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Detecting Vulnerable Plaques with MultiresolutionAnalysisSushma SrinivasCleveland State University

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Recommended CitationSrinivas, Sushma, "Detecting Vulnerable Plaques with Multiresolution Analysis" (2011). ETD Archive. Paper 279.

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DETECTING VULNERABLE PLAQUES WITH

MULTIRESOLUTION ANALYSIS

SUSHMA SRINIVAS

Bachelor of Engineering Electronics and Communications

University of Mysore

September, 1997

Master of Science - Physics

Cleveland State University

May, 2007

Submitted in partial fulfillment of requirements for the degree

DOCTOR OF ENGINEERING

in

APPLIED BIOMEDICAL ENGINEERING

at the

CLEVELAND STATE UNIVERSITY

November, 2011

Copyright by SUSHMA SRINIVAS 2011

This dissertation has been approved

for the Department of Chemical and Biomedical Engineering

and the College of Graduate Studies by

________________________________________________ ________________________________

Dissertation Committee Chairperson,

Aaron J. Fleischman Ph.D.

Biomedical Engineering, Cleveland Clinic

________________________________________________ ________________________________

Academic Advisor, George P. Chatzimavroudis Ph.D.

Cleveland State University

________________________________________________ ________________________________

Advisor, Miron Kaufman Ph.D.

Dept. of Physics, Cleveland State University

________________________________________________ ________________________________

Advisor, Randolph M. Setser Ph.D.

Manager, Research Collaborations, Angiography & X-Ray

Siemens Healthcare

________________________________________________ ________________________________

Clinical Advisor, Stephen Nicholls M.D, Ph.D.

Heart and Vascular Institute, Cleveland Clinic

________________________________________________ ________________________________

Advisor, William Davros Ph.D.

Diagnostic Radiology, Cleveland Clinic

Dedicated to:

My sound children two inexhaustible acoustic sources

You will NEVER get your P etch D!

Jahnavi (age 7)

I am happy with you on this planet, why do you want me to become an astronaut?

Chandni (age 4)

and

The few souls whose arteries were imaged for this study

ACKNOWLEDGEMENTS

First and foremost, I wish to express gratitude to my advisor, Dr. Aaron

Fleischman who encouraged and challenged me through my dissertation years. His

patience in listening to my viewpoints and reasoning, and strategies for my ideas

are to be admired. I take it as a responsibility to be successful and surpass his

expectations of me, as it is more rewarding to my advisor than words can thank him

for the rich experience in his laboratory.

It is a pleasure to thank my ever accommodating committee. The valuable

advice from Dr. George Chatzimavroudis, there is life beyond PhD helped me start

every day with a positive outlook. I thank him for all his advice on fulfilling academic

requirements and also teaching me medical imaging and signal processing; his

lessons on fluid dynamics were most enjoyable. Words cannot adequately thank Dr.

Miron Kaufman for his advice on choosing projects, mentors and making university

and career choices. I regard highly, his valuable advice of choosing CSU over Case

Western/Univ of Pittsburgh for the sake of my family. I appreciate his efforts and

involvement in the development and training of his students. I must thank Dr.

Randolph Setser for his mentoring during my Masters project as well as my doctoral

studies. I thank him for introducing me to the most beautiful imaging modality

MRI through his clear and comprehensive instructions. I respect his professionalism

and discipline with which he helps students in completing projects. I thank Dr.

Steven Nicholls for his support and for serving as a dissertation committee member.

I also thank Dr. William Davros for his enthusiastic teachings on medical physics and

for serving as a committee member.

I must also thank Dr. Peter Lewin at Drexel University. It was his enthusiasm

for physics and medical applications of ultrasound that brought me into the world of

ultrasonic imaging.

I extend my thanks to Dr. Nicholas Ferrel for culturing MDCK cells and also

providing pancreatic and breast tumor cells; Ken Gorski and Bill Magyar from IVUS

lab core for acquiring OCT images; Lindsey and Paul Bishop for providing peripheral

arteries; Dr. Ofer Reizes for providing fat tissue samples; Dr. Xuemui Gao, from the

laboratory of Dr. Linda Graham for providing rabbit aortic grafts; Dr. Sanjay Anand,

from the laboratory of Dr. Edward Maytin for providing adenocarcinoma samples

and helping me with mice experiments; Vivek from the laboratory of Dr. George

Muschler for providing tissue scaffolds, and personnel from the laboratory of Dr.

Ronald Midura for sharing osteoporotic bone samples. I would like to thank CHTN

for shipping carotid arteries. I must thank Dr. Cheri Deng and her student Yi-Sing

Hsiao, from University of Michigan, for allowing access to their laboratory and take

measurements with their hydrophone.

I acknowledge Dr. Judith Drazba and her joyful team, Dr. John Peterson and

Diane Mahovic for their efforts on sectioning and staining of difficult samples.

It is an honor to thank Dr. Joanne Belovich, the program director of Applied

Biomedical Engineering at CSU, for her support and timely advice during difficult

times. It is an honor to thank Drs. Linda Graham and Marcia Jarrett for their timely

advice.

Special thanks to all the secretaries for assisting me in many different ways.

Ms. Rebecca Laird, who, even during her vacation days reminds us of our deadlines,

secretly cares like a mother although she finds amusing to say I am not your

mother. I cannot thank her enough for her time and efforts for providing more than

administrative support throughout the years. Many thanks to Ms. Darlene

Montgomery, who keeps her cool even when the masses annoy her greatly, for her

support in many remarkable ways. Thanks to Jill Rusticelli and Sandi Zelewensky for

handling my many requests for appointments with Drs. Nicholls and Davros.

I would like to thank my friends and seniors Drs. Powrnima Joshi, Srividya

Sunderaraman, Eun Jung Kim and Nicholas Ferrel for helping me get through the

difficult times, and for all the emotional support, comradeship, entertainment, and

caring they provided. Dr. Joshi was very instrumental in having me complete my

thesis writing along with reminding me that sanity and happiness are worth more,

when I lost my composure during chaotic discontinuities in the laboratory. I would

also like to thank Marianne for her kindness and giving me company when

experiments ran late into dark. Thanks to Dr. Judd Gardner for encouraging me to

stay focused on my goals of completing the thesis during the last few months. I

would also like to thank experienced wise individuals at Cleveland Clinic, who wish

to remain anonymous, for offering guidance at variable times.

I would like to acknowledge the funding sources for financial support of my

studies: the American Heart Association, for the pre-doctoral fellowship and the

Doctoral Dissertation Research Expense Award from CSU for funding all my

materials, without which this thesis would not have been possible.

I am indebted to the Physics and Chemical & Biomedical Eng. departments at

CSU for granting me admission to the respective programs; I enjoyed the memorable

lectures and every class kept me captivated by the wealth of knowledge of the

professors. I also thank the CSU library and OhioLink, without which I would not

have access to tremendous source of information and textbooks.

I would not have been able to spend time in the laboratory without the help

of sittercity.com. I would like to thank Dr. Sandra Halliburton for recommending the

website. I extend my deepest thanks to all of the nannies, from the special ones who

assumed the role of a grandmother, to the ones who burnt down the kitchen.

Special thanks to my adorable children who went through vulnerable periods

during my doctoral studies. I offer my apologies and infinite thanks to them for

weathering difficult times and being resilient during the years. I also thank my

husband, parents, sister, brother-in-law and extended family for their support.

ix

DETECTING VULNERABLE PLAQUES WITH

MULTIRESOLUTION ANALYSIS

SUSHMA SRINIVAS

ABSTRACT

This thesis seeks to address the unmet need of identifying vulnerable

plaques, which result in 75% of the acute coronary episodes. With the limited

resolution of conventional IVUS transducers, the thin cap of the fibroatheromas

cannot be identified before they rupture. This dissertation evaluated the application

of harmonic imaging in characterizing lipid cores based on nonlinear propagation.

The hypothesis is that the multiresolution analysis of IVUS radiofrequency signals

with a focused broadband polymer transducer will result in additional diagnostic

information. The rationale is that tissue nonlinearity has a structural dependency

and the detection of this property can better resolve and differentiate plaque

components.

As part of this study, the system linearity, essential for harmonic imaging,

was established for a polymer micro-machined ultrasound transducer (PMUT)

imaging device. Pressure profiles of PMUTs were measured with a wideband

hydrophone. Nonlinear parameters of various fluids and fat from biological

x

specimen were estimated. New methods using wavelets were developed to

accurately measure the thin caps of fibroatheromas, to identify lipids and to

estimate stent apposition. An algorithm based on velocity inhomogeneity was

developed to differentiate lipids from necrotic regions. A real-time synchronized

pullback system was developed.

Measurements from multiresolution analysis of thin caps in excised human

coronary and carotid arteries (n = 5) ranged from 26 8 m to 73 28m. The

harmonic signals were better able to identify thin caps and micro-calcifications than

in fundamental mode. Lipid accumulations, as thin as 200 m to 1.5 mm thick were

identified signifying the early detection of plaque formation with wavelet analysis of

fundamental signals. However, the harmonic signals from lipid regions in fresh

tissue were significantly weaker than harmonics from fixed tissue. The specificity

and sensitivity of the new methods developed in this study need to be evaluated

with more ex vivo coronary arteries. The successful adaptation of these methods in

clinical imaging may enhance diagnostic capabilities and reduce the incidence of

acute coronary syndrome.

xi

TABLE OF CONTENTS

Page

NOMENCLATURE ..........................................................................................................XX

LIST OF TABLES .......................................................................................................XXIII

LIST OF FIGURES...................................................................................................... XXIV

I INTRODUCTION............................................................................................................. 1

1.1 Disease ....................................................................................................... 6

1.1.1 Morphology of coronary arteries ................................................. 7

1.1.2 Pathophysiology of atherosclerotic plaque................................. 7

1.1.3 Remodeling .................................................................................. 10

1.1.4 Vulnerable Plaque ....................................................................... 12

1.1.5 Mechanisms of Plaque Rupture.................................................. 13

1.1.6 Restenosis .................................................................................... 16

1.1.7 Risk factors .................................................................................. 16

1.1.8 Therapies ..................................................................................... 17

1.1.9 Reversal of CAD ........................................................................... 17

1.2 Diagnosis................................................................................................... 18

1.2.1 Biomarkers of vulnerable plaque............................................... 19

1.2.2 Non-invasive imaging ................................................................. 20

xii

1.2.2.1 Magnetic Resonance Imaging .................................................. 20

1.2.2.2 Computed Tomography Imaging ............................................ 21

1.2.2.3 Nuclear Imaging ....................................................................... 22

1.2.2.4 Hybrid Imaging PET/MR, PET/CT, SPECT/CT.................... 23

1.2.3 Invasive imaging.......................................................................... 24

1.2.3.1 Angiography.............................................................................. 24

1.2.3.2 Angioscopy................................................................................ 25

1.2.3.3 Elastography ............................................................................. 25

1.2.3.4 Thermography .......................................................................... 26

1.2.3.5 Near infrared spectroscopy ..................................................... 27

1.2.3.6 OCT ............................................................................................ 28

1.2.3.7 IVUS ........................................................................................... 29

1.3 Overview of limitations of Imaging Modalities ..................................... 32

II PROBLEM FORMULATION ....................................................................................... 34

2.1 Specific Aims ............................................................................................ 37

2.2 Significance of this study ........................................................................ 39

III MATERIALS AND METHODS .................................................................................. 40

3.1 Materials .................................................................................................. 40

3.1.1 PVDF-TrFE .................................................................................. 40

xiii

3.1.2 Reflectors ..................................................................................... 42

3.1.3 Amplifiers..................................................................................... 42

3.1.4 SMA cables ................................................................................... 43

3.1.4 Tissue specimen .......................................................................... 44

3.2 Making of the Device ............................................................................... 45

3.2.1 Fabrication of Transducer .......................................................... 45

3.2.2 Preamplifier Circuit ..................................................................... 46

3.2.3 External Amplifier ....................................................................... 48

3.2.3 Testing of Transducers ............................................................... 49

3.3 Data Acquisition ...................................................................................... 50

3.3.1 Synchronized pull back ............................................................... 50

3.3.2 Data acquisition system .............................................................. 51

3.3.3 Acquisition of IVUS RF harmonic signals ................................. 51

3.3.4 Processing of harmonic signals .................................................. 54

3.3.5 Multi resolution analysis of harmonic signals .......................... 54

3.3.6 Histological Correlation .............................................................. 55

3.3.7 Estimation of nonlinear parameters.......................................... 56

3.3.8 Enhancement of spectral parameters........................................ 58

3.3.9 Estimation of extent of neointimal hyperplasia........................ 58

xiv

3.4 Imaging of various biological specimen ................................................ 59

3.4.1 Imaging of Carotid arteries......................................................... 59

3.4.2 Imaging of Peripheral arteries ................................................... 60

3.4.3 Imaging of adenocarcinoma ....................................................... 60

3.4.4 Imaging of MDCK cells ................................................................ 61

3.4.5 Imaging of scaffolds for tissue engineering............................... 61

IV HARMONIC IMAGING ............................................................................................... 62

4.1 Development of Harmonics .................................................................... 62

4.2 Advantages of Harmonics ....................................................................... 65

4.3 Methods of Harmonic Imaging ............................................................... 66

4.3.1 Filters Approach .......................................................................... 66

4.3.2 Pulse Inversion Imaging ............................................................. 67

4.4 Harmonic Signal Processing ................................................................... 71

V MULTIRESOLUTION ANALYSIS ............................................................................... 72

5.1 Methods of analyzing a signal ................................................................ 72

5.1.1 Fourier frequency analysis ......................................................... 73

5.1.2 Windowed Fourier Transform ................................................... 74

5.1.3 Wavelet Transform ..................................................................... 75

5.2 The uncertainty principle ....................................................................... 76

xv

5.3 Multiresolution Analysis......................................................................... 76

5.4 Application in characterization of plaque ............................................. 78

VI RESULTS I ............................................................................................................... 80

6.1 PMUT characterization ........................................................................... 80

6.2 Device components characterization .................................................... 82

6.2.1 Quarter Matching ..................................................................... 82

6.2.2 Minimum Gain Required on the Preamplifier........................... 83

6.2.3 Operating range of Miteq Amplifier ........................................... 84

6.3 System linearity Aim 1(a) .................................................................... 85

6.3.1 Harmonic contribution from D/A card...................................... 86

6.3.2 Harmonic contribution from the preamplifier ......................... 87

6.3.3 Harmonic contribution from other amplifiers.......................... 87

6.3.4 Harmonic transduction from PVDF-TrFE film .......................... 88

6.3.5 Optimal BW for transmit waveforms ........................................ 90

VII RESULTS II ............................................................................................................ 92

7.1 Axial radiation profiles........................................................................... 93

7.2 Lateral radiation profiles ........................................................................ 95

7.3 2D radiation profiles ............................................................................... 97

7.4 Variability of Axial Resolution................................................................ 99

xvi

VIII RESULTS III ....................................................................................................... 100

8.1 Fluid nonlinearity Aim 1(a) ............................................................... 100

8.1.1 Distinct attenuation curves for harmonics.............................. 100

8.1.2 Harmonic generation in fatty fluids ......................................... 102

8.1.3 Egg Yolk and Egg White ............................................................ 103

8.2 Tissue nonlinearity.................................................................................... 104

8.2.1 Harmonic generation in diseased aorta .................................. 104

8.2.2 Lipid nonlinearity ...................................................................... 104

8.2.3 Nonlinearity of blood ................................................................ 105

IX RESULTS IV ........................................................................................................... 107

9.1 Analysis with wavelets.......................................................................... 107

9.1.2 Uncovering nonlinearity ........................................................... 107

9.1.3 Seeing with wavelets................................................................. 109

9.1.4 Precise measurements with MRA ............................................ 110

9.1.5 Pathological differences with harmonics ................................ 111

X RESULTS V .............................................................................................................. 112

10.1 Aim 1(b) ................................................................................................. 112

10.1.2 Fundamental and harmonic images of coronary artery ...... 112

10.1.3 Fundamental and harmonic images from a porcine model. 114

xvii

10.1.4 Harmonic signal strength from healthy tissue ..................... 114

10.1.4 Utility of low signal strength harmonics ............................... 116

10.1.5 MRA identification of thin cap................................................ 118

10.1.6 MRA identification of lipids .................................................... 118

10.1.7 MRA identification of borders ................................................ 119

10.1.8 Characterization by velocity differences............................... 121

XI RESULTS VI ........................................................................................................... 122

11.1 Aim 1(c).................................................................................................. 122

11.1.1 Extension of spectral parameters .......................................... 122

11.1.2 Estimation of nonlinear parameters ..................................... 123

XII RESULTS VII ........................................................................................................ 125

12.1 Aim 2(a-c) .............................................................................................. 125

12.1.1 Bare-metal stent in a silicone tubing .................................. 126

12.1.2 Imaging of aortic grafts ........................................................ 126

12.1.3 Importance of focal region................................................... 129

12.1.4 Harmonic imaging of stents ................................................. 130

12.1.5 MRA of harmonics and fundamental ..................................... 131

12.1.6 Identification of necrotic regions ........................................... 131

12.1.7 Stent apposition....................................................................... 133

xviii

XIII RESULTS VIII ..................................................................................................... 135

13.1 Carotid arteries Aim 3(a) ................................................................... 135

XIV RESULTS IX ........................................................................................................ 138

14.1 Cell clusters Aim 3(b)......................................................................... 138

14.1.1 Ultrasound bio-microscopy.................................................... 139

14.1.2 Aim ........................................................................................... 140

14.1.3 Processing of echoes from cell clusters................................. 140

14.1.4 Cell Culture .............................................................................. 142

14.1.4 Detection of inflection points ................................................. 143

14.1.5 Wavelet coefficient reconstruction........................................ 144

14.1.6 3D reconstruction of cell clusters .......................................... 145

XV RESULTS X ........................................................................................................... 147

15.1 Scaffolds for tissue engineering Aim 3(b) continued ...................... 147

15.1.1 Scaffolds ................................................................................... 148

15.1.2 2-dimensional scaffold............................................................ 148

15.1.3 3-dimensional scaffold............................................................ 149

XVI DISCUSSION .......................................................................................................... 151

XVII CONCLUSION....................................................................................................... 163

REFERENCES................................................................................................................. 165

xix

APPENDICES ................................................................................................................. 189

APPENDIX A ....................................................................................................... 189

APPENDIX A1......................................................................................... 190

APPENDIX A2......................................................................................... 191

APPENDIX A3......................................................................................... 194

APPENDIX A4......................................................................................... 195

APPENDIX A5......................................................................................... 196

APPENDIX A6......................................................................................... 197

APPENDIX B ....................................................................................................... 198

APPENDIX B1......................................................................................... 199

APPENDIX B2......................................................................................... 200

APPENDIX B3......................................................................................... 201

APPENDIX B4......................................................................................... 202

APPENDIX B5......................................................................................... 203

xx

NOMENCLATURE

ACS: Acute coronary syndrome

AHA: American Heart Association

ATCC: American Type Culture Collection

AMI: Acute myocardial infarction

CAD: Coronary artery disease

CHD: Coronary heart disease

CRP: C-reactive protein

CT: Computed tomography

CWT: Continuous wavelet transform

Db2, db4: Daubechies wavelets

DI: Deionized

F20: Fundamental 20 MHz

F40: Fundamental 40 MHz

18F: Flourine 18

18F-FDG: Flourine 18 Fludeoxyglucose

FT: Fourier Transform

EBCT: Electron beam CT

xxi

EC: Endothelial Cell

FHS: Framingham Heart Study

FIR: Finite impulse response

H40: Harmonic 40 MHz

H80: Harmonic 80 MHz

HPF: High pass filter

hs-CRP: High sensitivity C-reactive protein

HU: Hounsfield units

IL2: Interleukin 2

IVUS: Intravascular ultrasound

LAD: Left anterior descending

LDL: Low density lipoprotein

LPF: Low pass filter

MDCK:Madin Darby Canine Kidney cells

MDCT:Multi detector CT

MI: Myocardial infarction

MMP: Matrix metalloproteinase

MRA: MR Angiography / Multiresolution analysis

MRI: Magnetic resonance imaging

xxii

OCT: Optical coherence tomography

PE: pulse echo

PET: Positron emission tomography

PI: Pulse inversion

PMUT:Polymer micromachined ultrasound transducer

PSD: Power spectral density

PVDF-TrFE: Polyvinylidene fluoride trifluoroethylene

PZT: Lead Zirconate Titanate

SCD: Sudden cardiac death

SES: Sirolumis eluting stent

SMC: Smooth muscle cell

SNR: Signal to noise ratio

SPECT: Single photon emission computed tomography

99mTc: Metastable Technicium

TCFA: Thin cap fibroatheromas

THI: Tissue harmonic imaging

TIMP: Tissue inhibitor of metalloproteinase

UBM: Ultrasound biomicroscopy

WFT: Windowed Fourier Transform

xxiii

LIST OF TABLES

Table Page

Table 1: Classification by Committee on Vascular Lesions of the Council on

Atherosclerosis of AHA 11

Table 2: Seven Category Classification by Virmani et. al.,.12

Table 3: Imaging capabilities of various modalities w.r.t. vulnerable plaque 33

Table 4: Range of Transducer Characteristic Parameters 81

Table 5: BW for different lengths of cable 83

xxiv

LIST OF FIGURES

Figure Page

Figure 1: Plaque rupture leading to death of heart muscle ........................................... 2

Figure 2: Illustration of normal and diseased human coronary artery ........................ 8

Figure 3: Classification of atherosclerosis by Virmani et. al., ...................................... 11

Figure 4: Different morphologies of vulnerable plaques ............................................. 13

Figure 5: Mechanism of plaque rupture ........................................................................ 14

Figure 6: Illustration of IVUS catheter ........................................................................... 30

Figure 7: Various diagnostic methods for the detection of vulnerable plaque .......... 33

Figure 8: 40 MHz PMUT transducer .............................................................................. 46

Figure 9: Preamplifier circuit for a PMUT ..................................................................... 47

Figure 10 : Experimental setup for tissue imaging....................................................... 51

Figure 11: Excitation pulses for harmonic imaging...................................................... 52

Figure 12: Development of harmonics .......................................................................... 64

Figure 13: Pulse inversion technique ............................................................................ 69

Figure 14: Decomposition with MRA............................................................................. 78

Figure 15: PE and PSD of a high resolution transducer ............................................... 81

Figure 16: Demonstration of broad bandwidth of the PMT transducer..................... 82

xxv

Figure 17: Operating Range of Miteq Amplifier............................................................ 85

Figure 18: Harmonic contribution from the source ..................................................... 86

Figure 19: Harmonic contribution from the preamplifier ........................................... 88

Figure 20: Frequency transduction of PVDF-TrFE and optimal BW........................... 89

Figure 21: Axial radiation patterns of fundamental and harmonics at 50 V .............. 93

Figure 22: Axial radiation patterns of fundamental and harmonics at 100 V............ 94

Figure 23: Lateral radiation profiles.............................................................................. 96

Figure 24: 2D radiation profiles for 20 MHz ................................................................. 97

Figure 25: 2D radiation profiles for 40 MHz ................................................................. 98

Figure 26: Variability of axial resolution....................................................................... 99

Figure 27: Distinct attenuation curves for fundamental and harmonics ................. 101

Figure 28: Harmonics development in fatty fluids ..................................................... 103

Figure 29: Harmonic generation in diseased aorta .................................................... 105

Figure 30: Nonlinearity parameter values of egg and mice fat ................................. 106

Figure 31: Egg dual bilayer membranes imaged with harmonics............................. 108

Figure 32: Better Resolution and contrast with MRA ................................................ 109

Figure 33: Precise measurement of egg membranes with MRA ............................... 110

Figure 34: Pathological sections on different scales .................................................. 111

xxvi

Figure 35: Fundamental and harmonic images of a fresh coronary arterial section

......................................................................................................................................... 113

Figure 36: Fundamental and harmonic images from a control void of lipids .......... 115

Figure 37: Harmonic signal strength from healthy tissue ......................................... 116

Figure 38: Significance of harmonics in imaging thin cap ......................................... 117

Figure 39: MRA of thin cap of fibroatheromas............................................................ 119

Figure 40: Lipid identification by MRA ....................................................................... 120

Figure 41: Characterization by measuring the change in velocity ............................ 121

Figure 42: Extension of spectral parameters from nonlinear imaging..................... 123

Figure 43: Image generation based on differences between fundamental and

harmonics ...................................................................................................................... 124

Figure 44: Self-expanding stent imaged with IVUS, OCT and PMUT......................... 127

Figure 45: Harmonic characterization of neointimal growth over a graft ............... 128

Figure 46: Degradation of lateral resolution .............................................................. 129

Figure 47: Minimal harmonics from restenosis.......................................................... 130

Figure 48: MRA of fundamental and harmonics ......................................................... 132

Figure 49: Differentiating low echogenic regions ...................................................... 133

Figure 50: MRA evaluation of stent apposition .......................................................... 134

Figure 51: Harmonic detection of thin cap of a carotid plaque ................................. 136

xxvii

Figure 52: Thin cap, lipid region and intimal thickening in carotid arteries ........... 137

Figure 53: Setup for imaging cell clusters ................................................................... 143

Figure 54: Detection of inflection points ..................................................................... 144

Figure 55: Wavelet coefficient reconstruction ........................................................... 145

Figure 56: Reconstructed images of cells on membrane ........................................... 146

Figure 57: 3D reconstruction of cell clusters .............................................................. 146

Figure 58: Wavelet reconstruction of a 2D scaffold image ........................................ 149

Figure 59: Wavelet reconstruction of a 3D scaffold image ........................................ 150

Figure 60: PMUT& OCT image comparison of a stented artery ................................ 199

Figure 61: PMUT, OCT, Revo, HE of healthy artery .................................................... 200

Figure 62: PMUT, OCT, Revo & HE of artery with intimal thickening....................... 201

Figure 63: PMUT, OCT, Revo & HE of artery with thin cap ........................................ 202

Figure 64: 0.8 mm PMUT images of stent apposition ................................................ 203

Figure 65: 0.6 mm PMUT images of stent apposition ................................................ 204

1

CHAPTER I

INTRODUCTION

June 13th 2008 Tim Russert died at the age of 58 after collapsing at work.

The untimely death of the NBC host had many of us have the alarming thought of

could it happen to me? Mr. Russerts autopsy confirmed the rupture of a

cholesterol plaque in a branch of the LAD, causing sudden cardiac death. Sudden

death is ancient to humans and the earliest record of sudden death possibly due to

atherosclerotic coronary occlusion is suggested in an Egyptian relief sculpture from

the tomb of a noble of the Sixth Dynasty ( 2625- 2475 B.C.) [1]. Although FHS data

from 1950 to 1999 suggests 49% decline in sudden deaths, SCD claims 300,000 lives

in the US every year [2]. Unfortunately, the difficulty with diagnosing the risk for

SCD is that, in many people, SCD is the first and last manifestation. 50% of men and

64% of women who die of sudden CHD have no symptoms prior to the acute event

[2].

Mr. Russert had passed the exercise stress test just 2 months prior to his

death but autopsy showed significant blockages in several arteries [3]. The severity

and the anatomical status of CAD remain undetected without an appropriate

2

diagnostic test. Plaque rupture can be silent and the lack of symptoms would not

suggest an invasive test needed to make a definitive diagnosis. An illustration of the

blockage in the artery due to plaque rupture is shown in Figure 1.

Figure 1: Plaque rupture leading to death of heart muscle

There are several non-invasive and invasive diagnostics tests for the

estimation of extent of CAD. Several noninvasive methods have been demonstrated

to be of clinical value, but serious difficulties due to the small size of the coronary

arteries, cardiac and respiratory motion, flow disturbances, challenging anatomy

3

and mainly the limited spatial resolution need to be overcome. If noninvasive

diagnostic modalities were to be routine examinations and tomographic view of the

arterial system could be obtained, noninvasive methods still lack the resolution

needed to diagnose early stage disease as well as the culprit lesions smaller than the

imaging device limit. Due to the limited resolution, noninvasive modalities tend to

focus on managing the disease by the estimation of stenosis that is

hemodynamically significant. In 85% of the ACS, the culprit lesions were less than

70% stenotic prior to rupture. This might explain why managing hemodynamically

significant stenoses have not proven effective in predicting SCD [4, 5].

Among the invasive diagnostic tests, X-ray angiography has been considered

the gold standard for defining the degree of stenosis. Other main clinically available

modalities are OCT and IVUS. Several studies have dispelled the skepticism towards

the accuracy and reliability of both IVUS and OCT. The use of OCT as an

intracoronary imaging modality has been growing and has shown significance in

successful outcomes [6, 7]. IVUS offers tomographic visualization of the arteries but

with limited resolution compared to OCT, with the current clinical IVUS catheters.

The advances in IVUS have resulted in automated plaque characterization and 3D

visualization but the efficacy of these methods in identifying a vulnerable plaque is

yet to be proven. These invasive methods are not called for unless the patient

presents with symptoms and is first diagnosed by a noninvasive modality. This is

mainly due to the lack of detection capability of the current invasive techniques in

identifying the early stage disease and also the cost of an additional procedure. The

4

goal is to identify late stage disease to prevent acute events and also the early

diagnosis of the disease with accuracy and reliability.

This dissertation describes my attempts at imaging the human coronary

arteries in an effort to detect mainly the lipid pools and thin caps of vulnerable

plaques, not possible at this time. Multiresolution analysis with wavelets is the

approach employed for my hypothesis.

Section 2 of this chapter describes the atherosclerotic disease manifestations,

causes, prevention and treatment. Section 3 describes the current methods of

diagnosing atherosclerotic plaques. Both non-invasive and invasive methods, their

merits and limitations are discussed.

Chapter 2 formulates the medical problem, states the hypothesis and lists the

specific aims of this thesis which test the hypothesis, that multiresolution analysis of

IVUS signals lead to better classification of plaques.

Chapter 3 describes the materials and the methods that are common to most

of the experiments conducted during my research. Transducer materials and

various components used are explained. The synchronized data acquisition system

is described. Experimental protocols of imaging tissue specimen and signal analysis

are also detailed.

Chapter 4 connects harmonic imaging to the hypothesis and describes

development of harmonics by nonlinear propagation in biological tissue.

5

Chapter 5 describes various methods of signal analysis, the Heisenberg

uncertainty principle and application of multiresolution analysis for the

characterization of plaques.

Chapter 6 presents the transducer characteristics that are fundamental to

acquiring signals of good quality. The transducer and the various electronic

components are tested for linearity and any nonlinear modes of operation are

discussed.

Chapter 7 presents the acoustic pressures radiated by the PMUT as measured

by a hydrophone.

Chapter 8 presents results from experiments demonstrating nonlinearity of

fluids and tissue specimen.

Chapter 9 shows how multiresolution can be applied for plaque

characterization and identification of nonlinear components.

Chapters 10 through 12 present the results of specific aims using coronary

arteries.

Chapters 13 through 15 present results of imaging various other biological

specimens like the carotid arteries, cell clusters and tissue scaffolds.

In the Discussion, Chapter 16, the results are examined; the conclusions and

future research are provided in Chapter 17.

6

1.1 Disease

Hurry, Worry & Curry Recipe for Heart Disease.

-Teachings of Sathya Sai Baba on health by Srikanth Sola, M.D

Atherosclerosis, the primary cause of heart attack, stroke and other

conditions of the extremities remains a major contributor to morbidity and

mortality. Atherosclerosis originates from Greek words atheros meaning gruel, a

soft pasty material corresponding to the necrotic core in the arterial wall and

sclerosis meaning hardening or indurations matching the thin cap of the plaque.

With increasing age, arterial walls thicken leading to focal atherosclerotic lesions

that eventually advance to complex plaques that could block the lumen limiting

blood flow or rupture generating a thrombus leading to total occlusion. Several risk

factors like high cholesterol diet, smoking, metabolic-syndrome, diabetes, obesity,

psychological stress along with predisposition to genetic background induce

atherosclerosis [4, 5]. Atherosclerosis is a progressive systemic disease. However,

the plaque pathology differs depending on the vascular bed [8]. Although sections

from other sites like renal, peripheral and carotids were also imaged in this study

due to lack of availability of coronary arteries, the plaque characteristics described

in this section refer to the coronary plaques as the number of studies reporting the

differences in vascular beds are very few.

7

1.1.1 Morphology of coronary arteries

Coronary arteries are muscular and comprise three layers: intima, media and

the adventitia. The internal and external laminae separate the intima-media and the

media-adventitia layers respectively. Intima can vary in thickness. The thinnest

segments of the intima comprise the endothelium, basement membrane and

subendothelial layer, which consist of elastin, collage, proteoglycans, and scattered

smooth muscle cells. Thicker segments express a layer of longitudinally aligned

SMCs that originate in the medial layer and internal elastic lamina. Adventitial layer

is comprised of elastic fibers, collagen and fibroblasts. Vasa vasorum, the

microvasculature that nourish the arteries and nerve fibers are found in the

adventitia. Healthy arteries do not exhibit advanced lesions in the arterial wall.

Atherosclerotic lesions occur more frequently in certain sites on the

coronary tree. The left coronary artery has a higher incidence where the trunk

bifurcates, proximal to the LAD and circumflex. Lesions are seen more in the

proximal and middle segments [9].

1.1.2 Pathophysiology of atherosclerotic plaque

Pathological states can be reached by different mechanisms. Based on new

insights, due to progress in cell and molecular approaches, these mechanisms can be

summarized in to three main hypotheses response to injury, oxidized LDL and

inflammation [10-12]. Response to injury due to mechanical stress from variation

8

in the flow, wall tension and maturity often manifest as the variation in the intimal

thickness. This is more pronounced at the bifurcations or side branches, which are

predisposed to atherosclerotic lesions [9]. Oxidized LDL hypothesizes that LDL in

the blood oxidized by macrophages and SMCs that form cholesterol clefts within the

arterial wall contribute to atherosclerosis [11]. Inflammation hypothesis postulates

that immune cells interact with various metabolic risk factors to progress the

disease from initiation to terminal thrombogenic state [12]. These mechanisms

result in activation and alteration of the intima, media and adventitial layers leading

to the formation of atherosclerotic plaques that further progress to advanced

lesions. Figure 2 illustrates normal and diseased human arteries.

Figure 2: Illustration of normal and diseased human coronary artery

9

In the diseased state, intima thickening may be eccentric, diffuse or

circumferential. An eccentric bell shaped thickening is commonly seen [13]. Intima

to media thickness varies from normal ratio of 0.1-1 to 4.1 in the age-related disease

[14]. Activated ECs in the intima lead to degradation of the ECM, proliferate and

migrate to initiate angiogenesis, a process which has been shown to partake in many

pathological conditions. Proliferation and migration of ECs through ECM is

facilitated by the integrin 3 and integrin 3 stimulated MMP-2 degradation of

ECM [15]. ECs maintain the vascular tone and hence blood pressure by, the

controlled release of vasodilators like, NO, prostacyclin, and PGI2, and

vasoconstrictors like endothelins and PAFs. In a normal state, NO inhibits platelet

adhesion, leucocyte adhesion and injury induced neointimal proliferation. Shear

stress alters the production of NO and thus affects various regulatory mechanisms

[16]. An activated endothelium due to inflammation expresses adhesion molecules

resulting in binding and extravasation of leucocytes [17].

ECs in an inactivated state prevent the proliferation of SMCs and when

activated, have mitogenic effect on SMCs by the secretion of PDGF along with other

growth factors [18]. The media in a healthy artery is about 100 m [14]. The

function of SMCs is to contract and serve as an elastic reservoir from the pulse of the

blood flow. The main pathologies of SMCs are vasoconstriction and hypertension. In

response to vascular injury, SMCs proliferate into the intima and stabilize a

developing plaque by forming a fibrous cap [16].

The onset of plaque formation occurs in early childhood leading to fatty

streaks or xanthomas [19]. Fatty streaks are fat-laden macrophages in the intima.

10

One or many mechanisms of disturbance of the endothelium result in the immune

cell adhesion to ECs and migration through ECs to capture LDL to form foam cells. In

case of pathological intimal thickening, extracellular lipids accumulate and appear

slightly raised and yellowish in color to naked eye. SMCs may also contain lipids.

Secretion of MMPs result in degradation of the ECM and apoptosis of macrophages

and denudation of the ECs resulting in a lipid core separated from the lumen by a

fibrous cap/capsule. Lipid core is made up of necrotic remains, cholesteryl esters,

lipoproteins and phospholipids. The size of the lipid core depends on the number of

macrophages in the lesion [20]. The lipid core and the thickness of the fibrous cap

are inversely related [21]. Thin capsules have less collagen, abundant macrophages

and other inflammatory cells and loss of SMCs due to MMPs [22]. Such fragile spots

are found in the regions where the plaque meets the unaffected part of the artery.

Such a region is termed shoulder of the plaque, Plaques with a large lipid core with

a thin cap infiltrated by macrophages are termed thin cap fibroatheroma (TCFA).

Different classifications of atherosclerotic lesions based on lipid content and the

fibrous cap have been proposed and are as shown in Figure 3 and Table 1 and Table

2 [19, 23].

1.1.3 Remodeling

The process of increasing the lumen size in order to accommodate the blood

flow and wall tension is called remodeling [24]. The vessel wall reorganizes its

cellular and extracellular components in early stage disease, prior to significant

11

luminal stenosis [25]. Remodeling is bidirectional. Plaques responsible for ACS often

show outward remodeling preserving the lumen size [26]. Plaques causing stable

angina usually present inward growth resulting in lumen constriction.

Figure 3: Classification of atherosclerosis by Virmani et. al.,

Table 1: Classification by Committee on Vascular Lesions of the Council on

Atherosclerosis of AHA

Type I Fat-laden macrophages

Type II Fatty streak. Lipids remain intracellular

Type III Pre-atheromatous lesion. Extracellular lipids

Type IV Fibrolipid. Soft plaque defined capsule and lipid core

Type V Hard plaque collagen and SMCs

Type VI Complicated lesion

12

Table 2: Seven Category Classification by Virmani et. al.,

Non-atherosclerotic lesions Intimal thickening, intimal xanthoma

Progressive atherosclerotic lesions

Pathological intimal thickening, fibrous

capsule, thin cap fibrous atheroma (TCFA),

calcified nodule, fibrocalcific plaque

1.1.4 Vulnerable Plaque

Some of the other terms for vulnerable plaque are high risk plaque,

thrombosis-prone plaque, unstable plaque and TCFA. The following types are

considered vulnerable: TCFA, sites of erosion, some plaques with calcified nodules.

Although the plaques with large lipid cores and thin caps (inflamed TCFA) are

strongly suspected to be vulnerable, there appear to be plaques without these

features to be thrombogenic that also lead to ACS [27]. In a study involving SCDs,

thrombosis was seen at eroded sites, sites other than thin cap and lipid pool which

are considered vulnerable [28]. Such plaques at sites with erosion expressed

increased proteoglycans. Another study identified a calcified nodule to be

potentially vulnerable [29, 30]. Different morphologies of plaques that are

considered vulnerable are shown in Figure 4. It is also known that TCFAs can be

found at autopsy suggesting the low specificity of TCFA as vulnerable [30]. There is

still not a prospective definition or a prospective method of identifying vulnerable

plaques.

13

Figure 4: Different morphologies of vulnerable plaques

1.1.5 Mechanisms of Plaque Rupture

A number of intrinsic and extrinsic factors contribute to plaque vulnerability

size of lipid core, thickness and collagen content of the fibrous cap and

14

inflammation within the plaque. Factors like hemodynamic stress may cause cap

disruption. An illustration of plaque rupture is shown in Figure 5.

Figure 5: Mechanism of plaque rupture

15

Endothelial cells are exposed to hydrostatic forces by the blood,

circumferential stress caused by the vessel wall and the shear stress caused by

blood flow. According to Laplaces law, the wall tension developed is directly

proportional to the pressure on the wall and the luminal diameter. This

phenomenon may lead to unbearable stress on the thin cap and at the shoulder of

the plaque [31]. In case of fibrous caps, a moderately stenosed plaque may be at

higher risk for rupture than a severely stenosed plaque due to higher wall tension in

the former type [32-34].

Lipid core size and consistency are also factors that contribute to plaque

rupture. It has been shown that a large proportion of disrupted plaques were

occupied by lipid rich core than intact plaques causing < 70% stenosis [35].

Most vulnerable area of the plaque is the shoulder region where the cap is

the thinnest [36]. Reduced collagen content in the cap also increases the risk of

rupture. Also a reduction in the SMCs in the fibrous cap would destabilize the plaque

[37].

Neovascularizations are seen in plaques and may be involved in plaque

disruption. The postulation is that the newly formed vessels are fragile and thus

promote intra-plaque hemorrhage increasing the lipid volume further leading to

unbearable stress on the thin cap [38].

16

1.1.6 Restenosis

Restenosis is the re-narrowing of the arterial lumen after an intervention to

such as endarterectomy, bypass grafting and intraluminal approaches (angioplasty,

atherectomy, stent angioplasty) to enlarge the stenosed lumen. Greater than 20% of

interventions fail due to restenosis. Failures occur 12 months, failure occurs due to underlying atherosclerosis [39].

Restenosis can result due to elastic recoil of the artery within minutes of angioplasty

intimal hyperplasia in case of stenting, reorganization of thrombus, and remodeling.

Remodeling seemed to show greater loss of luminal area than intimal hyperplasia

[40]. In case of restenosis, a neointimal response to injury (by stenting, surgery or

angioplasty) is seen where the VSMCs proliferate creating a thickened intima. The

rates of restenosis at 20% 40% is similar in all vessels. In 30% of the cases,

restenosis leads to significant luminal stenosis [41]. Efforts to limit restenosis may

involve targeted drug delivery, genetic therapies and improving the resistance of

vascular beds.

1.1.7 Risk factors

Some of the risk factors for CHD are family history, smoking, hypertension,

dyslipidemia (elevated LDL, low levels of HDL, elevated triglycerides), metabolic

syndrome, diabetes, obesity, reduced fitness, and psychological risk factors

(depression, hostility, anxiety, stress) [3].

17

1.1.8 Therapies

Attempts to stabilize vulnerable plaques have been made by targeting

different pathways leading to plaque rupture. Some of them are endothelium

passivation by increasing the antioxidant NO by physical exertion, by reducing LDL

deposition by statins, MMP inhibition by TIMPS or doxycycline, and by increasing

collagen deposition [42, 43]. High levels of HDL show marked positive influence on

endothelial function and also the reversal of lipid accumulation in the arterial wall

[44].

1.1.9 Reversal of CAD

Making healthy dietary and lifestyle changes can delay and, even reverse

heart disease after one year. These lifestyle changes include whole foods, plant-

based diet, smoking cessation, routine physical activity and stress management.

This was scientifically demonstrated by the Lifestyle Heart Trial and prior studies

[45, 46] . Regression of the disease was seen to be more in 5 years than 1 year in the

experimental group, whereas, the disease progressed and more cardiac events

occurred in the control group.

The next section gives a review of latest diagnostic methods of identifying a

vulnerable plaque.

18

1.2 Diagnosis

A new scientific truth does not triumph by convincing its opponents and making

them see the light, but rather because its opponents eventually die, and a new

generation grows up that is familiar with it.

Max Planck

During the evolution of CAD to MI, atherosclerotic plaques undergo

progression and cause ischemic events either by direct luminal stenosis or by an

occlusive thrombus. Estimates show that 13 million individuals suffer from

coronary artery disease (CAD), 75% of acute coronary episodes are due to plaque

rupture and 87% of all strokes are ischemic [47]. Detection of atherosclerosis at an

early stage may recognize vulnerable patients at an early stage of CAD and help

undertake preventive measures. Several diagnostic imaging and physiology based

detection modalities have attempted to identify the vulnerable plaque. Each

modality offers unique diagnostic information which in the future may be combined

to help make integrated clinical decision in identifying a vulnerable patient. The

characteristics of vulnerable plaque are: size of lipid core (40% of entire plaque),

thickness of fibrous cap (23 19 m to 150 m), presence of inflammatory cells,

amount of remodeling and plaque-free vessel and 3D morphology [23, 48, 49].

19

1.2.1 Biomarkers of vulnerable plaque

Markers are molecules that leave the site of plaque and enter the

bloodstream for detection peripherally. There may be unique cell types expressed in

the blood due to CAD as well. Cholesterol and lipid content estimation are poor

markers of sudden events as fewer than 50% of the patients with ACS have elevated

lipid levels. Five inflammation-sensitive plasma proteins when elevated along with

hypercholesterolemia have been associated with high risk for stroke and MI,

whereas without elevation, proteins did predict high risk [50]. Studies with specific

immunoassay detection of oxLDL in the plasma show elevated oxLDL in CAD

patients [51]. Studies show that CRP is directly associated with plaque formation

[52, 53]. CRP stimulates additional inflammatory molecules and its opsonization of

LDL mediates uptake by macrophages [53, 54]. Although hs-CRP elevations

correlate with ACS, correlation with histopathology is poor [55, 56]. Soluble and

membrane bound CD40 ligand levels have been shown to be elevated in unstable

angina patients [57, 58]. MMPs are extracellular enzymes and are found in plaques and

ingest lipids. High blood levels of MMP-2 and MMP-9 were found in patients with

ACS compared with stable angina patients [58]. The successful identification of a

biomarker of vulnerable plaque could lead to non-invasive tests for ACS.

20

1.2.2 Non-invasive imaging

The desirable goal in order to manage patients with ACS is the non-invasive

identification of vulnerable plaque.

1.2.2.1 Magnetic Resonance Imaging

MR differentiates plaque components based on the biophysical and

biochemical properties. In vivo MR plaque imaging is achieved with high resolution

sequences like FSE and black blood spin echo [59, 60]. Bright blood imaging is

employed to image the fibrous cap thickness [60]. Characterization is usually based

on the signal intensities and plaque appearance on T1-weighted proton density-

weighted and T2-weighted images. Calcifications, due to their low mobile proton

density, can be identified by signal loss [61]. Fibrocellular regions provide high

signal intensities in all weightings, and lipids present with low signal on T2w and

hyperintense on T1w [62]. High resolution black blood MRI of normal and

atherosclerotic human coronary arteries showed statistically significant differences

in the wall thickness and no change in lumen area due to outward remodeling [63].

This study required breath holding and this was eliminated by employing

respiratory gating and slice position correction [64, 65]. Respiratory gating

provided a quick way to image a long segment of the coronary artery.

Dynamic contrast enhanced MRI with gadolinium as the signal enhancing

contrast has been used in preliminary studies to image inflammation through

21

identifying neovascularization of atherosclerotic plaque in human carotid arteries

[66]. The low molecular weight of the contrast agent diffuses rapidly aiding the

early detection of binding after injection. Human studies with SPIO contrast agents

that result in signal loss on T2*-weighted sequence, showed the accumulation of

iron oxide particles in the macrophages within carotid plaques [61, 67]. Further

development on T2*-effects should allow for better detection of iron oxide

accumulation within the plaque [68, 69].

1.2.2.2 Computed Tomography Imaging

Due to its high sensitivity to calcifications, CT has become the established

method for calcium scoring [70]. However, sensitivity for earlier stage disease is

lower due to lack of in-plane spatial resolution. Complex plaques in the vicinity of

high calcifications may be difficult to assess due to the same reasons [71]. MDCT and

EBCT allow faster acquisition than spiral CT [72]. EBCT showed good correlation

with non EBCT systems in assessing the volume of calcium [73, 74]. 16CDT provides

voxels with improved spatial resolution on the order of sub-millimeter. Beam

hardening artifacts of calcium are thus reduced due to reduced partial volume effect

[75].In vivo study using contrast enhance MDCT showed good correlation in

differentiating soft, intermediate and calcified plaques as compared to IVUS [76].

Intravascular thrombi appear with low attenuation of 20 -30 HU [74]. Non-calcified

plaques and blood have similar attenuation (50 70 HU). Significant enhancement

22

of the vessel over the non-calcified plaques is achieved by a contrast medium (200

HU) [76].

Contrast enhanced CTA for plaque characterization is although challenging, it

has been demonstrated that CTA can assess plaque area, density and volume with a

good correlation with IVUS examinations [77, 78]. A study examining 10, 037

coronary arterial segments from 1059 patients suspected of CAD reported the use of

contrast enhanced CTA in identifying vulnerable plaques before an acute event [79]!

The same study also had the findings of more frequent spotty calcification and

extensive remodeling in patients who had an ACS in the follow up duration of 27

months.

With improved spatial resolution from 320 and 256 DCT and better temporal

resolution from the dual source CT, better characterization and identification of

vulnerable plaques can be achieved [80-82].

1.2.2.3 Nuclear Imaging

PET and SPECT benefit from the ability to detect low concentrations of

radiotracers but lack resolution compared to other imaging modalities.

Radioisotopes are labeled with molecules that localize to certain regions and can be

imaged with non-invasive tomographic scintigraphy. PET (3-4 mm) has a superior

resolution than SPECT (10-15 mm). Capability of SPECT to image MMP activation

and degradation of the fibrous cap was demonstrated by the accumulation of the

labeled radiotracer 3 times greater in the affected plaque compared to unaffected

23

regions [83]. Higher resolution images of the same can be obtained with the new

MMP inhibitor labeled 18F for PET imaging [84]. Since macrophages and leukocytes

demonstrate increased oxidative metabolism and glucose use, 18F FDG is used to

predict plaque rupture and clinical events [85]. Although higher uptake of FDG is

seen in plaques that progress to rupture and thrombosis, FDG can also accumulate

in the ECs and lymphocytes, reducing specificity [86-89]. Tracers more specific than

FDG are being developed. Coronary artery imaging has the issues of respiratory

movement, myocardial FDG uptake and the small size of the coronary arteries.

1.2.2.4 Hybrid Imaging PET/MR, PET/CT, SPECT/CT

The high sensitivity of nuclear imaging methods when combined with higher

resolution modalities like CT and MR provide better understanding of the disease

characterization along with better anatomical information. A study using SPECT/CT

tracked indium-labeled monocytes to the plaque regions [90]. Another study

tracked T lymphocytes to culprit lesions in case of patients awaiting carotid

endarterectomy using 99Tc labeled IL2 and a significant reduction of the tracer

uptake was seen after statin therapy [91]. The limitation of partial volume effect

with PET is now being overcome with the PET/MR coupling where the exact volume

can be identified with MR [92].

24

1.2.3 Invasive imaging

Noninvasive identification of vulnerable plaque must be the ultimate goal in

order to arrive at a cost-effective solution with minimal risk. Most noninvasive

modalities face the drawbacks of coronary artery motion, small size and the

location. With several competing invasive techniques, the initial prospective

identification of vulnerable plaques may be achieved by an intracoronary modality.

1.2.3.1 Angiography

Coronary angiography has been the gold standard for estimating luminal

narrowing. Angiography can assess lumen borders, but not the plaque morphology,

components and the severity of the disease. Remodeling phenomenon affects most

coronary lesions and preserves the luminal area and hence is not detected by

angiography [93-96]. Diffuse nature of atherosclerosis results in underestimation of

the stenosis. Concentric and symmetrical disease may give the appearance of a

completely normal artery under angiography [93-95]. The interobserver and

intraobserver variability is high when the stenosis is 30-80% of the diameter [97].

The predictive power of angiography is low since 70% of the acute events occur

despite normal angiograms [98]. Also, studies show that in 48-78% of the MI

patients, stenosis is

25

has a low discriminating power to identify vulnerable plaques, it provides

information on the entire coronary tree and serves a guide for invasive imaging and

therapy.

1.2.3.2 Angioscopy

Thrombi, plaque surface and ruptures can be directly visualized with

intracoronary angioscopy. Extent of the disease is diagnosed by the color of the

plaque. Multiple yellow plaques indicating higher plaque instability were seen in all

three coronary arteries in patients with MI [102]. ACS occurred more frequently in

patients with yellow plaques than in patients with white plaques [103]. Angioscopy

requires the total occlusion of the artery and blood flushed out with saline which

may induce ischemia. Angioscopy can be performed in a limited part of the vessel.

1.2.3.3 Elastography

Elastography is based on the principle that deformation or the strain of a

tissue is related to its mechanical properties. Ultrasound is used as a stressor and

the strain per angle is plotted as a color-coded contour of the lumen. Increased

circumferential stress leads to increased radial deformation of the plaque

components. Typically, for pressure differences of 5 mmHg, the strain induced is 1%

which requires sub-micron estimation of the deformation. Speckle tracking in video

signals is the main method of using elastography. For intravascular purposes a

correlation based elastography is employed. The displacement of the vessel wall and

26

the region in the plaque are found by cross-correlation. The strain of the tissue is

then found using the differential displacement between the two. This method is

suited for strain values

27

between patients with stable angina, unstable angina and acute MI [112]. The

thermistor of the catheter has a temperature accuracy of 0.05 C, time constant of

300 ms and a resolution of 0.5mm. It was also seen that patients with higher

temperature gradient have a significantly worse outcome than patients with a low

gradient [113].

1.2.3.5 Near infrared spectroscopy

Molecular vibrational trasnsitions measured in the near infrared region

(750-2500 nm) gives the chemical composition, qualitative and quantitative

information about the plaque components. When a molecule is exposed to infrared

radiation, the atoms absorb a portion of the light at frequencies that induce physical

changes in the molecule. A spectrometer measures the frequencies of the radiation

absorbed by the molecule as a function of energy. The magnitude of absorption is

related to the concentration of species within the material. Combinations of carbon-

hydrogen and carbon-oxygen functional groups, water and other components in

tissue result in characteristic absorbance patterns. The presence or absence of

particular frequencies is the basis for tissue characterization. Photons in the NIR

region penetrate the tissue well and no preparation of the sample is necessary. Also,

the hemoglobin has relatively low absorbance making diffuse NIR spectroscopy an

attractive technique [114]. Algorithms have been developed to identify lipid pools

like the partial least squares discriminate analysis [115]. PLS-DA model was able to

distinguish lipid pool and other tissue samples through up to 3mm of blood with at

28

least 86% sensitivity and 72% specificity [116]. The issue of probe illumination area

of 1cm in diameter that may result in misclassification needs to be resolved. A 3.2 Fr

NIR catheter has been developed for in vivo validation.

1.2.3.6 OCT

OCT measures the intensity of the back-reflected light with a Michelson

interferometer technique. Wavelength of 1300 nm is used since it minimizes the

energy absorption by vessel wall components. The light is split into two signals. One

is sent into the tissue while the other to a reference arm with a mirror. Both signals

are reflected and cross-correlated by interference of the light beams. The mirror is

dynamically translated to achieve incremental cross-correlation with penetration

depths in the tissue. High resolution images ranging from 4 m to 20 m can be

achieved with a penetration depth of up to 2 mm [117]. The frame rate is ~15

frames/sec. Lipid pools generate decreased signal intensity compared to fibrous

regions [118]. Compared to IVUS, OCT demonstrates superior delineation of the thin

caps or tissue proliferation [119]. OCT can also be used in pharmacologic or catheter

based interventions like stenting. This high resolution technique has shown to

detect few cell layers of neointimal growth after an intervention [120]. In vitro

characterization of plaques with OCT demonstrated high sensitivity of 79%, 95%,

90% and specificity of 97%, 97%, 92% for fibrous, fibrocalcific and lipid-rich

regions respectively [121]. In vivo studies show that OCT can identify intimal

hyperplasia and lipid pools more frequently than IVUS [122]. A study at 6-month

29

follow-up after drug eluting stent placement, OCT identified neointimal coverage of

SES that could not be detected with IVUS [6]. A recent study with AMI patients, the

incidence of plaque rupture was 73% with OCT compared to 47% and 40% with

angioscopy and IVUS respectively [123]. In the same study, the thin cap was

estimated as 49 21 m. Limitations are low penetration depth and light

absorbance and scattering by blood which requires saline infusion.

1.2.3.7 IVUS

Conventional IVUS is based on the intensity of the backscattered echoes.

Lumen and the vessel wall can be visualized in real time and with high resolution.

Current IVUS catheters for coronary imaging have a center frequency of 25- 40 MHz

with theoretical resolutions of 31-19 m respectively. The axial resolution is ~80

m and the lateral resolution about 300 m. Frame rate is 30frames/sec [95]. An

illustration of the IVUS catheter is shown in Figure 6.

Studies comparing IVUS and histology show that the plaque calcification can

be detected with a sensitivity of 86-97% [124, 125]. Sensitivity for

microcalcification is ~60% [126]. Lipid pools are detected with sensitivity of 78-

95% and a low specificity of 30% due to misclassification of echolucent areas by

necrotic tissue [127, 128]. Positive remodeling associated with unstable plaques

may be classified as high risk with IVUS [129]. In a follow-up study of 114 patients,

patients who experienced ACS were found to have eccentric plaques at the time of

previous IVUS imaging [130]. A study reported that IVUS guidance during DES

30

implantation has the potential to influence treatment strategy and reduce both DES

thrombosis and the need for repeat revascularization [131].

Figure 6: Illustration of IVUS catheter

3D IVUS has led to important observations regarding the longitudinal extent

of plaque and restenosis after coronary interventions [132]. Bi-plane angiography is

used along with IVUS that produce more accurate 3D images [133]. Three-

dimensional IVUS (3D-IB-IVUS) allows volumetric reconstruction of sequential

circumferential scans 1mm apart. RF Integrated backscatter obtained with a

conventional 40 MHz IVUS catheter is color coded for better plaque

31

characterization. The applicability of 3D-IB-IVUS in detecting reduction in lipid

volume after 6 months of statin therapy and also quantification of the increase in

fibrous region of the plaque volume was reported [134, 135]. In this study, changes

were seen without any significant change in the lumen area and hence suggest that

this technique is able to identify early changes in plaque characteristics.

Spectral analysis of IVUS backscatter has led to classifying lesions as calcified,

fibrofatty, calcified-necrotic core, and lipid-rich areas [136]. This study assessed

various spectral algorithms like the classic Fourier transform (CPSD), Welch power

spectrum (WPSD) and autoregressive models (MPSD) and found that the

autoregressive classification tree provided the best correlation with histology. The

algorithm accepts two borders luminal and media-adventitial border. For each

window of 480 m within a scanline, eight frequency domain features are estimated

and each combination of these parameters was mapped to one of four histologically

derived categories. The predictive accuracy was ~80% for all four tissue types.

Limitation of VH is that calcification from necrotic core cannot be distinguished.

Also there is a 480 m window over which the parameters are estimated. It is

questionable when parameters over a smaller region are estimated will show any

significance to characterization. A recent study evaluated the feasibility of

combined use of VH IVUS and OCT for detecting TCFA [137]. The study concluded

that neither modality alone is sufficient for detecting TCFA, suggesting a combined

use of OCT and IVUS in the future.

A recent study examined the feasibility of wavelet analysis of IVUS signals in

detecting lipid-laden plaques in vitro as well as in vivo [138]. RF signals from lipid

32

regions showed different pattern than fibrous regions on a certain scale that

signified smaller wavelengths and thus higher resolution. Fatty plaques could be

detected from the clinical samples with a sensitivity of 81% and a specificity of 85%.

Limitation is that all the plaques analyzed had a thickness >0.5 mm, and any lipid

core had a thickness >0.3 mm. Therefore, it is not known whether it is possible to

analyze thinner plaques or to identify very thin lipid cores with this method.

Although IVUS characterization of plaques has been very promising, no one

has yet produced a technique with sufficient spatial and parametric resolution to

identify a lipid pool with a thin cap.

1.3 Overview of limitations of Imaging Modalities

An overview of different diagnostic methods for detecting vulnerable plaques

is shown in Figure 7. New methods may identify additional characteristics of the

plaque enabling physicians to plan diverse treatments. Although a multifocal disease

requiring systemic therapies, detecting vulnerable plaques may still prevent MI and

strokes, reducing the effort and cost of managing a systemic disease.

Limitations, requirements w.r.t. imaging vulnerable plaque and image

resolution of different imaging modalities and the specific tissue the modality best

identifies is given in Table 3. Each imaging technique has its insufficiencies that

need to be resolved. From a clinical diagnosis perspective, a combination of many of

these imaging modalities may be a requisite to identify a vulnerable patient.

33

Figure 7: Various diagnostic methods for the detection of vulnerable plaque

Table 3: Imaging capabilities of various modalities w.r.t. vulnerable plaque

OCT IVUS MRI CTA Angiography

Spatial

Resolution

(m)

5-20 80-120 80-300 400-800 100-200

Probe Size

(m)140 700 N/A N/A N/A

Thin Cap Yes No No No No

Best suited

for

Thin caps of

atheromasFibroatheromas

Inflammation and

Characterization

Calcium

scoring

Lumen

variations

34

CHAPTER II

PROBLEM FORMULATION

The most serious mistakes are not being made as a result of wrong answers.

The truly dangerous thing is asking the wrong questions

- Peter Drucker

In vivo identification of vulnerable plaque by imaging techniques is essential

to prevent acute events. Several non-invasive and invasive imaging techniques

discussed in previous chapter, Diagnosis, are currently under development and

validation. None of these techniques can identify a vulnerable plaque alone or

predict its further development. Of all the vascular imaging modalities, the ability of

IVUS to directly image the vascular wall with high resolution unlike angiography has

enabled its use in assisting physicians to detect plaques and evaluate therapeutic

interventions [139, 140].The high sensitivity of IVUS in detecting atherosclerosis

and quantifying plaques has been clinically accepted [141-144]. Miniaturization of

the IVUS transducers permits tomographic visualization of a cross-sectional arterial

anatomy [145]. Although several studies have reported plaque imaging abilities of

IVUS, Narula et al. identify that clinical identification of culprit plaques has still not

35

been achieved [146]. DeMaria et al. state that none of these methods are definitive

because the morphology descriptors are based on retrospective studies and

vulnerable plaque characteristics vary across studies [147]. Also, non-culprit

plaques exhibit similar characteristics as culprit plaques [148]. These shortfalls of

IVUS arise due to the imaging device limitations and lack of appropriate tissue

characterization methods.

Foremost, the resolution of conventional transducers is not adequate to

image the thin cap of the plaque, thickness ranging 2319 m [23]. Limitations of

conventional IVUS transducers based on PZT include narrow bandwidth of 43%

(lower axial resolution, best around 62 m), inability to focus (lower lateral

resolution, around 300 m) and the extended ring-down of the PZT transducers

[149, 150]. Furthermore, clinically available systems rely on the standard Fourier

transform for processing of the RF backscattered signals and tissue characterization.

Better delineation of the plaque is possible by improved transducer design and new

methods of analyzing RF backscatter signals.

In order to address the need for identifying vulnerable plaques, the

combination of a high resolution focused polymer transducer and the multi

resolution analysis of RF signals from tissue harmonic imaging of the atherosclerotic

plaque was proposed.

A high resolution focused transducer fabricated using PVDF-TrFE, termed

PMUT was developed in the BioMEMS laboratory at the Lerner Research Institute

[151]. In comparison with the conventional transducers, the focused PMUTs exhibit

36

broad bandwidth (~120% at -6dB). With the appropriate assembly of the polymer

film, backing, and electrical and acoustical impedance matching, Near-theoretical

axial resolution of ~19 m and diffraction limited lateral resolution of 80 100 m,

were demonstrated [152]. The broad bandwidth of the transducer facilitates

harmonic imaging. The polymer transducer allows focusing of harmonic content to

within the narrow coronary geometry [152].

Tissue harmonic imaging, considered a recent breakthrough in diagnostic

ultrasound, as important as Doppler, offers substantial advantages such as

nonlinear information, improved lateral resolution, higher contrast resolution, low

near field spatial variation and decreased side lobes [153-156]. These studies were

based on frequencies below 10 MHz. The results of the harmonic imaging

experiments showed the feasibility of intravascular THI with a conventional IVUS

catheter both in a phantom and in vivo rabbit aorta [157, 158]. The harmonic

acquisitions also showed the potential of THI to reduce image artifacts compared to

fundamental imaging. The harmonic imaging of human coronary arteries at 20 MHz,

30 MHz and 40 MHz using the pulse inversion technique was reported by the

BioMEMS laboratory [159]. This study was limited to the feasibility of pulse

inversion technique with PMUTs and further analysis of harmonic signals for tissue

characterization was not suggested. The RF harmonic signals were further analyzed

using multiresolution analysis (discussed in forthcoming chapter) instead of Fourier

analysis of the signals for various reasons explained later on and showed that each

frequency offers unique vessel wall information [160]. Consequently it was

hypothesized that there may have always been much anticipated information about

37

lipids and thin caps in the fundamental and harmonic RF signals and if processed

with the appropriate methods may result in better tissue characterization, leading

to additional diagnostic information.

2.1 Specific Aims

The hypothesis is that multi resolution analysis of IVUS RF signals from tissue

harmonic imaging of the vulnerable plaque with a focused broadband polymer

transducer will result in additional diagnostic information.

This hypothesis is tested by undertaking the following specific aims:

Aim 1

(a) Establish system linearity and fluid/lipid nonlinearity with a PMUT.

(b) Multi resolution analysis of RF backscattered fundamental and harmonic signals

thereby identifying plaque morphology, comp