Detecting Vulnerable Plaques with Multiresolution Analysis
1. Cleveland State University
EngagedScholarship@CSU
ETD Archive
2011
Detecting Vulnerable Plaques with Multiresolution
Analysis
Sushma Srinivas
Cleveland State University
How does access to this work benefit you? Let us know!
Follow this and additional works at: http://engagedscholarship.csuohio.edu/etdarchive
Part of the Biomedical Engineering and Bioengineering Commons
This Dissertation is brought to you for free and open access by EngagedScholarship@CSU. It has been accepted for inclusion in ETD Archive by an
authorized administrator of EngagedScholarship@CSU. For more information, please contact library.es@csuohio.edu.
Recommended Citation
Srinivas, Sushma, "Detecting Vulnerable Plaques with Multiresolution Analysis" (2011). ETD Archive. Paper 279.
2. 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
4. 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
5. 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
6. 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.
7. 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
8. 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.
9. 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.
10. 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
11. 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 µm to 73 ± 28µm. The
harmonic signals were better able to identify thin caps and micro-calcifications than
in fundamental mode. Lipid accumulations, as thin as 200 µm 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.
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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. Russert’s 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
30. 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
31. 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
32. 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.
33. 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.
34. 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.
35. 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
36. 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
37. 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.
38. 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
39. 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
40. 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.
41. 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
42. 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
43. 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 Laplace’s 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].
44. 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 due to technical
problems and >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].
45. 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.
46. 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].
47. 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.
48. 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
49. 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
50. 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
51. 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].
52. 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 <50% [99-101]. Thrombosis and ruptured plaques were seen in
angiograms done one week before the acute event [101]. This suggests predictive
power may be higher if angiography is timed appropriately. Although angiography
53. 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
54. 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 <2.5% [104]. In vitro studies have shown that there is a
difference in the strain between fibrous, fibro-fatty and the fatty components
whereas these could not be differentiated with echo intensity based IVUS [105, 106].
Significantly higher strains were found for non-calcified than calcified plaques
[107]. In a pig study, high strain rates were associated with the presence of
macrophages and the fatty regions had a higher mean strain value [108]. In an ex
vivo study of human coronary arteries using a 20 MHz array catheter and
intraluminal pressures of 80-100 mmHg, strain values of 0.27, 0.45 and 0.60% were
found for fibrous, fibro-fatty and fatty plaque components, respectively. Plaque was
considered vulnerable when a high strain region was present at the lumen-plaque
boundary that was surrounded by low strain values. In vitro study of 54 arteries
showed high sensitivity and specificity to detect vulnerable plaques [105, 109, 110].
1.2.3.4 Thermography
A rise in the temperature is seen in inflammated tissue. The hypothesis is
that there is an increased change in temperature in case of vulnerable plaques as it
is an active metabolic area. Temperature heterogeneity was found in carotid
plaques taken from endarterectomy patients. The difference in temperatures was up
to 2.2 °C and a negative correlation between the temperature differences and cap
thickness [111]. Another study reported a temperature difference of 1.5 ±0.7 °C
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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.
61. 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
atheromas
Fibroatheromas
Inflammation and
Characterization
Calcium
scoring
Lumen
variations
62. 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
63. 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
64. 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
65. 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, composition and pathology at different scales.
(c) Estimation of normalized nonlinear parameter thereby enhancing the spectral
parameters for improved characterization of the vulnerable plaque.
Establishing system linearity ensures any harmonic detection is due to the
medium and the sample under consideration. Successful outcome with MRA may be able
to discretely identify constituents of the plaque at different resolutions (termed scales in
MRA). This may also help isolate lipid pools that supposedly exhibit nonlinearity.
66. 38
Enhancement the spectral parameters with nonlinear data may offer improved
characterization of the plaque.
Aim 2
(a) Evaluation of the competence of PMUT with respect to OCT for stent imaging.
(b) Estimation of extent of recurrence of coronary artery disease (CAD) secondary to
stent deployment.
(c) Application of multi resolution analysis for the evaluation of stent apposition for
the apt deployment of stents.
Imaging of stents and neointimal growth require high resolution in order t0
distinctly image the stent struts which is currently not attainable with conventional
IVUS transducers. PMUT may enable competencies of OCT to be realized with
PMUT-IVUS alone. Stents may be better deployed if new methods of evaluating stent
apposition are developed.
Aim 3
(a) Application of MRA for carotid arteries.
(b) Extension of MRA with modified wavelets for various applications like imaging of
cell clusters and cell growth over scaffolds in tissue engineering.
Ability to image a monolayer of cells and cell clusters has great potential in many
research areas.
67. 39
2.2 Significance of this study
The principal objective of this study is a high resolution IVUS technique for
the early detection of plaque thereby reducing the incidences of acute coronary
events and strokes. Estimates show that 13 million individuals suffer from CAD, 1.1
million of them represent with acute myocardial infarction and 75% of acute
coronary episodes are due to plaque rupture and 87% of all strokes are ischemic
[47, 161].
The proposed technique is novel as it takes advantage of tissue analysis at
multiple frequencies. There has been no report to date on the application of MRA on
IVUS harmonic imaging for plaque characterization. The proposed research signifies
the development of a technique for the timely detection of vulnerable plaques and
vascular wall transformation. The findings from this research may have the positive
impact of detecting atherosclerosis at an early stage and identifying vulnerable
patients and help undertake preventive measures. The findings from stent imaging
may also result in therapeutic interventions with better outcomes.
68. 40
CHAPTER III
MATERIALS AND METHODS
Technology presumes there's just one right way to do things and there never is.
-Robert M. Pirsig
Materials and methods described below are common to most experiments in
this study. Any new material or method specific to a particular experiment is
described in the corresponding section.
3.1 Materials
3.1.1 PVDF-TrFE
Clinically available transducers employ ceramic PZT crystals as ultrasonic
source elements. Ceramics are brittle and cannot be easily fashioned to any desired
focusing profile. Ceramics need additional matching layers due to poor acoustic
matching with tissue.
69. 41
PVDF and its copolymer – PVDF TrFE can be rendered piezoelectric by
poling/polarization. A high electric field is applied in order to rotate the molecular
dipoles in the same direction. PVDF polymer piezo films have many advantages –
flexibility, high mechanical resistance, homogenous piezo activity, high dielectric
constant and chemically inert. They are available from 9 µm to 100 µm [162]. PVDF-
TrFE does not need stretching and demonstrates higher level of piezoelectricity
than PVDF [163-165]. Such polymers offer broad bandwidth necessary for high axial
resolution and harmonic imaging. These polymers can be shaped into desired
contours using appropriate methods. Shaping of the polymer film into a spherical
section enables focusing resulting in good lateral resolution in the focal zone. These
polymers also offer better acoustic matching with the tissue [162, 166]. PVDF and
its copolymers are compatible with IC fabrication facilitating the fabrication of
transducer film and integrated electronics embedded in one miniaturized device.
The drawback of PVDF is that the transducers made from these polymers
have high output impedance and the electrical impedance mismatch results in low
SNR. PVDF-TrFE has a higher electromechanical coupling coefficient of 0.30 as
compared to 0.15-0.20 for PVDF [162]. As a result, copolymer devices exhibit higher
insertion loss and a lower sensitivity than PZT [164].
The PVDF-TrFE used in this study is 9 µm thick with 200 nm Au coating on
one side. The thickness of the film defines the wavelength of the signal generated.
Total destructive interference occurs when the film is exactly one wavelength thick.
Constructive interference occurs when the film is one-half wavelength or an odd
multiple of one-half wavelength thick. Ideally, the 9 µm thickness generates a wave
70. 42
of 18 µm wavelength. The speed of sound in a piezoelectric material is given by
equation (1)
ܿ = 2݂ܶ = 2ܰଷ௧ (1)
where, cc, fc,, T, and N3t are the speed of sound in the material, resonant frequency,
thickness of material if it were λ/4 thick and frequency constant, respectively.
The quarter wavelength, Lc, of the material at the design frequency f is given by
equation (2)
ܮ =
ସ
(2)
3.1.2 Reflectors
A 1.5 mm thick glass plate is used as a specular reflector for pulse echo
measurements. Differences in theoretical and empirical radiation profiles are seen
due to the finite size of the reflector, that is the reflector is not an ideal point source.
Measurements with the glass reflector, although not ideal, are used as bases for all
other measurements from biological specimen.
3.1.3 Amplifiers
As mentioned earlier transducers fabricated with PVDF-TrFE copolymer
present low SNR due to loading of the low impedance cable. SNR can be improved
by impedance matching with a high impedance preamplifier in close proximity with
71. 43
the transducer [152, 167, 168]. A preamplifier circuit with 12 dB gain and power
consumption of 35 mW is used in conjunction with the transducer [151, 152]. An
external amplifier of 30 dB gain is also used to boost the signal. These amplifiers
must have broad bandwidth and fast recovery time in order to be able to image
tissue specimen close to the face of the transducer. A 60 dB power amplifier with
low harmonic distortion is used to amplify the drive signals for harmonic imaging.
3.1.4 SMA cables
SMA cables (Pasterneck RG 178 B/U) are used to make the connections from
the transducer lead to the pulsing and data acquisition units. Maximum transfer of
power results when the transducing element is pulsed at quarter wavelength. This
excites the transducer to generate broader range of frequencies. The transmission
line, RG 178 B/U has a velocity factor (VF) of 0.695. The length needed for quarter
wavelength matching is given by equation (3)
ܮఒ =
.ி
ସ
(3)
where, Lq, c, VF and f are the length of the cable for quarter wavelength matching,
velocity of an EM wave in vacuum, velocity factor and the operating frequency,
respectively.
For a frequency of 40 MHz, this results in a length of 1.3028 m. SMA cable of
this length is used to excite the transducer element. The effect of cable length is
shown in the Results section. For a 20 MHz signal, a length of 2.6 m is needed. Since
72. 44
the quarter wavelength matching is frequency dependent, for the same cable length
in case of harmonic imaging, the transducer is not energized at the same power for
20 MHz and 40 MHz.
3.1.4 Tissue specimen
Human coronary arteries were obtained from autopsy without any personal
identification information. 5 coronary arteries fixed in formalin were received.
Fresh coronary arteries received were imaged and then fixed in HistochoiceTM. The
arteries were refrigerated in DMSO solution until they were imaged. During the scan
process, the arteries were placed in DI water. Some of the arteries were splayed
open to image due to the limitations posed by the transducer size. All the arterial
segments were imaged in sections of 4-5 mm to prevent the submersion of
preamplifier electronics in water.
Due to the unavailability of fresh human coronary arteries, arterial segments
were obtained from CHTN, Columbus. Fresh human carotid plaques obtained from
endarterectomy were imaged and then fixed for histology.
Stented coronary arteries were unavailable. Stents were deployed after the
excision of coronary arteries. Two fixed stented femoral arteries with neointimal
growth were available for imaging.
Due to the unavailability of fresh arteries, several other biological specimens
were imaged. Carotid arteries, Rabbit aortic grafts, cell clusters of MDCK cells,
73. 45
scaffolds for tissue engineering and adenocarcinoma from mice were imaged and
new methods developed to process the signals from these specimen.
3.2 Making of the Device
3.2.1 Fabrication of Transducer
Focused transducers were fabricated using the formerly defined protocols as
reported by Fleischman et al [151, 152]. A 9 µm thick PVDF-TrFE copolymer with
200 nm of gold coating metallized side (Ktech corp.) placed on a standard (100)
Silicon with 1.5 µm growth of SiO2 or a 0.7 mm thick polycarbonate substrate is
spherically deflected using an air pressure system. Voltage levels of 100 – 250 mV,
350-500 mV and 400-600 mV are used for 0.5-1.0 mm, 1.5-2 mm, and 4 mm
apertures respectively. An appropriate pressure is selected depending on the focal
number required which depends on the specimen being imaged. 2902 conductive
epoxy (Tra-duct Corp.) is injected to form a thick backing without any air bubbles.
An electrical lead is placed inside the epoxy to form the positive electrode of the
transducer. The ground electrode is placed on the metallized side once the epoxy is
cured at room temperature (at least 24 hours). The electrodes are then connected to
either preamplifier circuits or 50 Ω terminations. A flat model of the transducer was
used for experiments such as cell cluster imaging, tumor imaging and other
harmonic measurements, all described in subsequent chapters. In order to image
74. 46
the arterial segments, the transducers were fabricated using the polycarbonate
substrate and not Si due to the mere reason of Si being brittle and the ease of
cropping with the polycarbonate substrate. A tower model was built so that the
transducer could be inserted vertically down in to an arterial segment pinned to a
paraffin block. The Figure 8 depicts the transducers in different configurations. For
imaging arterial segments, 0.6 mm to 1.5 mm with and w/o tower configurations
were developed.
Figure 8: 40 MHz PMUT transducer
3.2.2 Preamplifier Circuit
The flat configurations of the transducers were not connected to the
preamplifier circuit as wider apertures of 2 mm or 4 mm were employed. The signal
75. 47
loss with 2 mm and 4 mm is less compared to 1 mm apertures [169]. Also,
depending on the echogenicity of the specimen used, the 2 mm and 4 mm
transducers in juxtaposition with the preamplifier circuit may drive the external
amplifier in to saturation mode as explained in the results section. As a result, it was
preferred to keep the signals within the operating range by not including the
preamplifier circuit for the flat configuration.
The tower configurations mounted with transducers ≤ 1 mm benefited from
the additional gain of the preamplifier circuit placed in close proximity to the
transducer. The preamplifier circuit is depicted in Figure 9.
Figure 9: Preamplifier circuit for a PMUT
The preamplifier has a high input impedance to match the high output
impedance of the transducer. This matched electrical impedance improves the SNR
of the device. The AD 8001 has a broad bandwidth of 440 MHz for a gain of +2,
which implies for a gain of +4 (12 dB), the bandwidth is 220 MHz, as given by
equation (4).
Transmit
Transducer
C1
15 pF
R3 10 kΩ
R1
680 Ω
R2
220 Ω
D2D1
D3
D4
AD8001
Output
76. 48
In general,
ܩ = 1 +
ோଵ
ோଶ
ܽ݊݀ ܩ × ܹܤ = ܿݏ݊ݐܽ݊ݐ; ݓℎ݁ܩ݁ݎ = ݅ܽܩ݊ (4)
This BW exceeds the required bandwidth of the transducer for harmonic
imaging although the gain flatness of 0.1 dB is up to 100 MHz. The current feedback
amplifier also offers low distortion and fast settling times. The gain of AD 8001 may
not be sufficient as discussed in the results section. The preamplifier circuit is then
connected to an external amplifier through a 50 Ω termination.
3.2.3 External Amplifier
The pre-amplified pulses are passed through a limiter circuit and a 30dB
external amplifier (Miteq AU-1114, Miteq Corporation, Hauppauge NY, USA).
Overall, the system gain was 42 dB on the receive side. The Miteq has a -1 dB
compression point and the second harmonic is typically -15dBc at +10dBm.
Although Miteq AU-1114 has been used as an external amplifier for this application
for several years, it is shown in the results section that Miteq is unsuitable beyond a
certain operating range for this application. The appropriated operating range was
established and is presented in the results section. The Miteq amplifier was not used
in the experiments where the input was above +1dB, unit level at which the
amplifier is in hard saturation.
77. 49
3.2.3 Testing of Transducers
The transducers are then characterized for their axial resolution and
bandwidth, using the pulse-echo technique. A glass plate is used as a reference
reflector for acquiring the pulse echo. The theoretical focal number, f# is estimated
as in equation (5)
݂# = ܣ/ݎ (5)
where r is the radius of curvature and A is the diameter of the aperture.
The empirical f# is measured from the pulse echo. The theoretical and experimental
focal numbers are compared. The axial radiation pattern and lateral radiation
patterns are also acquired. The axial resolution, lateral resolution and the focal
length are given by the following equations (6-8).
ܴ௫ =
ଶௐ
(6)
ܴ௧ = ߣ. ݂# (7)
ܨܱܦ = 7.0 ߣ. ݂# (8)
where, Rax, Rlat, DOF, c, BW, , and f# are the axial resolution, lateral resolution,
depth of focus (focal length), speed of sound in the medium, bandwidth, wavelength
and focal number, respectively. A transducer of appropriate aperture and focal
length is chosen to image depending on the specimen under consideration.
78. 50
3.3 Data Acquisition
Whenever Science makes a discovery, devil grabs it while the
angels are debating the best way to use it
– Alan Valentine
3.3.1 Synchronized pull back
Clinically available IVUS systems are ECG-gated for acquiring appropriate
scan lines. The ECG-gating box sends out continuous trigger pulses to the data
acquisition card (usually Gage) upon detection of the peak R-wave. Scan lines are
acquired for every trigger pulse. The catheter is operated by a pullback device which
withdraws the catheter at a constant rate of 0.5mm/s or 1mm/s. The continually
acquired data is later registered with the slices along the arterial segment. This type
of pullback and data acquisition is not synchronized and the registration is
inaccurate.
I conceived, designed and implemented a synchronized pullback system
where the pullback of the catheter is synchronized with the trigger pulses and hence
the data acquisition in real time. This was tested using the Newport motion
controller, ESP 300 and the Cobra Gage card. Compared to the implementation
without synchronization, this method of synchronized implementation cut down the
scan time by at least ten-fold depending on the averaging used. For example, for the
acquisition of 32K samples with 1024 averages, the time taken with the new system
