied to determine the numerical linear relationship between CT number and physical

density as illustrated in figure 4-6.

 

Figure 4–6. Relationship exhibited between CT number (HU) and mass density where an increase in CT number results in an increase in CT number. The points represent the mean where as the error bars represent the maximum and minimum measured CT values. Linear regression analysis was also completed and illustrated as

the noted trend line.

The results of this study have demonstrated the ability of TPU foams to conform to a wide

range of CT number values to mimic various low density human tissues. The morphological

key metrics, which include porosity, cell density and average cell size, show general trends in

which an increase in foamed characteristics demonstrate a decrease in CT number. These

characteristics are then limited by the resolution of the CT scanner in which we aim to have a

homogenous sample. Hence, the cell sizes of the samples should be less than the minimum

y = 1155.7x - 1157.6 R² = 0.97832

-300

-250

-200

-150

-100

-50

0

50

100

150

0.75 0.80 0.85 0.90 0.95 1.00 1.05 1.10

CT

Num

ber

(HU

)

Density (g/cm3)

4.4 Discussion

54

pixel size of a CT image, which was also demonstrated through a morphological analysis

through SEM imaging. These images are thus seen as homogenous under CT scanning.

The error bars associated with figures 4-4 and 4-6 are indicative of the range of CT

numbers measured from each sample likely introduced due to noise. The foam cells, as noted

by the SEM images, are generally of the same size, however the range of cell sizes and

distribution due to the fabrication technique can exhibit a possible range in CT number

within the sample.

It was found that the prominent trend was decreasing CT number with increasing

characteristics of foamed properties. This can be described by increasing porosity, cell

density and average cell size, while additionally we find a decrease in physical density. These

properties can be attributed to two concurrent factors of an increase in void space acting as

air and a decrease in overall physical density.

The effect of void space can first be attributed to the porosity, cell density and the

average cell size. Thus we are likely to find a greater volume of the sample that has a CT

number of -1000 HU contributing as air. In addition, a greater amount of void space results in

a lower density due to an increased amount of polymeric expansion. The combined effect of

the CT scanner defining void space as air and an overall decrease in the density of the sample

contributes to a decrease in measured CT number.

However, when considering average cell size, competing factors can be considered.

Though an increase in average cell size would likely contribute to a greater amount of void

space, similar average cell sizes can have different cell densities thus varying the cell wall

thickness. High average cell sizes and cell densities can result in overall thinner cell walls

compared to that of the same average cell size but with low cell densities [59], [110], [115].

55

Our results generally showed increasing cell sizes in correspondence with increasing cell

densities though there was some variation in data, as demonstrated in figure 4-4.

Furthermore, when analyzing equations 7 and 8, a relationship can be noted between a ratio

of densities between solid and foamed density. This can then be attributed to the general

trend similarities seen of decreasing CT number with increasing foam properties. The

decrease in polymer density contributes to both cell density and porosity thus decreasing CT

number. Additionally, some of the discrepancy then seen with the relationship between

average cell size and CT number can be noted due to the competing effects of cell size and

cell density. With increasing cell size one can expect a decrease in cell density again as seen

through equation 8.

Ideally, the cell size we would aim for would be less than the pixel size. A typical CT

scanner sees the pixel size at no less than 0.4mm x 0.4mm. Thus, the average cell size would

be optimally produced to be less than this to introduce the CT scan as homogenous if desired

as such. With regards to cell density and porosity, we would select these properties based

upon the desired CT number. As mentioned, the lower the CT number aimed towards, the

higher the cell density and porosity necessary. Additionally, having a homogenous structure

through CT scans depends on the tissue and need for such, for the given applications of fatty

plaque and adipose, it would be desired that the scan illustrate the phantom as homogenous.

With reference to table III, there is some variation in the CT numbers of samples

within the same foam set. The discrepancy can be attributed to the wait period between

retrieving the sample from the heated water bath to submerging the sample within the cold

water bath. Since samples are retrieved one at a time from the heated water bath, certain

samples are left in the water bath for a slightly longer period. At longer heated water bath

56

submerging times, the foaming properties are not as prominently affected in compared to

short heated water bath periods such as 30 - 60 seconds.

Furthermore, a current limitation is the fact that a single polymer system was

analyzed. Though the polymer is expected to have wide ranging foam properties, we expect

that further research will include and desire the use of various polymers utilizing foam

processing. To do so, water bath temperature and foaming time will necessitate optimization

based on the polymer used essentially based upon the Tg of the polymer system.

Lastly, one of the main formulaic concepts behind this research is based upon the

relationship between CT number and mass density. Illustrated by a study conducted by

Watanabe et al.[10], mass density is directly proportional to attenuation coefficient, which, in

turn is also proportional to CT number. Porosity and cell density as expressed through

equations (1) and (2), are both inversely proportional to the foam density (ρfoam). As

mentioned earlier and supported by the effect of density, an increase in porosity and cell

density are a result of a decrease in foam density. This is because our processing results in a

decrease in mass density due to the addition of voids within the sample. Any of these effects,

in turn, result in a decrease in CT number, relative to the relationship exhibited between

physical density and CT number. Though various constituents also affect the CT number,

most notably elemental composition, the fabricated foam phantoms are constructed of the

same material though at different mass densities.

This proof of concept study demonstrates that custom design and the manufacturing of

polymeric foam CT phantoms to meet rigorous imaging tasks is feasible and controllable

4.5 Summary

57

with a high degree of precision using the described fabrication technique. Low density CT

imaging phantoms were created with the ability to have tailored CT number through the

controlled fabrication technique illustrated.

The concept of using commercially available foam phantoms has been frequently

studied to determine whether different foams can be differentiated using CT imaging, The

current study demonstrates a method in which foams can be fabricated and controlled to a

fine degree to achieve a desired CT number. This approach is important when aiming to

mimic various types of adipose and low-density tissues. The materials analysis approach in

which foam characteristics are measured against CT number illustrates how porosity, cell

density, and mass density affect the HU value. The presented fabrication technique can be

used to control the necessary material properties and tailor the CT number to the desired

characteristics for the CT phantom. It is suggested that further studies include the

advancements in CT scanning with multispectral analysis and dual energy decomposition

utilizing varied foam fabrication techniques for low-density CT phantoms. It is further

suggested that follow up studies conduct large scale fabrication processes utilizing similar

fabrication techniques on larger equipment to create anthropomorphic phantom devices such

as the lungs.

58

The fabrication of CT phantom devices allows researchers to have the accessibility to

customized size, shape and tissue mimicking properties. With the flexibility and choice of

material for a phantom to mimic specific human tissues and systems, it is important that the

fabricated phantom have a high degree of CT consistency and predictability based upon its

measured material properties. Due to CT calibration, a phantom device typically requires a

water bath or water-equivalent environment which often is a rigid poly(methyl methacrylate)

(PMMA) surrounding. Both scanning environments, though standard for CT phantom

scanning, can limit the potential complex, tortuous, and non-rigid phantom designs.

CT phantom devices are also limited to which they cannot be scanned accurately in

only air surrounding environments. Though scanning without any surrounding water bath or

Chapter 5 Effect of Scanning Medium on the In-house Fabricated Polymeric Composite CT Phantom Devices

5.1 Introduction

59

water-equivalent media would allow for an entirely flexible design of CT phantom, it is

expected that when scanning these phantoms in air that significant inaccuracies will occur. It

would be expected that within air environments, the characteristic CT number values in HU

should vary from water bath environment scans due to the polychromatic nature of CT

scanning.

Any deviations that result are often described through the probabilistic nature of the

CT scanner by the processes of photon production, interaction, and detection [9], [36], [118].

This variation is reflected upon the attenuation coefficient (µ) and, thus, the measured CT

number of the phantom device. This is due to the variation upon the CT scanning conditions

and the CT machine used [22], [33], [79], [105], [119], [120]. Since CT scanning is of

polychromatic nature, depending on the path of the beam, lower energy x-rays can be

removed from the spectrum prior to interacting with the phantom. Under CT scanning, this

can occur under two methods. The first is the filters applied immediately to the beam for

modern CT scanners, thus removing lower energy x-rays. This filter will vary by the CT

scanner and protocol used [103]. The second method is a beam hardening effect. This effect

is well studied and is understood to change the attenuation of the phantom [64], [121], [122].

This beam hardening effect is introduced by applying a surrounding media. The scanning

environment will then further remove low energy x-rays which is often a water or water-

equivalent surrounding environment [11], [120], [123]. Since CT scanners are clinically

calibrated to water or a water-equivalent material, a similar surrounding media must be

introduced around the phantom for CT number accuracy and consistency. The lack of

scanning media may result in a heterogeneous scan to which the outer edge of the sample

60

may have a higher level of x-ray interaction while middle sections may result in less

attenuation due to beam hardening throughout the solid.

Fabricated CT phantoms scanned under a water bath environment are compared to

scans of an enclosed saline solution surrounding and open air environments. The comparison

involves differences in measured CT numbers between scans in the different media. These

differences are then further assessed through the likeness of calculated linear attenuation

coefficient (µ) to measured CT numbers scanned in the different media. The definition of µ

includes both measured material properties and effective energy calculated through a non-

linear regression analysis of CT numbers and electron density values. The differences in

measured CT numbers between water bath environment scans and saline solution shows the

least variation at higher applied voltages (120 and 135kVps) as well as having effective

atomic numbers (ZT and ZR) closer to that of water. As such it is possible that utilizing an

enclosed saline bag surrounding environment alternatively to a water bath or rigid water-

equivalent will allow for accurate scans of fabricated CT phantoms.

For this study, it is proposed that accurate phantom scans can be conducted on

fabricated CT phantoms by means of a surrounding environment of enclosed bags of saline

solution. As opposed to the rigid environments of the water bath and water-equivalent

polymer surrounding environments, the saline solution does not submerge the sample in

solution, nor does it limit the shape and conformation of the fabricated CT phantoms.

CT phantoms can be fabricated such that it conforms precisely to the study at hand.

The comprehension of CT numbers in relation to material properties can allow for a high

level of anthropomorphic behavior, thus allowing studies to mimic various human

61

environments accurately. However, CT phantoms typically are placed under a water or

water-equivalent environment with reference to CT calibration matching that of the human

body. Fabricated CT phantoms on the other hand may perform ideally under non-aqueous or

non-rigid surrounding media due to porosity, fragile nature, or complex shape. Thus, ideally

scans of fabricated CT phantoms would prefer to avoid a water bath or any rigid water-

equivalent surrounding medium. This study proposes the use of saline solution in enclosed

bags allowing for placement of the surrounding media in any conformation without

submerging the phantoms in a wet environment.

In-house fabricated CT phantoms have been proposed and scanned under a

conventional submerged water bath environment and, in contrast, both saline solution and

open air environments. The in-house fabricated CT phantoms are polymeric composites with

ranging material properties with expected CT number values ranging between -100 and 1200

HU, ideally to mimic widely varying biological tissue. This study will contrast the

effectiveness of fabricated CT phantoms under a surrounding saline solution and open air

environments as compared against scanning under a submerged water bath. We evaluate a

comparison between the CT numbers measured as well as the accuracy of measured material

properties to the measured CT numbers.

Three polymeric base materials were selected in combination with a single additive in order

to fabricate CT phantoms with the ability to mimic a wide range of CT numbers encountered

in clinical applications. These applications include various adipose, soft tissue and hard

5.2 Materials and Methods

5.2.1 Material Selection & Processing

62

tissues with CT numbers ranging between that of -100 and 1100. Polyethylene (PE),

thermoplastic polyurethane (TPU), and polyvinylidene fluoride (PVDF) were all selected

based on their differing densities and material properties influential to evaluating CT

numbers. Furthermore, the addition of hA in discrete amounts allowed for a fine control of

mass and electron density and, thus, the CT number.

Polymeric composite CT phantoms were fabricated by means of the three polymers: low

density polyethylene (LDPE; Nova Chemicals Novapol, LC-0522-A), polyester-type

thermoplastic polyurethane (TPU; Desmopan 385E), and polyvinylidene fluoride (PVDF;

ARKEMA, Kynar 720). Polymers were processed as received in pellet form. The additive,

hydroxyapatite (hA; 677418, Sigma-Aldrich) nanopowder with controlled particle sizing of

less than 200nm was used in combination with the polymeric bases.

Polymers were fabricated with discrete amounts of hA at 2.5, 5, 10 and 20 weight percentage

with all sets containing a pure polymer type with no hA added. Polymer and hA additions

were melt compounded by means of a 15 mL benchtop twin screw microcompounder (DSM

15, DSM Xplore). The polymer and hA were added simultaneously to the rotating twin screw

compounder at 130oC, 200oC, 230oC for PE TPU and PVDF respectively. Combinations

were processed at 100 rpm for 15 minutes and then extruded to be quenched immediately

from the outlet in room temperature water.

Extruded composites were processed into identical cylindrical shaped samples for CT

measurement by means of compression molding (12 Ton Carver Hot Press) into a disc

shaped mold (12.8-cm height by 0.65-cm diameter).

5.2.2 Computed Tomography Scanning Conditions

63

The phantoms were measured in a wide volume CT (320 MDCT AquilionONE; Toshiba

Medical Systems, Japan) with CT numbers measured using ImageJ on post-processed

images. The scanning conditions were set at 160 x 0.5mm detector confirmation, a gantry

rotation of 0.5 seconds, fixed tube current of 50mA a tube potential of 100kVp. The images

were reconstructed using a single filter kernel with 1mm sections at 0.5mm intervals.

The fabricated phantoms were introduced into the three environments of a water bath,

saline solution and air. For the water bath environment, the phantoms were submerged into

the centre of a 10cm length water bath. For the saline solution environment, the saline was

enclosed in 5cm bags in which these bags were placed above and below the phantoms. For

the air environment, the samples were placed with no surrounding environment.

Density measurements were carried out by ASTM Standard D792 – 13 by the measurement

of plastic density through displacement. The elemental composition was assessed as weight

percents per element for each polymeric composite..

Spherical regions of interest (ROI) measuring 10mm diameter were prescribed within the

centre of the sample, so that the outer perimeter was excluded to remove any CT variation

due to beam hardening at the perimeter of the phantom. The CT number in Hounsfield Units

(HU) was noted. Each CT number dataset was constructed by an average of 27 data points, 3

samples representing each polymer composite type with 3 measurements made per slice with

5.2.3 Physical Properties

5.2.4 Data Analysis

64

3 slices per sample. Concurrently, samples were measured under 4 different x-ray tube

voltages of 80, 100, 120, and 135kVps. Density value measurements were taken by 3

samples with 3 continuous measurements

Each CT number dataset was constructed by an average of 27 data points, 3 samples

representing each polymer composite type with 3 measurements made per slice with 3 slices

per sample. The same sample sets were measured in air, water bath, and saline solution

environments. Concurrently, samples were measured under 4 different tube voltages of 80,

100, 120, and 135kVps. Density value measurements were taken by 3 samples with 3

continuous measurements made each. CT numbers were measured with a centered field of

view (FOV).

Table 5-1 illustrates the weight percent of all polymeric composite phantoms fabricated.

Further, the material properties of measured mass density, calculated effective atomic

numbers and relative electron density are also presented. CT number measurements are

illustrated in table 5-2 and are compared by means of material composition and CT number

measurements made in air and water environments.

5.3 Results

5.3.1 CT Number and Material Property Measurements

65

Table 5-1. Weight percentages of H, C, N, O, F, P, and Ca within polymers and polymeric composites fabricated for CT phantom studies. Measured densities are illustrated for each polymeric composites. Effective

atomic numbers (ZƮ and ZR) as well as relative ρe were calculated by means of equations (2) through (5).

H C N O F P Ca

PE100% 14.37 85.63 0.00 0.00 0.00 0.00 0.00 0.862 5.54 5.17 0.886PE95% / hA5% 13.66 81.35 0.00 2.07 0.00 0.92 1.99 0.942 7.61 5.89 0.961PE90% / hA10% 12.95 77.07 0.00 4.14 0.00 1.85 3.99 0.974 8.79 6.54 0.989PE80% / hA20% 11.54 68.50 0.00 8.28 0.00 3.70 7.98 1.054 10.40 7.73 1.055TPU100% 9.07 64.35 6.00 20.57 0.00 0.00 0.00 1.167 6.34 5.98 1.145TPU95% / hA5% 8.63 61.13 5.70 21.61 0.00 0.92 1.99 1.205 8.04 6.61 1.177TPU90% / hA10% 8.19 57.92 5.40 22.66 0.00 1.85 3.99 1.249 9.12 7.20 1.215TPU80% / hA20% 7.30 51.48 4.80 24.74 0.00 3.70 7.98 1.295 10.64 8.29 1.249PVDF100% 3.15 37.51 0.00 0.00 59.34 0.00 0.00 1.743 7.99 7.65 1.570PVDF95% / hA5% 3.00 35.64 0.00 2.07 56.37 0.92 1.99 1.772 9.11 8.15 1.596PVDF90% / hA10% 2.85 33.76 0.00 4.14 53.40 1.85 3.99 1.770 9.96 8.62 1.594PVDF80% / hA20% 2.56 30.01 0.00 8.28 47.47 3.70 7.98 1.865 11.24 9.49 1.678

ZRRelative ρe

ZƮρ (g/cm3)

SampleWeight Percent per Element (% )

Table 5-2. Comparison of measured CT numbers for the same polymeric composites measured under separate conditions of air, saline solution and water bath environments.

CT Number (HU)

Sample Water Bath Saline Solution Air Medium

80kVp 100kVp 120kVp 135kVp 80kVp 100kVp 120kVp 135kVp 80kVp 100kVp 120kVp 135kVp

PE100% -125.0 -108.8 -95.0 -91.7 -147.4 -124.5 -111.7 -103.5 -173.4 -150.7 -139.3 -130.5 PE95% / hA5% -62.8 -52.3 -44.5 -48.1 -69.6 -61.7 -58.0 -51.4 -78.5 -68.4 -62.7 -65.7 PE90% / hA10% 32.6 19.0 19.2 14.7 34.3 23.7 18.7 9.4 41.4 32.7 27.8 15.6 PE80% / hA20% 250.1 202.5 174.5 160.1 257.6 203.0 171.8 160.3 297.0 236.4 207.5 188.6 TPU100% 103.5 114.1 120.7 123.0 80.9 95.1 104.1 110.4 60.1 81.8 92.1 97.3 TPU95% / hA5% 200.2 196.2 191.9 191.7 186.4 185.2 182.4 180.9 203.0 196.1 191.5 186.2 TPU90% / hA10% 333.6 294.3 269.9 258.6 328.7 289.9 266.8 253.6 348.2 321.3 282.6 268.0 TPU80% / hA20% 434.6 370.9 329.3 306.5 466.4 392.5 346.8 320.3 510.6 435.4 383.0 350.6 PVDF100% 622.4 581.1 543.1 512.8 634.0 591.4 551.7 509.6 652.0 599.4 555.3 521.9 PVDF95% / hA5% 797.6 708.1 646.7 601.0 723.1 642.0 582.0 546.6 841.3 743.3 672.2 619.1 PVDF90% / hA10% 879.0 765.2 690.4 638.7 889.1 765.4 689.2 641.0 991.4 850.8 761.5 697.4 PVDF80% / hA20% 1183.0 983.9 867.9 791.0 1195.7 995.3 869.2 794.1 1372.8 1154.3 987.1 896.8

66

5.3.2 CT Number Comparison

The CT number values measured from the acquired CT scans performed using the water

bath, saline solution and air environment were compared. The difference in CT number

(ΔCT) varied between compositions however were more closely aligned between scans taken

in the water bath and saline solution environments as compared with the water bath and air

environment scans. The increase in added hA to each polymer results in increased atomic

number and consequently in increased ΔCT.

Table 5-3. Comparison of differences in CT number between the referenced water bath measurements and saline solution or air. Differences in CT number are illustrated for all polymeric composite CT phantoms

fabricated at 80, 100, 120 and 135kVps.

80kVp 100kVp 120kVp 135kVp 80kVp 100kVp 120kVp 135kVp

PE100% -22.41 -15.75 -16.72 -11.75 -48.47 -41.87 -44.30 -38.78PE95% / hA5% -6.84 -9.39 -13.54 -3.30 -15.77 -16.10 -18.23 -17.55PE90% / hA10% 1.71 4.71 -0.52 -5.32 8.77 13.71 8.63 0.83PE80% / hA20% 7.52 0.54 -2.69 0.19 46.95 33.91 33.03 28.52TPU100% -22.64 -18.98 -16.56 -12.60 -43.47 -32.31 -28.54 -25.68TPU95% / hA5% -13.86 -10.95 -9.49 -10.77 2.77 -0.08 -0.41 -5.45TPU90% / hA10% -4.89 -4.47 -3.09 -5.00 14.58 26.97 12.74 9.43TPU80% / hA20% 31.82 21.52 17.53 13.80 76.04 64.49 53.77 44.12PVDF100% 11.53 10.26 8.52 -3.12 29.53 18.21 12.12 9.16PVDF95% / hA5% -74.44 -66.10 -64.65 -54.35 43.78 35.21 25.56 18.13PVDF90% / hA10% 10.07 0.18 -1.22 2.28 112.35 85.58 71.07 58.63PVDF80% / hA20% 12.77 11.38 1.28 3.17 189.80 170.38 119.22 105.89

ΔCT (HU) = CTWater - CTSaline ΔCT (HU) = CTWater - CTAirSample

To quantify a comparison between the measured material properties prevalent to CT

scanning within water, saline solution and air environments, eqn. (1) through (5) can be

5.3.3 Material Property Comparisons

67

leveraged. Material properties illustrated in table 5-2 can represent a means to calculate the

linear attenuation coefficient.

Linear attenuation coefficient is a property derived upon both material properties and

effective energy (Eeff) values. Eeff values were determined through a non-linear regression

analysis conducted through the SigmaPlot program (SPSS Inc., Chicago, IL, USA). Eqn. (1)

through (5) were utilized and re-arranged in the following manner:

(𝐶𝑇  𝑁𝑢𝑚𝑏𝑒𝑟 + 1000)1000

   =  𝜌! 𝑎 𝑍!

!

𝐸! + 𝑏 𝑍!!

𝐸! + 𝑐(𝐸)

𝜌!,! 𝑎𝑍!,!!

𝐸! + 𝑏𝑍!,!!

𝐸! + 𝑐(𝐸) (Eqn. 9)

where a ratio of µ over µw was conducted which then followed a combining of eqn. (1) and

(2). By applying the values illustrated in table 5-2 as well as the measured CT numbers in

table 5-3, the remaining E constant can be determined through the non-linear regression

model. The results are illustrated in table 5-4.

Table 5-4. Comparison of assessed Eeff values through water bath, saline solution and air environments at 80, 100, 120, and 135 kVps.

80kVp 100kVp 120kVp 135kVpWater Bath 61.1 71.4 82.2 91.5Saline Solution 58.6 69.4 82.9 95.8Air 53.0 61.0 69.8 78.7

MediumEeff (keV)

By then utilizing the Eeff values determined in table 5-4, we can then calculate the µ

values within water bath, saline solution and air environments. Ultimately, a comparison can

be made between the referenced water bath scans and the saline solution or air environment

scans. The differences are illustrated as a percent deviation from the µ values calculated from

the water bath environment.

68

Table 5-5. Comparison of u values as calculated through equation (2). Comparisons are made as a % deviation from water bath environment scans and is conducted for both saline solution and air environments at 80, 100,

120 and 135kVps.

80kVp 100kVp 120kVp 135kVp 80kVp 100kVp 120kVp 135kVp

PE100% 1.4% 0.9% -0.3% -1.9% 3.6% 4.4% 6.1% 7.8%

PE95% / hA5% 1.9% 1.2% -0.4% -2.1% 5.2% 5.8% 7.4% 8.9%

PE90% / hA10% 2.4% 1.4% -0.4% -2.2% 6.7% 7.2% 8.7% 9.9%PE80% / hA20% 3.3% 1.9% -0.5% -2.6% 9.4% 9.7% 11.1% 11.9%TPU100% 1.6% 1.1% -0.3% -2.0% 4.3% 5.0% 6.7% 8.4%TPU95% / hA5% 2.2% 1.3% -0.4% -2.2% 5.9% 6.5% 8.0% 9.4%TPU90% / hA10% 2.7% 1.5% -0.4% -2.3% 7.4% 7.8% 9.3% 10.4%TPU80% / hA20% 3.5% 2.0% -0.5% -2.7% 9.9% 10.3% 11.6% 12.4%PVDF100% 2.3% 1.4% -0.4% -2.2% 6.3% 6.9% 8.4% 9.8%PVDF95% / hA5% 2.8% 1.6% -0.4% -2.4% 7.7% 8.2% 9.7% 10.8%PVDF90% / hA10% 3.2% 1.8% -0.5% -2.6% 9.0% 9.4% 10.9% 11.8%PVDF80% / hA20% 4.0% 2.2% -0.6% -2.9% 11.3% 11.7% 13.1% 13.7%

% deviation of µAir from µWaterSample

% deviation of µSaline from µWater

With both the linear attenuation coefficients and CT number values assessed, a comparison

can be determined as trend lines between these two values. The following figure illustrates a

comparison between trend lines developed between µ and CT number for water bath, saline

solution and air environment scans.

5.3.4 Linear Attenuation Coefficient to CT number Comparisons

69

Figure 5–1. Trend analysis and comparison between CT number and its corresponding calculated linear attenuation coefficient of scans completed in a water bath (Yw), saline solution (Ys), and air environments (Ya)

at 80kVp.

Figure 5–2. Trend analysis and comparison between CT number and its corresponding calculated linear attenuation coefficient of scans completed in a water bath (Yw), saline solution (Ys), and air environments (Ya)

at 100 kVp

Yw = 5169.7x - 1060.1

Ys = 4968.9x - 1048.2

Ya = 4964.8x - 1116.4

-300

-100

100

300

500

700

900

1100

1300

1500

0.1 0.2 0.3 0.4 0.5 0.6

CT

Num

ber (

HU

)

Linear Attenuation Coefficient (cm-1)

80kVp Water Bath Saline Air Linear (Water Bath) Linear (Saline) Linear (Air)

Yw = 5191x - 985.05

Ys = 5069.3x - 980.94

Ya= 5055.8x - 1047.6

-300

-100

100

300

500

700

900

1100

1300

1500

0.1 0.2 0.3 0.4 0.5 0.6

CT

Num

ber (

HU

)

Linear Attenuation Coefficient (cm-1)

100kVp Water Bath Saline Air Linear (Water Bath) Linear (Saline) Linear (Air)

70

Figure 5–3. Trend analysis and comparison between CT number and its corresponding calculated linear attenuation coefficient of scans completed in a water bath (Yw), saline solution (Ys), and air environments (Ya)

at 80kVp.

Figure 5–4. Trend analysis and comparison between CT number and its corresponding calculated linear attenuation coefficient of scans completed in a water bath (Yw), saline solution (Ys), and air environments (Ya)

at 80kVp.

Yw = 5204.1x - 923.76

Ys = 5225.8x - 931.88

Ya = 5086.9x - 985.32

-300

-100

100

300

500

700

900

1100

1300

1500

0.1 0.2 0.3 0.4 0.5 0.6

CT

Num

ber (

HU

)

Linear Attenuation Coefficient (cm-1)

120kVp Water Bath Saline Air Linear (Water Bath) Linear (Saline) Linear (Air)

Yw = 5201.1x - 878

Ys = 5337.6x - 887.32

Ya = 5172.6x - 949.62

-300

-100

100

300

500

700

900

1100

1300

1500

0.1 0.2 0.3 0.4 0.5 0.6

CT

Num

ber (

HU

)

Linear Attenuation Coefficient (cm-1)

135kVp Water Bath Saline Air Linear (Water Bath) Linear (Saline) Linear (Air)

71

Illustrated in figures 5-1 to 5-4, over the spectrum of calculated linear attenuation

coefficients, the corresponding CT numbers are closely aligned between water bath and

saline solution scans and largely varying for that of air scans.  

The measured CT numbers obtained from the water bath and saline solution environments

are closely aligned. However, the ΔCT fluctuates depending on the material composition.

This fluctuation has a notable trend of increasing ΔCT with increasing hA% added to each

polymeric base. The increase in hA increases the values of two key material properties

related to the CT number, ρ and elemental composition that pertains to effective atomic

numbers, ZƮ and ZR. With the exception of outlier data at PVDF95% / hA5%, the ranges of

ΔCT are notably higher in scans performed at 80kVp; these can be noted as variations

between -22.64 to 31.82 HU between water bath and saline solution scans respectively.

However, as the applied x-ray voltage is increased, the effective range of ΔCT is significantly

decreased, notably at 135kVp to a range of -12.60 to 13.80 HU. In contrast, a comparison of

CT number values between water bath and air scans demonstrates large ΔCT values even at

135kVp with a range between -38.78 and 105.89 HU.

From this comparison, two specific deductions can be made. Firstly, both ρ and atomic

number ZƮ and ZR are directly related to the range in ΔCT values. Addition of hA in discrete

amounts to separate polymers seems to have varying effects on ΔCT, however, the general

trend is an increase in ΔCT with increasing hA%. Previous studies that evaluated the effect of

5.4 Discussion

5.4.1 CT Number Comparison

72

increasing hA% on CT scans have noted that due to the polychromatic nature of CT there is a

resulting variation in beam hardening effect, that requires beam hardening correction

algorithms to compensate[13], [62], [63], [124]. The beam hardening correction algorithm,

however, is calibrated using a water bath, or water-equivalent environment which can cause

variations in measured CT number when using saline solution and air environments as

demonstrated.

Secondly, the ΔCT variation with change in applied voltage, ρ, and elemental composition all

pertain to the characteristic linear attenuation coefficient, µ. Though the ρ and elemental

composition remain consistent between scans in different environments, the resulting

effective energy within each environment will change, thus altering the expected µ values.

To analyze the effect of material properties on CT numbers in different scanning media, it is

pertinent to describe how material properties and CT numbers are related through µ; µ is a

combination of material properties and resulting effective energy following beam hardening

of the incident polychromatic beam. The effective energy is expected to be similar for saline

solution scans whereas the air scans should have an effective energy significantly lower due

to the absence of a water or water-equivalent material to remove lower energy x-ray photons

from the x-ray spectrum. Table 5 demonstrates this phenomenon with comparative calculated

keV in which the values differ by no more than 5keV between the water bath and saline

solution environment scans. In contrast, the difference can be as large as 13keV between

water bath and air environment scans.

5.4.2 Material Property Comparisons – Linear Attenuation Coefficient

73

As mentioned, the effective energy can be further characterized in combination with the

material properties of ρ, ZƮ and ZR through calculated µ values. The µ values show a

relatively high level of consistency between water bath and saline solution environment

scans. A maximum difference of 5% deviation of µsaline to µwater was exhibited among all

applied voltages with most of the deviation nearing 0% at applied voltages aside from

80kVp; µair however had large percentage deviations from µwater at all applied voltages,

reaching as high as 14.4%. This phenomenon can be greatly attributed to the calculated

effective energies. As indicated by equations (2) and (4), the effective energy affects the

attenuation coefficient both in a proportional manner due to incoherent scattering and in an

inversely proportional manner due to photoelectric absorption and coherent scattering. The

contribution of incoherent scattering to the attenuation coefficient, as noted in equation (4), is

not a property of effective atomic number and thus will be constant, however photoelectric

absorption and coherent scattering tend to decrease the attenuation coefficient with

increasing effective atomic number. This variation of increasing attenuation coefficient with

increasing effective atomic number is more apparent in the percentage deviation of µair to

µwater as opposed to µsaline to µwater due to the significantly lower effective energy values from

air environment scans.

The least amount of variation in CT number is observed at 120kVp between µsaline to µwater

percentage deviation. As noted in table 5-4, the effective energies between saline and water

are most closely aligned and thus will show the least amount of variation in linear attenuation

coefficient, regardless of change in effective atomic number. This, in turn, should result in

closely aligned CT number values between saline solution and water bath environment scans.

74

Through our study we have proposed the use of enclosed bags of saline solution as a

surrounding environment for CT phantoms when a water bath or rigid water-equivalent

environment is suboptimal. This can allow for accurate CT scans to be conducted under a

dry, shape conforming environment. Ideally, the use of saline solution bags will have the

least variation from water bath scans at all applied voltages (80, 100, 120 and 135kVp) and

demonstrate densities most closely aligned to the effective atomic numbers of water (ZT and

ZR). As compared to the use of merely a water solution, a saline solution is readily available

in a clinical setting while also resembling the scanning conditions introduced to native

vessels prior to the addition of contrasting agents. By noticing that the saline solution

medium allows for similar results to that of the water bath medium, we can suggest that

saline solution can act as an alternative scanning environment, which can conform, to any

shape complexities and phantom fragility.

This study aimed to encourage the fabrication of CT phantoms to a higher degree of

anthropomorphic behavior in order to more closely mimic native human tissues by CT

numbers ranging between that of -100 to 1100 HU. Further studies necessitate the validation

of fragile, complex and anthropomorphic phantoms under controlled enclosed saline solution

environments. Varied thicknesses and conformations of saline solutions may also be assessed

to further this research for clinical studies. Ultimately, the aim is to improve CT scanning

phantom studies in such a way that the CT phantoms used may wholly resemble its desired

tissues and environments.

5.5 Summary

75

From our research, we have noted the following key findings:

1. CT Number Fine-Tuning: The control of CT number can be completed and fine-tuned

through both polymeric composite and foam fabrication techniques. The ranges to

which we have successfully done so are necessitated towards the cardiac system. As

such the range of control includes: -225 to 0HU for adipose and fatty plaque, 0 to

100HU for soft tissue, and 0 to 1200HU for range of coronary plaque.  

2. Specified CT Property Control: CT numbers have been noted as controlled through

the processing techniques of polymeric compounding and batch foaming. Both are

noted through the control of linear attenuation coefficient however more specifically

this involves mass density for select foam systems and mass density together with

effective atomic number for varied composites.  

Chapter 6 Conclusions and Recommendations

6.1 Conclusion

76

3. Alterative Scanning Media: Due to the potential shape complexity and fragility of a

cardiac phantom by its complex system, the alternative scanning media of enclosed

saline solution can be alternatively used in place of a water bath or solid water bath

equivalent. This environment is closely aligned to that of a water bath medium while

also allowing for an open space environment for the placement of any type of CT

phantom.  

CT phantoms are capable of enhancing the potential for CT scanning in terms of diagnostic

ability, imaging quality and calibration techniques. The use of polymers is often considered

due to its flexibility in material properties when characterizing and fabricating CT phantoms.

By being able to design and tailor key material properties, the attenuation and thus,

characteristic CT number can be calibrated to match any desired tissue. This capability is

particularly important for studies involving the cardiac tissue and its coronary artery

environment. The intricate and detailed cardiac system requires that various be mimicked.

Further, when detailing the coronary artery itself, to define coronary plaque, a wide range of

CT number values are required to mimic both the environment and the varied levels of

coronary plaque.

This research thesis investigated polymeric fabrication techniques that can both

increase and decrease the CT number in a controlled manner of a base polymer to mimic the

desired tissue. Firstly, the fabrication of CT phantoms through polymeric composite twin

screw compounding assessed the potential of increasing control of CT number. This study

considered polymer matrices such as PE, TPU, and PVDF in combination with discrete

amounts of micro-sized particle hA through twin screw compounding to attain uniform

mixing of hA particles within the polymeric matrix. This includes the soft tissue of the heart

77

and arterial wall, surrounding adipose tissue and various types of coronary plaque including

increasing levels of calcification. Secondly, the fabrication of polymeric foams was studied

for its capability in mimicking low density tissues, particularly within the cardiac

environment such as adipose tissue. This fabrication technique involved TPU and a batch

polymeric foaming technique utilizing a physical blowing agent of CO2. Following the

fabrication techniques, polymeric CT phantoms then necessitated an environment in which

they could be scanned without the use of water or a rigid water equivalent due to potential

sample fragility. Since, most commonly, CT phantoms are scanned either in a water bath or a

solid PMMA surrounding environment, any fabricated samples are limited in size, shape and

design.

The current research has demonstrated the ability of polymeric fabrication techniques and

specified scanning environments to be utilized for cardiac CT phantom studies. However,

specific to each study, limitations of each have been listed.

CT phantom fabrication has shown a closely aligned predictive nature at effective

atomic numbers and densities closer to that of water. With deviation from that of water at a

Zτ of 7.49 and a ZR of 6.95 with a mass density of 1.0 g/cm3 we find that the theoretical

predictive model seems to deviate from our results causing, also for more variation in the

measured data. Furthermore, the use of varied kVps illustrates that the values, which are

closely aligned to the theoretical, are of 100 or 120kVp. The use of 80kVp and 135kVp tend

to show a greater variation.

6.2 Limitations

78

Similarly, in terms of sample consistency, the closer the samples were to

characteristics of water, the less variation in CT properties. When referenced to chapters 3, 4,

and 5, it can be noted that there are smaller error bars associated with phantoms with

characteristics closer to water. However, large and small effective atomic numbers from

added hA illustrated greater variation in CT number. Additionally, chapter 4 illustrates

cellular foams with potential variation in processing technique. As can be seen, there is some

variation with the same processing technique for CT number. Samples processed at 0.5 and 1

min in the heated water bath have varied densities and thus CT number, however at longer

processing times we find that there is more consistency.

Additionally, our samples were fabricated as cylindrical shapes, similar to

commercial phantom inserts such as the Gammex 467 phantom. CT scans are not

dimensionally critical as it is a polychromatic, gantry-loaded technique. Thus slight

variations in length, width or thickness due to processing technique should not affect the CT

properties. However, large variations in shape and thickness of material can affect the

accuracy of the scans thus potentially causing variations from the theoretical model

illustrated.

The fabrication and scanning techniques for both polymeric composite and foam phantoms

act as the starting point for creating a cardiac system phantom. The control of CT number

allows for the components of the cardiac system to be mimicked to the desire of the

physician or researcher. It is suggested that following this thesis study, first a geometrically

6.3 Recommendations

79

accurate cardiac system CT phantom be fabricated. This system can then be CT scanned and

compared against patient scans to determine its likeness.

The geometrically accurate phantom can be considered to be fabricated both under

static and dynamic conditions. Depending on the research direction, a static cardiac system

phantom would likely be aimed at studies involving coronary disease, particularly that of

lumen size identification under varied plaque levels of calcification. The goal within the

static cardiac phantom would be to determine scanning methods, post processing algorithms

and image diagnostics, under which the lumen size, or patency, can be identified accurately.

It would be suggested that by utilizing the fabrication techniques of polymeric composites,

particularly the polymer / hA system, a wide range of varied coronary plaque can be

fabricated and utilized.

Under dynamic conditions, various material and mechanical properties must be

considered. The use of both the compounding and foaming techniques can be used in

combination with a polymer material selection process in which ideal mechanical properties

are selected. The fabrication of a dynamic heart phantom can aid in studies improving motion

scans of the heart. By creating a system which can be consistently scanned under various

motion conditions, mimicking that of the heart, these scans can be tested and improved upon,

again by means of controlled scanning conditions, post processing algorithms and image

diagnostics.

Lastly, as this study has identified a method in which a polymeric system can both be

increased and decreased in CT number by fabrication techniques, it can be suggested that

various intricate and complex CT phantoms be fabricated. A first means of fabrication may

have various composite of foam processing techniques within a single material. Potentially,

80

the various structures such as the cardiac wall and coronary artery tissues may require

different levels of CT number within the same structure. Though multiple materials may be

stacked upon one another, it is possible to use a single product with functionally graded

properties.

By means of 3D printing technology and the implementation of in-house polymer

composite and foam fabrication, a number of different low and high density tissues, accurate

in both geometry and CT number, be fabricated. Potentially, low density phantoms with

uniformly dispersed cellular structure can be fabricated based on the desired foam size and

3D printer resolution. The use of 3D printing can also accurately fabricate delicate and small

dimensional objects including the coronary artery and degrees of various plaque.

Additionally, due to the ease of fabrication, mold-ability and machinability of a wide variety

of polymeric systems, phantoms can be fabricated in a number of desired shapes and

conformations. Lastly, bone studies can involve both foam and composite fabrication due to

the porous nature of the bone as well as the high density values.

81

[1] CANSIM, “Deaths, by cause, Diseases of the circulatory system (I00 to I99), age

group and sex, Canada, annual (number),” 2014.

[2] K. Pohle, S. Achenbach, B. Macneill, D. Ropers, M. Ferencik, F. Moselewski, U.

Hoffmann, T. J. Brady, I.-K. K. Jang, and W. G. Daniel, “Characterization of non-

calcified coronary atherosclerotic plaque by multi-detector row CT: Comparison to

IVUS,” Atherosclerosis, vol. 190, no. 1, pp. 174–180, Jan. 2007.

[3] S. E. Nissen and P. Yock, “Intravascular ultrasound: novel pathophysiological insights

and current clinical applications.,” Circulation, vol. 103, no. 4, pp. 604–616, 2001.

[4] N. S. Paul, J. Blobel, H. Kashani, M. Rice, and A. Ursani, “Quantification of arterial

plaque and lumen density with MDCT.,” Med. Phys., vol. 37, no. 8, pp. 4227–4237,

2010.

[5] W. Kristanto, P. M. a Van Ooijen, M. J. W. Greuter, J. M. Groen, R. Vliegenthart, and

M. Oudkerk, “Non-calcified coronary atherosclerotic plaque visualization on CT:

Effects of contrast-enhancement and lipid-content fractions,” Int. J. Cardiovasc.

Imaging, vol. 29, no. 5, pp. 1137–1148, 2013.

Bibliography

82

[6] J.-H. H. Choi, J. K. Min, T. M. Labounty, F. Y. Lin, D. D. Mendoza, D. H. Shin, N. S.

Ariaratnam, S. Koduru, J. F. Granada, T. C. Gerber, J. K. Oh, H.-C. C. Gwon, and Y.

H. Choe, “Intracoronary transluminal attenuation gradient in coronary CT angiography

for determining coronary artery stenosis,” JACC Cardiovasc. Imaging, vol. 4, no. 11,

pp. 1149–1157, Nov. 2011.

[7] Y. Funama, S. Oda, D. Utsunomiya, and K. Taguchi, “Coronary Artery Stent

Evaluation by Combining Iterative Reconstruction and High-resolution Kernel

at Coronary CT Angiography,” Acad. Radiol., vol. 19, no. 11, pp. 1324–1331,

2012.

[8] T. Boltz, W. Pavlicek, R. Paden, M. Renno, A. Jensen, and M. Akay, “An

anthropomorphic beating heart phantom for cardiac X-ray CT imaging evaluation,” J.

Appl. Clin. Med. Phys., vol. 11, no. 1, pp. 191–199, 2010.

[9] P. M. Shikhaliev, “Dedicated phantom materials for spectral radiography and CT.,”

Phys. Med. Biol., vol. 57, no. 6, pp. 1575–93, Mar. 2012.

[10] Y. Watanabe, “Derivation of linear attenuation coefficients from CT numbers for low-

energy photons.,” Phys. Med. Biol., vol. 44, no. 9, pp. 2201–2211, 1999.

[11] D.-H. Kim, H.-J. Kim, C.-L. Lee, H.-M. Cho, H.-S. Park, S.-W. Lee, Y.-N. Choi, Y.-

S. Kim, and S.-J. Park, “Absolute measurement of effective atomic number and

electron density using dual-energy computed tomography images,” in SPIE Medical

Imaging, 2012, vol. 8313, p. 83132X.

[12] L. H. Sperling, Introduction to physical polymer science. 2006.

[13] J. M. Deuerling, D. J. Rudy, G. L. Niebur, and R. K. Roeder, “Improved accuracy of

cortical bone mineralization measured by polychromatic microcomputed tomography

using a novel high mineral density composite calibration phantom.,” Med. Phys., vol.

37, no. 9, pp. 5138–5145, 2010.

83

[14] P. Homolka, a Gahleitner, M. Prokop, and R. Nowotny, “Optimization of the

composition of phantom materials for computed tomography.,” Phys. Med. Biol., vol.

47, no. 16, pp. 2907–2916, 2002.

[15] G. J. Kemerink, H. H. Kruize, R. J. Lamers, and J. M. van Engelshoven, “Density

resolution in quantitative computed tomography of foam and lung.,” Medical physics,

vol. 23, no. 10. pp. 1697–1708, 1996.

[16] G. T. Herman and H. T. Gabor, Fundamentals of computerized tomography: image

reconstruction from projections, vol. 325, no. 5946. Laboratory of Atomic and Solid-

State Physics, Cornell University, Ithaca, NY 14853, USA. nmg32@cornell.edu DOI -

10.1126/science.1176112 SRC - Pubmed ID2 - 19745146 FG - 0, 2009.

[17] J. R. Mikolich, “Cardiac computed tomographic angiography and the primary care

physician.,” J. Am. Osteopath. Assoc., vol. 112, no. 5, pp. 267–75, 2012.

[18] H. Spitz, M. Jenkins, J. Lodwick, and R. Bornschein, “A new anthropometric phantom

for calibrating in vivo measurements of stable lead in the human leg using x-ray

fluorescence.,” Health Phys., vol. 78, no. 2, pp. 159–169, 2000.

[19] J. H. Hubbell, “Review of photon interaction cross section data in the medical and

biological context.,” Phys. Med. Biol., vol. 44, no. 1, pp. R1–R22, Jan. 1999.

[20] S. B. Kaginelli, T. Rajeshwari, Sharanabasappa, B. R. Kerur, and A. S. Kumar,

“Effective atomic numbers and electron density of dosimetric material.,” J. Med.

Phys., vol. 34, no. 3, pp. 176–179, 2009.

[21] E. C. McCullough and T. W. Holmes, “Acceptance testing computerized radiation

therapy treatment planning systems: direct utilization of CT scan data.,” Med. Phys.,

vol. 12, no. 2, pp. 237–242, 1985.

[22] S. J. Thomas, “Relative electron density calibration of CT scanners for radiotherapy

treatment planning,” Br. J. Radiol., vol. 72, no. AUG., pp. 781–786, 1999.

84

[23] S. Schroeder, a F. Kopp, a Baumbach, C. Meisner, a Kuettner, C. Georg, B.

Ohnesorge, C. Herdeg, C. D. Claussen, and K. R. Karsch, “Noninvasive detection and

evaluation of atherosclerotic coronary plaques with multislice computed

tomography.,” J. Am. Coll. Cardiol., vol. 37, no. 5, pp. 1430–1435, 2001.

[24] J. I. Murungi, S. Thiam, R. E. Tracy, J. W. Robinson, and I. M. Warner, “Elemental

analysis of soft plaque and calcified plaque deposits from human coronary arteries and

aorta.,” J. Environ. Sci. Health. A. Tox. Hazard. Subst. Environ. Eng., vol. 39, no. 6,

pp. 1487–1496, 2004.

[25] R. Molteni, “From to Hounsfield Units in Cone Beam Volumetric Imaging: the effect

of artifacts,” 2011.

[26] R. A. Rutherford, B. R. Pullan, and I. Isherwood, “Measurement of effective atomic

number and electron density using an EMI scanner.,” Neuroradiology, vol. 11, no. 1,

pp. 15–21, 1976.

[27] T. P. Szczykutowicz, Z. Qi, and G.-H. Chen, “A simple image based method for

obtaining electron density and atomic number in dual energy CT,” Cleve. Clin. J.

Med., vol. 7961, no. 1, p. 79613A–79613A–6, 2011.

[28] M. J. Berger, J. H. Hubbell, S. M. Seltzer, J. Chang, J. S. Coursey, R. Sukumar, and D.

S. Zucker, “XCOM: photon cross sections database,” NIST Stand. Ref. Database, vol.

8, pp. 87–3597, 1998.

[29] J. H. Hubbell, W. J. Veigele, E. a. Briggs, R. T. Brown, D. T. Cromer, and R. J.

Howerton, “Atomic form factors, incoherent scattering functions, and photon

scattering cross sections,” J. Phys. Chem. Ref. Data, vol. 4, no. 3, pp. 471–538, 1975.

[30] J. Hubbell and I. Øverbø, “Relativistic atomic form factors and photon coherent

scattering cross sections,” J. Phys. Chem. Ref. Data, vol. 8, no. 1, p. 69, 1979.

[31] J. H. Scofield, “Theoretical photoionization cross sections from 1 to 1500 keV.,”

Lawrence Livermore Lab. Rep. No. UCRL-51326, 1973.

85

[32] J. H. Hubbell, H. a. Gimm, and I. O ̸verbo̸, “Pair, Triplet, and Total Atomic Cross

Sections (and Mass Attenuation Coefficients) for 1 MeV-100 GeV Photons in

Elements Z=1 to 100,” J. Phys. Chem. Ref. Data, vol. 9, no. 4, p. 1023, 1980.

[33] M. a Ebert, J. Lambert, and P. B. Greer, CT-ED conversion on a GE Lightspeed-RT

scanner: influence of scanner settings., vol. 31, no. 2. 2008.

[34] C. Bai, L. Shao, A. J. Da Silva, and Z. Zhao, “A Generalized Model for the

Conversion from CT Numbers to Linear Attenuation Coefficients,” IEEE Trans. Nucl.

Sci., vol. 50, no. 5 II, pp. 1510–1515, 2003.

[35] C. B. Saw, A. Loper, K. Komanduri, T. Combine, S. Huq, and C. Scicutella,

“Determination of CT-to-density conversion relationship for image-based treatment

planning systems,” Med. Dosim., vol. 30, no. 3, pp. 145–148, 2005.

[36] S. Brown, D. L. Bailey, K. Willowson, and C. Baldock, “Investigation of the

relationship between linear attenuation coefficients and CT Hounsfield units using

radionuclides for SPECT,” Appl. Radiat. Isot., vol. 66, no. 9, pp. 1206–1212, Sep.

2008.

[37] S. Blankespoor, X. Wu, K. Kalki, J. K. Brown, C. E. Cann, and B. H. Hasegawa,

“Attenuation correction of SPECT using x-ray CT on an emission-transmission CT

system,” IEEE Nucl. Sci. Symp. Med. Imaging Conf., vol. 2, pp. 1126–1130, 1995.

[38] K. J. LaCroix, B. M. W. Tsui, B. H. Hasegawa, and J. K. Brown, “Investigation of the

use of X-ray CT images for attenuation compensation in SPECT,” IEEE Trans. Nucl.

Sci., vol. 41, no. 6, pp. 2793–2799, 1994.

[39] P. Kinahan, “PET / CT Issues  : CT-based attenuation correction ( CTAC ), Artifacts ,

and Motion Correction,” 2008.

[40] P. E. Kinahan, B. H. Hasegawa, and T. Beyer, “X-ray-based attenuation correction for

positron emission tomography/computed tomography scanners,” Semin Nucl Med, vol.

33, no. 3, pp. 166–179, 2003.

86

[41] P. E. Kinahan, D. W. Townsend, T. Beyer, and D. Sashin, “Attenuation correction for

a combined 3D PET/CT scanner,” Med. Phys., vol. 25, no. 10, pp. 2046–2053, 1998.

[42] D. R. White, R. J. Martin, and R. Darlison, “Epoxy resin based tissue substitutes,” Br.

J. Radiol., vol. 50, no. 599, pp. 814–821, 1977.

[43] M. Abella, A. M. Alessio, D. a Mankoff, L. R. MacDonald, J. J. Vaquero, M. Desco,

and P. E. Kinahan, “Accuracy of CT-based attenuation correction in PET/CT bone

imaging,” Phys. Med. Biol., vol. 57, no. 9, pp. 2477–2490, May 2012.

[44] a. H. R. Lonn, “Evaluation of method to minimize the effect of X-ray contrast in PET-

CT attenuation correction,” 2003 IEEE Nucl. Sci. Symp. Conf. Rec. (IEEE Cat.

No.03CH37515), vol. 3, 2003.

[45] P. R. Costa, “Computer Tomography Phantom Applications,” in The Phantoms of

Medical and Health Physics, 2014, pp. 123 – 142.

[46] M. E. Poletti, D. Gonçalves, and I. Mazzaro, “X-ray scattering from human breast

tissues and breast-equivalent materials.,” Phys. Med. Biol., vol. 47, no. 1, pp. 47–63,

2002.

[47] J. W. Byng, J. G. Mainprize, and M. J. Yaffe, “X-ray characterization of breast

phantom materials.,” Phys. Med. Biol., vol. 43, no. 5, pp. 1367–1377, 1998.

[48] S. Ramakrishna, J. Mayer, E. Wintermantel, and K. W. Leong, “Biomedical

application of polymer-composite materials: a review,” Compos. Sci. Technol., vol.

61, pp. 1189–1224, 2001.

[49] T. Villmow, P. Pötschke, S. Pegel, L. Häussler, and B. Kretzschmar, “Influence of

twin-screw extrusion conditions on the dispersion of multi-walled carbon nanotubes in

a poly(lactic acid) matrix,” Polymer (Guildf)., vol. 49, no. 16, pp. 3500–3509, 2008.

[50] J. . Cho and D. . Paul, “Nylon 6 nanocomposites by melt compounding,” Polymer

(Guildf)., vol. 42, no. 3, pp. 1083–1094, 2001.

87

[51] Aroon V. Shenoy, Rheology of Filled Polymer Systems. 1999.

[52] F. Hussain, “Review article: Polymer-matrix Nanocomposites, Processing,

Manufacturing, and Application: An Overview,” J. Compos. Mater., vol. 40, no. 17,

pp. 1511–1575, 2006.

[53] L. Biasci, M. Aglietto, G. Ruggeri, and A. D’Alessio, “Functionalization of

montmorillonite by acrylamide polymers containing side-chain ammonium cations,”

Polym. Adv. Technol., vol. 6, no. 10, pp. 662–670, 1995.

[54] L. Fang, Y. Leng, and P. Gao, “Processing and mechanical properties of

HA/UHMWPE nanocomposites,” Biomaterials, vol. 27, no. 20, pp. 3701–3707, 2006.

[55] Z. Wang and T. J. Pinnavaia, “Nanolayer Reinforcement of Elastomeric

Polyurethane,” Chem. Mater., vol. 10, no. 12, pp. 3769–3771, 1998.

[56] V. Sendijarevic, “Daniel, Klempner, Handbook of Polymeric Foams and Foam

Technology.,” Munich Hanser, 1991.

[57] K. T. Okamoto, “Microcellular Processing,” Radiol. Clin. North Am., vol. 41, no. 2, p.

186, 2003.

[58] G. Liu, C. B. Park, and J. a Lefas, “Production of low-density LLDPE foams in

rotational molding,” Polym. Eng. Sci., vol. 38, no. 12, pp. 1997–2009, 1998.

[59] G. Guo, K. H. Wang, C. B. Park, Y. S. Kim, and G. Li, “Effects of nanoparticles on

the density reduction and cell morphology of extruded metallocene polyethylene/wood

fiber nanocomposites,” J. Appl. Polym. Sci., vol. 104, no. 2, pp. 1058–1063, 2007.

[60] W. Michaeli and S. Habibi-Naini, “New Technology for Foam Injection Moulding

with Physical Blowing Agents,” in Blowing Agents and Foaming Processes, 2001, p.

Paper 9, Page 1 – 8.

88

[61] S. N. Leung, C. B. Park, D. Xu, H. Li, and R. G. Fenton, “Computer simulation of

bubble-growth phenomena in foaming,” Ind. Eng. Chem. Res., vol. 45, no. 23, pp.

7823–7831, 2006.

[62] G. J. Kazakia, a J. Burghardt, S. Cheung, and S. Majumdar, “Assessment of bone

tissue mineralization by conventional x-ray microcomputed tomography: comparison

with synchrotron radiation microcomputed tomography and ash measurements.,” Med.

Phys., vol. 35, no. 7, pp. 3170–3179, 2008.

[63] A. J. Burghardt, G. J. Kazakia, A. Laib, and S. Majumdar, “Quantitative assessment of

bone tissue mineralization with polychromatic micro-computed tomography,” Calcif.

Tissue Int., vol. 83, no. 2, pp. 129–138, 2008.

[64] L. Mulder, J. H. Koolstra, and T. M. G. J. Van Eijden, “Accuracy of microCT in the

quantitative determination of the degree and distribution of mineralization in

developing bone.,” Acta radiol., vol. 45, no. 7, pp. 769–777, 2004.

[65] J. a. Meganck, K. M. Kozloff, M. M. Thornton, S. M. Broski, and S. a. Goldstein,

“Beam hardening artifacts in micro-computed tomography scanning can be reduced by

X-ray beam filtration and the resulting images can be used to accurately measure

BMD,” Bone, vol. 45, no. 6, pp. 1104–1116, 2009.

[66] W. J. Yang, K. M. Chen, L. F. Pang, Y. Guo, J. Y. Li, H. Zhang, and Z. L. Pan, “High-

definition computed tomography for coronary artery stent imaging: a phantom study,”

Korean J Radiol, vol. 13, no. 1, pp. 20–26, 2012.

[67] M. Toepker, G. Euller, E. Unger, M. Weber, D. Kienzl, C. J. Herold, and H. Ringl,

“Stenosis quantification of coronary arteries in coronary vessel phantoms with second-

generation dual-source CT: Influence of measurement parameters and limitations,”

Am. J. Roentgenol., vol. 201, no. 2, pp. W227–34, Aug. 2013.

[68] H. Spitz and R. Bornsched, “A New Anthropometric Phantom for Calibrating in vivo

Measurements of Stable Lead in the Human Leg Using X-ray Fluorescence,” Health

Phys., vol. 78, no. 2, pp. 159–169, 2000.

89

[69] B. D. R. White, M. Sc, and D. Ph, “Epoxy resin based tissue substitutes,” Br. J.

Radiol., vol. 50, no. 599, pp. 814–821, 1977.

[70] C. Constantinou, “An electron density calibration phantom for CT-based treatment

planning computers,” Med. Phys., vol. 19, no. 2, p. 325, 1992.

[71] F. Cademartiri, N. R. Mollet, G. Runza, N. Bruining, R. Hamers, P. Somers, M.

Knaapen, S. Verheye, M. Midiri, G. P. Krestin, and P. J. de Feyter, “Influence of

intracoronary attenuation on coronary plaque measurements using multislice computed

tomography: observations in an ex vivo model of coronary computed tomography

angiography.,” Eur. Radiol., vol. 15, no. 7, pp. 1426–31, Jul. 2005.

[72] C. Robert, “ACCF/ACR/SCCT/SCMR/ASNC/NASCI/SCAI/SIR 2006

Appropriateness Criteria for Cardiac Computed Tomography and Cardiac Magnetic

Resonance Imaging,” J. Am. Coll. Radiol., vol. 3, no. 10, pp. 751–771, 2006.

[73] D. B. Mark, D. S. Berman, M. J. Budoff, J. J. Carr, T. C. Gerber, H. S. Hecht, M. a

Hlatky, J. M. Hodgson, M. S. Lauer, J. M. Miller, R. L. Morin, D. Mukherjee, M.

Poon, G. D. Rubin, and R. S. Schwartz,

“ACCF/ACR/AHA/NASCI/SAIP/SCAI/SCCT 2010 expert consensus document on

coronary computed tomographic angiography: a report of the American College of

Cardiology Foundation Task Force on Expert Consensus Documents.,” Catheter.

Cardiovasc. Interv., vol. 76, no. 2, pp. E1–E42, 2010.

[74] K. A. Eagle, “ACC/AHA 2004 guideline update for coronary artery bypass graft

surgery: a report of the American College of Cardiology/American Heart Association

Task Force on Practice Guidelines (Committee to Update the 1999 Guidelines for

Coronary Artery Bypass Graft),” Circulation, vol. 110, pp. e340 – e437, 2005.

[75] S. C. Smith, J. T. Dove, A. K. Jacobs, J. Ward Kennedy, D. Kereiakes, M. J. Kern, R.

E. Kuntz, J. J. Popma, H. V Schaff, D. O. Williams, R. J. Gibbons, J. P. Alpert, K. a

Eagle, D. P. Faxon, V. Fuster, T. J. Gardner, G. Gregoratos, R. O. Russell, and S. C.

Smith, “ACC/AHA guidelines for percutaneous coronary intervention (revision of the

90

1993 PTCA guidelines)—executive summary,” J. Am. Coll. Cardiol., vol. 37, no. 8,

pp. 2215–2238, 2001.

[76] J. Sun, Z. Zhang, B. Lu, W. Yu, Y. Yang, Y. Zhou, Y. Wang, and Z. Fan,

“Identification and quantification of coronary atherosclerotic plaques: A comparison

of 64-MDCT and intravascular ultrasound,” Am. J. Roentgenol., vol. 190, no. 3, pp.

748–754, Mar. 2008.

[77] K. Jansen, M. Wu, A. F. W. van der Steen, and G. van Soest, “Lipid detection in

atherosclerotic human coronaries by spectroscopic intravascular photoacoustic

imaging,” Opt. Express, vol. 21, no. 18, p. 21472, 2013.

[78] H. Seifarth, M. Ozgün, R. Raupach, T. Flohr, W. Heindel, R. Fischbach, and D.

Maintz, “64- Versus 16-slice CT angiography for coronary artery stent assessment: in

vitro experience.,” Invest. Radiol., vol. 41, no. 1, pp. 22–27, 2006.

[79] C. Cheng, L. Zhao, M. Wolanski, Q. Zhao, J. James, K. Dikeman, M. Mills, M. Li, S.

P. Srivastava, X. Q. Lu, and I. J. Das, “Comparison of tissue characterization curves

for different CT scanners: implication in proton therapy treatment planning,” Transl.

Cancer Res., vol. 1, no. 4, pp. 236–246, 2013.

[80] F. Cademartiri, J. D. Schuijf, F. Pugliese, N. R. Mollet, J. W. Jukema, E. Maffei, L. J.

Kroft, a Palumbo, D. Ardissino, P. W. Serruys, G. P. Krestin, E. E. van der Wall, P. J.

de Feyter, and J. J. Bax, “Usefulness of 64-slice multislice computed tomography

coronary angiography to assess in-stent restenosis,” J.Am.Coll.Cardiol., vol. 49, no.

22, pp. 2204–2210, 2007.

[81] a. F. Kopp, S. Schroeder, a. Baumbach, a. Kuettner, C. Georg, B. Ohnesorge, M.

Heuschmid, R. Kuzo, and C. D. Claussen, “Non-invasive characterisation of coronary

lesion morphology and composition by multislice CT: First results in comparison with

intracoronary ultrasound,” Eur. Radiol., vol. 11, no. 9, pp. 1607–1611, 2001.

[82] A. W. Leber, A. Knez, A. Becker, C. Becker, F. Von Ziegler, K. Nikolaou, C. Rist, M.

Reiser, C. White, G. Steinbeck, and P. Boekstegers, “Accuracy of multidetector spiral

91

computed tomography in identifying and differentiating the composition of coronary

atherosclerotic plaques: A comparative study with intracoronary ultrasound,” J. Am.

Coll. Cardiol., vol. 43, no. 7, pp. e340 – 437, 2004.

[83] H.-Y. Mi, X. Jing, M. R. Salick, T. M. Cordie, X.-F. Peng, and L.-S. Turng,

“Morphology, mechanical properties, and mineralization of rigid thermoplastic

polyurethane/hydroxyapatite scaffolds for bone tissue applications: effects of

fabrication approaches and hydroxyapatite size,” J. Mater. Sci., vol. 49, no. 5, pp.

2324–2337, Dec. 2013.

[84] M. Selvakumar, S. K. Jaganathan, G. B. Nando, and S. Chattopadhyay, “Synthesis and

Characterization of Novel Polycarbonate Based Polyurethane/Polymer Wrapped

Hydroxyapatite Nanocomposites: Mechanical Properties, Osteoconductivity and

Biocompatibility,” J. Biomed. Nanotechnol., vol. 10, no. xx, pp. 1–15, 2014.

[85] W. Bonfield, “Polyethylene as an Analogous Material for Bone Replacement,” Ann. N.

Y. Acad. Sci., vol. 523, no. 1, pp. 173–177, 1988.

[86] W. Suchanek and M. Yoshimura, “Processing and properties of hydroxyapatite-based

biomaterials for use as hard tissue replacement implants,” J. Mater. Res., vol. 13, no.

01, pp. 94–117, Jan. 2011.

[87] L. Di Silvio, M. Dalby, and W. Bonfield, “In vitro response of osteoblasts to

hydroxyapatite-reinforced polyethylene composites,” J. Mater. Sci. Mater. Med., vol.

9, pp. 845–849, 1998.

[88] A. V. Shenoy, Rheology of filled polymer systems. Norwell: Kluwer Academic, 1999.

[89] M. Wang, D. Porter, and W. Bonfield, “Processing, characterisation, and evaluation of

hydryoxyapatite reinforced polyethylene composites,” in British Ceramic

Transactions, 1994, pp. 91–95.

[90] E. Bilotti, R. Zhang, H. Deng, M. Baxendale, and T. Peijs, “Fabrication and property

prediction of conductive and strain sensing TPU/CNT nanocomposite fibres,” J.

Mater. Chem., vol. 20, no. 42, p. 9449, 2010.

92

[91] R. Sousa, R. Reis, A. Cunha, and M. Bevis, “Coupling of HDPE/hydroxyapatite

composites by silane-based methodologies,” J. Mater. Sci. …, vol. 4, pp. 475–487,

2003.

[92] T. Yoshizumi and T. Nakamura, “Abdominal Fat: Standardized Technique for

Measurement at CT,” Radiology, vol. 211, pp. 283–286, 1999.

[93] M. van der Glas, Principles of computerized tomographic imaging. 2000.

[94] A. K. Jones, D. E. Hintenlang, and W. E. Bolch, “Tissue-equivalent materials for

construction of tomographic dosimetry phantoms in pediatric radiology.,” Med. Phys.,

vol. 30, no. 8, pp. 2072–2081, 2003.

[95] S. Kinase, M. Kimura, H. Noguchi, and S. Yokoyama, “Development of lung and soft

tissue substitutes for photons,” Radiat. Prot. Dosimetry, vol. 115, no. 1–4, pp. 284–

288, 2005.

[96] Z. H. Levine, M. Li, A. P. Reeves, D. F. Yankelevitz, J. J. Chen, E. L. Siegel, A.

Peskin, and D. N. Zeiger, “A low-cost density reference phantom for computed

tomography,” Med. Phys., vol. 36, no. 2, p. 286, 2009.

[97] K.-P. Chang, S.-H. Hung, Y.-H. Chie, A.-C. Shiau, and R.-J. Huang, “A comparison

of physical and dosimetric properties of lung substitute materials.,” Med. Phys., vol.

39, no. 4, pp. 2013–20, Apr. 2012.

[98] D. L. Tomasko, H. Li, D. Liu, X. Han, M. J. Wingert, L. J. Lee, and K. W. Koelling,

“A Review of CO 2 Applications in the Processing of Polymers,” Ind. Eng. Chem.

Res., vol. 42, pp. 6431–6456, 2003.

[99] R. Rizvi and H. Naguib, “Porosity and composition dependence on electrical and

piezoresistive properties of thermoplastic polyurethane nanocomposites,” J. Mater.

Res., vol. 28, no. 17, pp. 2415–2425, Aug. 2013.

93

[100] N. Hossieny, M. Barzegari, and M. Nofar, “Crystallization of hard segment domains

with the presence of butane for microcellular thermoplastic polyurethane foams,”

Polymer (Guildf)., vol. 55, pp. 651–662, 2014.

[101] S. G. Mosanenzadeh, H. E. Naguib, C. B. Park, and N. Atalla, “Development of

polylactide open-cell foams with bimodal structure for high-acoustic absorption,” J.

Appl. Polym. Sci., vol. 131, no. 7, pp. 1–11, Apr. 2014.

[102] J. W. Shi, H. Z. Dai, L. Shen, and D. F. Xu, “Dual-energy CT: clinical application in

differentiating an adrenal adenoma from a metastasis.,” Acta radiol., Aug. 2013.

[103] J. A. a. Bryant, N. A. a. Drage, and S. Richmond, “CT number definition,” Radiat.

Phys. Chem., vol. 81, no. 4, pp. 358–361, Apr. 2012.

[104] W. A. Kalender, “X-ray computed tomography.,” Phys. Med. Biol., vol. 51, no. 13, pp.

29–43, Jul. 2006.

[105] P. Mah, T. E. Reeves, and W. D. McDavid, “Deriving Hounsfield units using grey

levels in cone beam computed tomography.,” Dentomaxillofac. Radiol., vol. 39, no. 6,

pp. 323–35, Sep. 2010.

[106] D. S. Sharma, S. D. Sharma, K. K. Sanu, S. Saju, D. D. Deshpande, and S. Kannan,

“Performance evaluation of a dedicated computed tomography scanner used for virtual

simulation using in-house fabricated CT phantoms.,” J. Med. Phys., vol. 31, no. 1, pp.

28–35, 2006.

[107] E. C. McCullough, “Acceptance testing computerized radiation therapy treatment

planning systems: Direct utilization of CT scan data,” Med. Phys., vol. 12, no. 2, p.

237, 1985.

[108] G. Landry, J. Seco, M. Gaudreault, and F. Verhaegen, “Deriving effective atomic

numbers from DECT based on a parameterization of the ratio of high and low linear

attenuation coefficients.,” Phys. Med. Biol., vol. 58, no. 19, pp. 6851–66, 2013.

94

[109] M. Bazalova, J.-F. Carrier, L. Beaulieu, and F. Verhaegen, “Dual-energy CT-based

material extraction for tissue segmentation in Monte Carlo dose calculations,” Phys.

Med. Biol., vol. 53, pp. 2439–2456, 2008.

[110] W. H. Kim, C. G. Kim, and D. W. Kim, “Optimal CT number range for adipose tissue

when determining lean body mass in whole-body F-18 FDG PET/CT studies,” Nucl.

Med. Mol. Imaging (2010)., vol. 46, no. 4, pp. 294–299, 2012.

[111] W. Michaeli and R. Heinz, “Foam extrusion of thermoplastic polyurethanes (TPU)

using CO2 as a blowing agent,” Macromol. Mater. Eng., vol. 284–285, pp. 35–39,

2000.

[112] L. J. M. Jacobs, M. F. Kemmere, and J. T. F. Keurentjes, “Sustainable polymer

foaming using high pressure carbon dioxide: a review on fundamentals, processes and

applications,” Green Chem., vol. 10, no. 7, p. 731, 2008.

[113] D. Mozejko, “Image Texture , Uniformity , Homogenity and Radiation Dose

Properties in CT,” 2013.

[114] L. Chen, R. Ozisik, and L. S. Schadler, “The influence of carbon nanotube aspect ratio

on the foam morphology of MWNT/PMMA nanocomposite foams,” Polymer

(Guildf)., vol. 51, no. 11, pp. 2368–2375, 2010.

[115] D. Jahani, A. Ameli, M. Saniei, W. Ding, C. B. Park, and H. E. Naguib,

“Characterization of the Structure, Acoustic Property, Thermal Conductivity, and

Mechanical Property of Highly Expanded Open-Cell Polycarbonate Foams,”

Macromol. Mater. Eng., vol. 300, no. 1, pp. 48–56, Jan. 2015.

[116] S. Ghaffari Mosanenzadeh, H. E. Naguib, C. B. Park, and N. Atalla, “Effect of

biopolymer blends on physical and Acoustical properties of biocomposite foams,” J.

Polym. Sci. Part B Polym. Phys., vol. 52, no. 15, pp. 1002–1013, 2014.

[117] S. N. S. Leung, “Mechanism of Cell Nucleation, Growth and Coarsening in Plastic

Foaming: Theory, Simulation and Experiments,” p. 239, 2009.

95

[118] G. T. Herman, Fundamentals of Computerized Tomography: Image Reconstructions

from Projections, vol. 2. 2009.

[119] E. P. S. Sande, A. C. T. Martinsen, E. O. Hole, and H. M. Olerud, “Interphantom and

interscanner variations for Hounsfield units--establishment of reference values for HU

in a commercial QA phantom.,” Phys. Med. Biol., vol. 55, no. 17, pp. 5123–5135,

2010.

[120] U. Schneider, E. Pedroni, and a Lomax, “The calibration of CT Hounsfield units for

radiotherapy treatment planning.,” Phys. Med. Biol., vol. 41, no. 1, pp. 111–124, 1996.

[121] D. D. Robertson and H. K. Huang, “Quantitative bone measurements using x-ray

computed tomography with second-order correction,” Med. Phys., vol. 13, no. 4, pp.

474–479, 2014.

[122] G. Van Gompel, K. Van Slambrouck, M. Defrise, K. J. Batenburg, J. de Mey, J.

Sijbers, and J. Nuyts, “Iterative correction of beam hardening artifacts in CT.,” Med.

Phys., vol. 38 Suppl 1, no. 2011, p. S36, Jul. 2011.

[123] E. C. McCullough, “Photon attenuation in computed tomography,” Med. Phys., vol. 2,

no. 6, pp. 307–320, 1975.

[124] R. J. Fajardo, E. Cory, N. D. Patel, A. Nazarian, A. Laib, R. K. Manoharan, J. E.

Schmitz, J. M. DeSilva, L. M. MacLatchy, B. D. Snyder, and M. L. Bouxsein,

“Specimen size and porosity can introduce error into microCT-based tissue mineral

density measurements.,” Bone, vol. 44, no. 1, pp. 176–184, 2009.