Nonmodel-Based Dynamic Contrast-Enhanced Magnetic ... · Background: Parameters of carotid atherosclerosis dynamic contrast-enhanced MRI (DCE-MRI) are associated with stroke risk

Nonmodel-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Assessment of High versus

Low Risk Carotid Atherosclerosis

by

David Bailey MacLean

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

Institute of Medical Science University of Toronto

copy Copyright by David Bailey MacLean 2011

ii

Nonmodel-Based Dynamic Contrast-Enhanced Magnetic

Resonance Imaging for the Assessment of High versus Low

Risk Carotid Atherosclerosis


Master of Science


2011

Abstract

Background Parameters of carotid atherosclerosis dynamic contrast-enhanced MRI (DCE-

MRI) are associated with stroke risk indices but studies have only evaluated symptomatic

arteries I hypothesized that DCE-MRI parameters are different between carotid

atherosclerotic plaques at high and low risk for precipitating ischemic stroke Methods High

and low risk carotid plaques undergoing nonmodel-based DCE-MRI (n=18) were compared

using two independent schema 1) clinical standard (high risk defined as ipsilateral strokeTIA

lt1 week old or stenosis gt70) 2) MRI standard (high risk defined as presence of intraplaque

hemorrhage [IPH]) Results IPH-positive plaques (n=9) exhibited greater area under the

curve early and late enhancement rate and peak enhancement than IPH-negative plaques

(n=9) (plt005 for all) High (n=8) and low (n=7) risk plaques defined by clinical criteria were

not differentiated by any DCE-MRI parameters Conclusions Nonmodel-based DCE-MRI

discriminates high versus low risk carotid plaque based on the presence of IPH but not by

clinical criteria

iii

Acknowledgments

I thank my supervisor Dr David Mikulis for his consistent encouragement while navigating

the many challenges of this thesis I am truly grateful for his mentorship and patience at the

most difficult of times and am inspired by his deep passion for all things vascular

I thank Dr Adrian Crawley for his invaluable insight and advice at every stage of this

research for his excellence in the teaching of MRI physics and for his warm guidance as a

mentor

I thank Dr Alan Moody for his expertise as an advisor for counsel on the experimental design

of this study and for critical evaluation of this manuscript along with Dr Mikulis and Dr

Crawley

I thank Dr Frank Silver Dr Yael Perez Dr Martin del Campo Dr Leanne Casaubon and Dr

Thomas Lindsay for their enthusiastic support in the recruitment of study subjects

I thank the research MRI technologists Eugen Hlasny Keith Ta David Johnstone and Hien

Tran for their expert assistance in the development of the MRI protocol used in this study I

especially thank Eugen and Keith for consistently rising above and beyond the call of duty

I thank Jeffery Stainsby for hours of valuable discussion pertaining to technical considerations

in the development of the MRI protocol

iv

I thank Julien Poublanc for his lasting patience in answering my incessant ldquoquick questionsrdquo

during the seven month-long development of the automated analysis scripts integral to the

success of this thesis

I thank Dr Jesse Klostranec for his assistance in developing the MRI protocol at the outset of

this project

I thank Yang Sun for her administrative support over the course of multiple amendments

submitted to the research ethics board and for her assistance in obtaining written informed

consent from study subjects

I thank Dr Danny Mandell for his support and valuable advice throughout the duration of this

project

I thank my fellow lab members Aneta Chmielewski Anne Battisti Jay Han Joe Barfett John

Conklin Jorn Fierstra Kevin Sam Olivia Pucci Stephie Speith and Vincent Spano for their

friendship and support throughout the duration of this project

I thank Dr Steve Iscoe a first-class mentor for introducing me to the world of research and

fostering many of the scientific skills that aided in the successful completion of this thesis

I thank my friend house-mate and fellow Masterrsquos student Joseph Gabriel for his friendship

and moral support throughout the entirety of this project There is no doubt his well-focused

and critically valuable feedback improved the quality of my defense examination

v

I thank Anne McGee for her loving encouragement understanding and support from the

outset of this project

Most importantly I thank my parents David and Iris for instilling in me a hunger for

challenge and a strong sense of perseverance I owe all of my successes to their continued

love and support

vi

Dedication

I dedicate this work to my late grandfather Dr David Bailey MacLean

vii

Table of Contents

Acknowledgments iii

Table of Contents vii

List of Tables x

List of Figures xi

List of Equations xii

List of Abbreviations xiii

Chapter 1 Introduction 1

Chapter 2 Review of the Literature 4

21 Vascular Anatomy 4

211 Histological Organization of Arteries 4

212 The Carotid Artery 6

22 Atherogenesis 9

221 Early Lesion Development 9

222 Progression to Fatty Streak 10

223 Smooth Muscle Proliferation and Phenotypic Switching 10

224 Role of Hemodynamics 11

23 Characterization of Atherosclerosis 15

231 American Heart Association Classification 15 2311 Early Lesions 15

2312 Advanced Lesions 16

232 The Vulnerable Plaque 17

24 Stroke 18

241 Burden of Stroke 18

242 Types of Stroke 18

25 Angiographic Assessment of Atherosclerosis 18

251 Clinical Trials 19

252 Trial Impacts and Limitations 21

26 Magnetic Resonance Imaging 22

261 Blood Signal Suppression Techniques 23

27 Non-Contrast Enhanced Methods for MRI of Atherosclerosis 25

28 Use of Contrast Agents for MRI of Atherosclerosis 28

281 Contrast-Enhanced MRI 28

viii

282 Dynamic Contrast-Enhanced MRI 30 2821 General Theory 31 2822 Model-Based Analysis 32

2823 Nonmodel-Based Analysis 39 2824 Dynamic Imaging of Atherosclerosis 41

Chapter 3 Study Aims and Hypothesis 47

31 Study Aims 47

32 Hypothesis 48

Chapter 4 Methods 50

41 Study Design 50

411 Ethics Approval and Subject Recruitment 50

412 Subject Inclusion and Exclusion Criteria 51

413 Exclusion of Atypical Atherosclerosis 52

414 Sample Size Estimation 52

42 Carotid Artery Classifications 53

421 Classification by Clinical Criteria 53

422 Classification by Imaging Criteria 54

43 Magnetic Resonance Imaging Protocol 56

431 Subject Preparation 56

432 Localization of the Carotid Bifurcation 56

433 Carotid Vessel Wall MRI 59

434 Dynamic Contrast-Enhanced MRI 60

435 Post-Contrast Carotid Vessel Wall MRI 61

44 Post-Processing of DCE-MRI Data 62

441 Region of Interest Selection and Cropping 63

442 Image Coregistration 63

443 Signal Intensity Normalization of DCE-MRI Data 65

444 Curve Fitting of DCE-MRI Signal Intensity Time Course 66

45 Calculation of Nonmodel-Based DCE-MRI Parameters 67

451 Area Under the Curve 71

452 Early Enhancement Rate 72

453 Maximum Enhancement 72

454 Time to Peak 73

455 Late Enhancement Rate 73

456 Early-Late Enhancement Rate Ratio 73

46 Analysis of Nonmodel-Based DCE-MRI Parameters 74

461 Region of Interest Selection 74

462 Group Analysis 74

ix

47 Statistical Analysis 77

471 Evaluation of Curve Fitting Algorithm 77

472 Comparison of High versus Low Risk Arteries 77

Chapter 5 Results 79

51 Plaque Characteristics of Subjects with Successful MRI 79

511 Clinical Criteria 79

512 Imaging Criteria 79

52 AUC Enhancement Rate and Maximum Enhancement Are Increased in IPH-Positive High Risk Plaques Defined by Imaging Criteria 80

54 DCE-MRI Parameters Are Not Different Between High Risk and Low Risk Carotid Plaques Defined by Clinical Criteria 88

55 Curve-Fitting Using AFNI Provides Excellent Noise Reduction 88

Chapter 6 Discussion 90

61 Increased Enhancement Characteristics in High versus Low Risk Plaques Defined on Imaging Criteria by Presence of IPH 90

62 No Difference Between Carotid Plaques Defined as High and Low Risk by Clinical Criteria 94

63 Interpretation of Differences in Findings Between Clinical and Imaging Criteria for Definition of High and Low Risk Carotid Plaques 98

64 Methodological Considerations 99

65 Study Limitations 102

66 Future Directions 106

67 Conclusions 108

References 109

x

List of Tables

Table 21 MRI signal intensity of plaque components relative to muscle

Table 41 Demographics of enrolled subjects

Table 42 Summary of MRI scan parameters

Table 51 Carotid plaque characteristics of subjects with successful MRI

Table 52 Evaluation of goodness of model fitting by coefficient of determination

Table 61 Summary of significant findings by DCE-MRI parameter and ROI

xi

List of Figures

Figure 21 Artery wall histology

Figure 22 Carotid artery wall sheer stress during systole and diastole

Figure 23 Four-compartment model of contrast distribution within the body

Figure 24 Two-compartment model of contrast distribution within the tissues

Figure 41 Example of intraplaque hemorrhage on MRI

Figure 42 Result of model fitting in a representative artery

Figure 43 Nonmodel-based DCE-MRI parameter maps in a representative artery

Figure 44 ROI selection in a representative carotid plaque

Figure 45 Evaluation of model fitting in a representative artery

Figure 51 Mean signal intensity time course by presence of intraplaque

hemorrhage

Figure 52 Cumulative AUC versus time by presence of intraplaque hemorrhage

Figure 53 Incremental AUC versus time by presence of intraplaque hemorrhage

Figure 54 Early enhancement rate by presence of intraplaque hemorrhage

Figure 55 Maximum enhancement by presence of intraplaque hemorrhage

Figure 56 Late enhancement rate by presence of intraplaque hemorrhage

xii

List of Equations

Equation 21 Calibration of gadolinium-based contrast concentration

Equation 22 Extended Tofts model

Equation 41 Mutual information cost function

Equation 42 Curve-fitting model

Equation 43 Derivation of Cumulative AUC

Equation 44 Derivation of Incremental AUC

xiii

List of Abbreviations

18FDG 18Fluorodeoxyglucose

2D Two Dimensional

3D Three Dimensional

ACAS Asymptomatic Carotid Atherosclerosis Study

AFNI Analysis of Functional Neuroimages

AHA American Heart Association

AIF Arterial Input Function

ASSET Array Spatial Sensitivity Encoding Technique

AT2 Angiotensin II

AUC Area Under the Curve

CA Contrast Agent

CASANOVA Carotid Artery Stenosis with Asymptomatic Narrowing

CCA Common Carotid Artery

CEA Carotid Endarterectomy

CNR Contrast to Noise Ratio

CT Computed Tomography

CTA Computed Tomographic Angiography

DCE-MRI Dynamic Contrast-Enhanced Magnetic Resonance Imaging

DIR Double Inversion Recovery

DWI Diffusion Weighted Imaging

ECA External Carotid Artery

xiv

ECST European Carotid Surgery Trial

EES Extracellular Extravascular Space

eNOS Endothelial Nitric Oxide Synthase

ET Endothelin

FLAIR Fluid Attenuated Inversion Recovery

fMRI Functional Magnetic Resonance Imaging

FSE Fast Spin Echo

ICA Internal Carotid Artery

IPH Intraplaque Hemorrhage

IR Inversion Recovery

kep Transfer Rate Constant

ktrans Bulk Volume Transfer Rate

LDL Low Density Lipoprotein

LMMSE Linear Minimum Mean Square Estimate

MI Mutual Information

MIP Maximum Intensity Projection

MMP Matrix Metalloproteinase

MRA Magnetic Resonance Angiography

MRI Magnetic Resonance Imaging

MZ Net Longitudinal Magnetization

NASCET North American Symptomatic Carotid Endarterectomy Trial

NMR Nuclear Magnetic Resonance

xv

NO Nitric Oxide

NOS Nitric Oxide Synthase

NNT Number Needed to Treat

oxLDL Oxidized Low Density Lipoprotein

p Probability

PD Proton Density

PURE Phased Array Uniformity Enhancement

R Pearson Correlation Coefficient

R2 Coefficient of Determination

RF Radiofrequency

ROI Region of Interest

ROS Reactive Oxygen Species

SI Signal Intensity

SMC Smooth Muscle Cell

SNR Signal to Noise Ratio

SPECIAL Spectral Inversion at Lipids

SPGR Spoiled Gradient Recalled Echo

T1 Spin-Lattice Relaxation Time

T2 Spin-Spin Relaxation Time

TE Echo Time

TI Inversion Time

TIA Transient Ischemic Attack

xvi

TOF Time of Flight

TOF-MRA Time of Flight Magnetic Resonance Angiography

TR Repetition Time

TTP Time to Peak

ve Fractional Volume of Extracellular Extravascular Space

VEGF Vascular Endothelial Growth Factor

vp Fractional Volume of Intravascular Space

1

Chapter 1 Introduction

Atherosclerosis colloquially termed ldquohardening of the arteriesrdquo is a progressive low-grade

inflammatory process of the blood vessel wall that is responsible for a number of clinical

manifestations together referred to as cardiovascular disease the leading cause of death

worldwide (World Health Organization 2009) Of these manifestations heart disease and

ischemic stroke are together the most prevalent remaining the principal causes of

hospitalization in Canada (Heart and Stroke Foundation of Ontario [HSFO] 2009) with

economic costs totaling $22 billion (CAD) in lost productivity and healthcare expenses per

year according to the Canadian Heart Health Strategy-Action Plan Steering Committee

(2009) Over the previous two decades the tremendous financial (economic) and personal

(morbidity and mortality) cost of cardiovascular disease has spurred the improvement of

methods for assessing atherosclerotic burden and for predicting adverse health events arising

from atherosclerotic lesions Within this time the maturation of advanced imaging

technologies has further advanced atherosclerosis research by enabling high-resolution non-

invasive imaging of the disease as it occurs in vivo

In the application of these technologies a special focus has been paid to atherosclerosis of the

carotid arteries because lesions at this site are a substantial contributor to atherosclerotic

(ischemic) stroke Stroke remains the third-most common cause of death in Canada resulting

in long-term disability or death in 90 of cases (Heart and Stroke Foundation of Canada

[HSFC] 2011a) A substantial body of evidence has emerged to suggest that atherosclerotic

plaque composition rather than the degree of luminal stenosis provides greater sensitivity as

2

a metric for stroke risk assessment when evaluating the carotid atherosclerotic plaque This

evidence has grown in tandem with technological advances in magnetic resonance imaging

(MRI) Although generally more time-consuming and costly than other clinical imaging

modalities MRI affords safe (no exposure to ionizing radiation) and highly reproducible

imaging while also providing excellent soft-tissue contrast ideal for the study of

atherosclerosis However conventional MRI lacks the ability to discern the micro-scale

features that are most disparate between atherosclerotic plaques at high and low risk for

precipitating ischemic stroke The use of MRI to identify these differences in features which

include plaque inflammatory status and presence of neovasculature was recently

demonstrated (Kerwin et al 2006) with dynamic contrast-enhanced MRI (DCE-MRI) in an

attempt to overcome these limitations Despite a handful of pioneering studies (Aoki et al

1999 Kerwin et al 2003 Kerwin et al 2006 Kerwin et al 2008 Chen et al 2010 Chen et

al 2011 Dong et al 2011) however the application of DCE-MRI to imaging of the carotid

atherosclerotic plaque remains largely unexplored Thus there remains a lack of research

concerning the comparison of DCE-MRI features between high and low risk carotid

atherosclerotic plaques in humans

To address this paucity of data the present research was designed to identify differences in

uptake and distribution of an MRI contrast agent between carotid atherosclerotic plaques

identified as high or low risk for precipitating ischemic stroke To assess carotid plaque

composition high-resolution structural MRI was performed with multiple contrast

weightings followed by DCE-MRI Using these data an automated method for image co-

registration and analysis was developed and then applied for calculation and spatial

3

representation of several parameters of MRI contrast agent dynamics within the carotid

plaques Finally to examine the concordance between standard clinical criteria and the more

recently-defined (Kelodgie et al 2003 Vermani et al 2005 Sirol et al 2009 Wang et al

2010) imaging criteria for determining high versus low risk carotid plaque two independent

analyses were performed on the DCE-MRI parameters using the aforementioned criteria

categories to determine the relationship between each criteria grouping (high versus low risk)

and parameters derived from DCE-MRI data

4

Chapter 2 Review of the Literature

21 Vascular Anatomy

211 Histological Organization of Arteries

Arteries are the vessels through which blood flows from the heart to the tissues carrying

oxygenated blood in most cases (the pulmonary arteries carry deoxygenated blood) The

arterial system can be sub-divided into the large conducting arteries which are highly elastic

the smaller distribution arteries which are highly muscular and the microscopic arterioles

which lack many of the anatomic features of the two former subtypes (the capillaries which

are distinguished from the arterioles by the absence of smooth muscle cells (SMC) are not

considered here since these vessels are not susceptible to atherosclerosis) The conducting

and distribution arteries are composed of three well-defined layers the inner tunica intima

the tunica media and the outer tunica externa and associated vasa vasorum

The tunica intima consists of the endothelium and associated connective tissues and is

loosely interspersed with macrophages residing within the extracellular matrix superficial to

the endothelium (Stary et al 1992) The endothelium is a continuous monolayer of cells that

line the lumen of all blood vessels playing an important role in the maintenance of vascular

health The endothelium acts as a selective barrier to passage of substances in the blood and

coordinates transportation of nutrients and waste and the extravasation of leukocytes

including monocytes from the lumen into the arterial wall The biosynthesis and release of

nitric oxide (NO) is also a primary role of the vascular endothelium While the most notable

action of NO is inducible relaxation of SMCs through second-messenger pathways NO also

5

possesses anti-thrombotic and anti-platelet actions that are required for maintenance of

vascular health Dysregulated or insufficient endothelial NO production is detrimental to the

artery and leads to an imbalance between vasodilatory and vasoconstrictive factors a

condition termed endothelial dysfunction ndash a condition that is thought to be a key indication

of atherogenesis (see 221)

The internal elastic lamina lying immediately superficial to the endothelium denotes the

transition between tunica intima and tunica media vessel layers Composed of elastic

connective tissue this structure provides the capacity for elastic recoil while providing wall

strength required to withstand high blood pressures that occur within the large conducting

arteries For this reason the internal elastic lamina is thickest in the conducting arteries

becoming progressively thinner within the distribution arteries and beyond The tunica media

itself is composed of a variable number of concentric SMC sheets that lie immediately

outward from the internal elastic lamina being thickest in the distribution arteries Through

their constriction or relaxation SMCs regulate lumen diameter which in turn controls blood

flow through changes in vascular resistance The SMCs of the tunica media are directly acted

upon by NO which promotes vasodilation through activation of a second messenger-

mediated pathway

Superficial to the SMC layers of the tunica media lays the external elastic lamina which

denotes the transition between the tunica media and the tunica externa and provides a

functional role similar to that of the internal elastic lamina The tunica externa also called the

tunica adventitia lies superficial to the tunica externa and is the outer-most layer of the artery

6

This layer is composed primarily of collagen fibers that interconnect with adjacent structures

to provide structural stability Associated with the outer border of the tunica externa is the

vasa vasorum meaning ldquovessels of vesselsrdquo a network of small vessels that supply blood to

the arterial wall of arteries in which the demand for nutrient transport and waste removal

cannot be met by simple diffusion alone The adventitial vasa vasorum is found in vessels in

which the thickness of the tunica media exceeds 350 microm or approximately 29 SMC layers

(Wolinsky and Glagov 1969) and is thought to play an important role in facilitating the

progression of advanced atherosclerotic lesions (see 23) Figure 21 is a cross-sectional

representation of artery wall histology

212 The Carotid Artery

The carotid arteries are the major vessels supplying oxygenated blood to the brain and

extracranial structures The carotid arterial system is divided into three major vessels that

occur bilaterally the common carotid arteries the external carotid arteries and the internal

carotid arteries

The common carotid artery (CCA) is a conducting artery with an average internal diameter of

65 plusmn 10 mm in men and 61 plusmn 08 mm in women (Krejza et al 2006) The left common

carotid artery (CCA) originates at the aortic arch and travels through the thorax before

reaching the neck while the right CCA originates at the brachiocephalic artery At

approximately the level of the 4th

or 5th

cervical vertebra (C4ndashC5) the CCA bifurcates into the

internal and external carotid arteries

7

Considerable inter- and intrasubject variations exist in both anatomic location (with respect to

the cervical vertebrae) and geometry (with respect to the angle) of the carotid bifurcation

Within the carotid bifurcation and extending into the internal carotid artery lies the carotid

sinus a localized dilation of the arterial wall Contained within the walls of the carotid sinus

are baroreceptors pressure-sensitive mechanoreceptors responsible for sensing blood pressure

changes and eliciting the baroreceptor reflex Also present are numerous chemoreceptors

responsible for sensing partial pressures of oxygen and carbon dioxide together termed the

carotid body

Distal to the carotid bifurcation the external carotid artery (ECA) resembles the histological

organization of the muscular distribution arteries with an average internal diameter of 49 plusmn

07 mm in men and 44 plusmn 08 mm in women (Williams and Nicolaides 1987) The ECA is the

primary blood supply for the extracranial structures of the head scalp and face

The internal carotid artery (ICA) is the main vascular supply to the brain with an average

internal diameter of 51 plusmn 09 mm in men and 47 plusmn 08 mm in women (Krejza et al 2006)

Distal to its origin at the carotid bifurcation the ICA ascends through the skull base before

becoming continuous with the middle cerebral artery Prior to this the ICA gives rise to three

intracranial vessels the anterior choroidal artery the ophthalmic artery and the posterior

communicating artery

8

Figure 21 Micrograph of hematoxylin and eosin (HampE) staining in a carotid artery wall

segment removed by endarterectomy (surgical resection of carotid atherosclerotic plaque) in a

study subject (subject 07 left carotid artery) The inner artery wall is oriented toward the

right and is bordered by a layer of endothelial cells (arrows) The approximate border

between the tunica intima and the tunica media is denoted by the dashed line while the

approximate border between the tunica media and the tunica adventitia is demoted by the

solid line These denote the approximate locations of the internal and external elastic

laminae respectively Adv = tunica adventitia Med = tunica media Int = tunica intima

Lumen = carotid vessel lumen E = endothelial cells

9

22 Atherogenesis

221 Early Lesion Development

Atherogenesis is defined as the process leading to the initiation of the atherosclerotic lesion

the details of which are complex and not completely understood Regardless of the exact

mechanism however it is widely accepted that endothelial dysfunction is a first sub-clinical

indication of atherosclerotic lesion formation characterized by decreased bioavailability of

nitric oxide (NO) a potent vasodilator and important cell signaling molecule synthesized in

the endothelium by the enzyme endothelial NO synthase (eNOS) Beyond its vasodilatory

capacity NO also serves as an anti-inflammatory anti-platelet and anti-oxidant molecule

(Davignon and Ganz 2004) therefore any decrease in the bioavailability of NO increases the

propensity for inflammatory thrombotic and reactive oxygen species (ROS) activity

respectively all of which are considered to be proatherogenic

A likely source of initial endothelial dysfunction is lipid accumulation within the arterial wall

The role of low-density lipoprotein cholesterol (LDL) in atherogenesis particularly in its

oxidized form (oxLDL) has been recently scrutinized because this oxidized form is

associated with a more rapid progression of pathological changes early in atherosclerosis

(Steinberg 2009) and is known to inactivate NO directly (Kinlay Libby and Ganz 2001)

Under normal circumstances NO inhibits the oxidative modification of LDL through anti-

oxidant actions (Rubbo et al 2002) however high concentrations of plasma LDL promote

their accumulation in the arterial wall (Insull et al 2009) where unoxidized LDL molecules

may become oxidized or engulfed by macrophage cells and occasionally smooth muscle

cells residing within the intima (Matsuura Hughes and Khamashta 2008) Indeed

10

hyperlipidemia and hypercholesterolemia are independent risk factors for the development of

clinically-overt atherosclerosis (Austin 1989 Bozkurt et al 2007) However in addition to

simple inactivation of NO by the presence of oxLDL more complex mechanisms also exist

through which disruption of endothelial NO production occurs indirectly

222 Progression to Fatty Streak

Uptake of oxLDL by macrophages within the intima may promote the release of chemo-

attractant molecules that promote the migration of circulating monocytes across the

endothelium and their subsequent differentiation into macrophages (Volkman 1970) Upon

differentiation additional receptors are expressed on the macrophage cell surface that

accelerate the uptake of LDL and particularly oxLDL which promotes subsequent cell

loading of lipid and cholesterol esters (Matsuura Hughes and Khamashta 2008) This

monocytemacrophage differentiation process may eventually result in isolated lipid-loaded

foam cells and microscopic lipid droplets characteristic of early atherosclerotic lesions

Other immune cells such as T-lymphocytes may also become involved at advanced stages

but in lesser numbers (Matsuura Hughes and Khamashta 2008) As the process of

differentiation and lipid-loading continues the accumulation of foam cells causes the

formation of more confluent lipid droplet collections These may become apparent upon

pathological examination as a visible lesion on the arterial intimal surface termed a fatty

streak (Stary et al 1994)

223 Smooth Muscle Proliferation and Phenotypic Switching

A consequence of arterial wall lipid accumulation is upregulation of caveolin-1 production a

protein that inactivates the enzyme eNOS (Kinlay Libby and Ganz 2001) thereby decreasing

11

NO bioavailability and endogenous anti-oxidant supplies in general Unoxidized LDL

(Davignon and Ganz 2004) and intermediates in the cholesterol synthesis pathway (Jantzen et

al 2007) have also been found to inhibit eNOS which act to further decrease NO

bioavailability As a result of these processes the local balance of pro- and anti-oxidant

molecules becomes increasingly disrupted in some cases leading to the accumulation of pro-

oxidant vasoconstrictors angiotensin II (AT2) and endothelin (ET) (Davignon and Ganz

2004) AT2 and ET are known SMC trophic factors and act upon SMCs residing in both the

arterial intima and media (Doran Meller and McNamara 2008) Intimal SMCs near the lumen

appear to be particularly susceptible to the effects of these factors exhibiting both increased

production of extracellular matrix and collagen as well as increased LDL receptor expression

an effect termed phenotypic switching (Doran Meller and McNamara 2008) Susceptibility

to phenotypic switching may then lead to eccentric or diffuse intimal thickening and uptake of

lipid by the SMCs themselves (Stary et al 1994) Large numbers of intimal SMCs within

early lesions are thought to signify those prone to further progression by lipid uptake while

conversely lesions with fewer SMCs appear less prone to progression (Stary et al 1994)

224 Role of Hemodynamics

Sites of intimal thickening are known to occur in otherwise healthy arteries at points of high

hemodynamic stress In these cases hemodynamic stress refers to the normal stress applied

by the blood on the artery wall acting perpendicular to its surface The sites of highest

hemodynamic stress within the body include the abdominal aorta dorsal wall coronary

arteries and carotid bifurcation which are also the most common sites of atherosclerosis later

in life tending to develop most quickly into advanced lesions (Stary et al 1992) This has

12

stimulated debate over whether this ldquonaturalrdquo intimal thickening constitutes pathology (see

ldquoRelation Between Adaptive Intimal Thickening and Atherosclerosisrdquo in Stary et al 1992)

Given that arterial wall remodeling (thickening) occurs as a natural response to increased wall

stress (as governed by Laplacersquos Law) intimal thickening observed at these locations might

not represent pathology in all cases and instead may result from adaptation to focal

hemodynamic stress An additional possibility is that focal hemodynamic stressors increase

the rate of LDL deposition within the arterial wall thus accelerating the process of

atherogenesis in the same manner as discussed above

13

Figure 22 Depiction of blood flow through the carotid bifurcation during systole and

diastole High flows during systole ensure laminar flow past the carotid bulb (left panel)

however flows during diastole may become turbulent at the carotid bulb (right panel)

resulting in oscillatory shear stress that acts on the endothelium at this point ECA = external

carotid artery ICA = internal carotid artery CCA = common carotid artery

14

Sheer stress defined here as the strain force applied parallel to the endothelium by flowing

blood is required for the normal functioning of the endothelium and may also play a role in

the progression of atherosclerosis Studies performed in vitro indicate that bulk flow over

endothelial cell monolayers is a stimulus for their proper orientation relative to the direction

of flow (Remuzzi et al 1984) Furthermore shear stress may increase eNOS activity and may

thereby aid in the prevention of endothelial dysfunction while its absence may result in

disorganization increased proliferation of endothelial cells and subsequent endothelial

dysfunction (Boo et al 2002) Considering that sympathetic activation during exercise

increases heart rate cardiac output and therefore endothelial sheer stress this mechanism

may provide insight into a recent study that reported evidence of a negative association

between exercise capacity and severity of atherosclerotic disease (Mohlenkamp et al 2009)

However it is prudent to note that the degree of sheer stress sensed by the endothelium

appears to be more accurately reflected by the average stress measured throughout the cardiac

cycle rather than its peak magnitude during systole This has been verified by studies

performed in vitro (de Keulenaer et al 1998) and may be of particular importance at sites of

turbulent flow since the absolute sheer stress at these locations is likely to change throughout

the cardiac cycle For example at the carotid bifurcation flow patterns are especially

complex and laminar flow is not necessarily preserved throughout the cardiac cycle (Steinman

and Rutt 1998) This is especially well-demonstrated within the carotid sinus where periodic

flow reversal occurs during diastole due to enlargement of the lumen at this site (Steinman

and Rutt 1998 Figure 22) Therefore in vessels that are susceptible to periodic oscillations

in the direction of blood flow the resultant decrease in average sheer stress may represent an

15

appropriate risk factor for precipitating endothelial dysfunction and subsequent atherogenesis

Indeed a positive correlation between the location of oscillatory shear stress and the location

of atherosclerotic plaque has been demonstrated at the carotid bifurcation (Ku et al 1985)

23 Characterization of Atherosclerosis

231 American Heart Association Classification

In a series of three papers from 1992 to 1995 the American Heart Association (AHA)

Committee on Vascular Lesions classified the progression of atherosclerosis into a series of

six stages (types I-VI) based both on gross morphological appearance and histological

organization (Stary et al 1992 Stary et al 1994 Stary et al 1995) More recently this

classification scheme was revised and clarified in consultation with the Committee to define

eight stages of lesion progression (types I-VIII) without the requirement for sub-type

classification (Stary 2000) Subsequent sections of this thesis will employ these more

recently modified AHA criteria

2311 Early Lesions

Concerning lesion formation the Committee defines type I and II lesions as the initiation and

development of early atherosclerosis respectively (Stary et al 1994) linked to the advanced

stages through the intermediate type III lesion Specifically type I lesions are defined by the

isolated microscopic accumulation of macrophage-derived foam cells while type II are

defined by layers of lipid-loaded cells that include foam cells derived from SMCs (Stary et al

1992) These early types represent sub-clinical stages of atherosclerosis that are clinically

silent and are present within a large proportion of the population Indeed lesion types I-II are

common in children and adolescents and have been identified as early as infancy (Stary

16

1987) underscoring that atherosclerosis is a life-long process Type III lesions are

characterized by increased intra- and extracellular accumulation of lipid and cholesterol esters

in layers within the tunica intima and media causing separation of adjacent layers of smooth

muscle cells but not yet characterizing the confluent lipid core observed in later stages (Stary

et al 1992)

2312 Advanced Lesions

In contrast to early lesion types which are always clinically silent (Stary et al 1992)

advanced lesions (types IV-VIII) may be clinically silent or overt characterized as such by

their potential to precipitate ischemic events An additional distinction between the early and

late lesions as characterized by the modified AHA criteria is that regression of atherosclerotic

features is possible in the early lesions (Stary 2000)

The AHA type IV lesion is defined by the first appearance of a confluent extracellular

macroscopic accumulation of lipid and cholesterol esters termed the lipid core formed by the

apoptosis of lipid-loaded macrophage and SMC foam cells (Stary et al 1994) Subsequent

progression of lipid accumulation and hemorrhage of immature neovessels within the intima

leads to the accumulation of a fibrous covering that is termed the fibrous cap characterizing

the type V lesion (Stary 2000) Progressive accumulation of extracellular matrix exacerbated

by phenotypic switching of SMCs within the intima may also contribute to accumulation of

the fibrous cap proteins (Doran Meller and McNamara 2008) Type V lesions are also

associated with progression of stenosis Outward expansion of the arterial wall (positive

remodeling) is associated with lesions occupying less than 40 of the area of the internal

elastic lamina beyond this however luminal encroachment is significantly correlated with

17

the size of internal elastic lamina area occupied by the lesion (Glagov et al 1987) This

appears to occur often during stage V (Stary 2000) Type VI lesions are the most likely to

cause clinical events and are characterized by fissuring of the fibrous cap and intraplaque

hemorrhage (IPH Stary 2000) which both may lead to plaque progression and clinical

symptoms Exposure of the thrombogenic lipid core to the blood as occurs during fissuring

of the fibrous cap may precipitate the formation of thrombus local to the plaque site or

emboli that become detached from the plaque and travel deeper into the arterial circulation to

cause events distally Subsequent to this stage AHA types VII and VIII have been shown to

predominate at sites at which regression of the lipid core has been achieved therefore these

stages may be representative of plaques that have been previously active (Stary 2000) In

particular both calcification and significant fibromuscular changes are thought to be the

primary indication of ldquomaturerdquo atherosclerotic lesions that characterize the AHA type VII and

type VIII plaques respectively (Stary 2000) It should be noted that while the AHA

classification is generally linear with respect to lesion severity for the early lesion types

advanced lesions may progress and regress while skipping intermediate stages

232 The Vulnerable Plaque

Characterization of atherosclerotic plaques as vulnerable is made in relation to their

propensity for the precipitation of clinical events These are in contrast to so-called stable

plaques that are unlikely to cause symptoms In general plaque vulnerability is thought to

correlate with plaque features that increase the likelihood of thromboemboli formation

particularly fibrous cap rupture large lipid core or significant intraplaque rupture of

neovessels causing hemorrhage (Makris et al 2010) The previous classification scheme

18

developed by the AHA for characterization of atherosclerotic plaques was developed based on

gross morphological and histological studies conducted during autopsies and on plaque

specimens removed via surgery (Stary et al 1994 Stary et al 1995) More recent methods

for estimation of plaque vulnerability are detailed in sections 27 and 28 and their respective

subsections

24 Stroke

241 Burden of Stroke

Stroke is defined as symptoms arising from the interruption of blood flow to the brain

whether global or focal lasting greater than 24 hours In Canada stroke is the third-leading

cause of mortality accounting for 50000 hospitalizations and 14000 deaths and costing the

Canadian economy $27 billion in lost productivity and healthcare expenses each year (HSFC

2006 HSFC 2011b)

242 Types of Stroke

Of all strokes approximately 87 result from global or focal ischemia 10 result from

intracerebral hemorrhage and 3 result from subarachnoid hemorrhage (HSFC 2006) Of

these carotid atherosclerosis is a major cause of ischemic stroke and is therefore of particular

importance due to both the preventable and treatable nature of atherosclerosis and the

potential for loss of life and neurological function that stroke represents

25 Angiographic Assessment of Atherosclerosis

In 1958 American cardiologist F Mason Sones Jr accidentally injected contrast dye into the

right coronary artery of a patient leading him to realize the potential of his mistake for

19

visualization of atherosclerotic disease within the vasculature (Hurst Conti and Fye 2003)

Since this event angiography has become a clinical standard for the diagnosis of

cardiovascular disease relying upon measurement of luminal stenosis caused by the presence

of atherosclerotic plaque within the vessel wall Central to the evaluation of angiographic

images is a general understanding that the degree of stenosis is related to its propensity to

precipitate an ischemic event In the evaluation of the carotid arteries angiography has

therefore found a niche as the current clinical standard-of-practice for the prediction of stroke

risk

251 Clinical Trials

Several clinical trials of symptomatic and asymptomatic carotid atherosclerosis have helped to

quantitatively define stroke risk in relation to angiographic data (ACAS Collaborators 1989

NASCET Collaborators 1991a ECST Collaborators 1991 Mayberg et al 1991 CASANOVA

Collaborators 1991 Hobson et al 1993) The first published findings of a large randomized

multi-center trial to demonstrate a correlation between carotid stenosis and stroke risk were

from the North American symptomatic carotid endarterectomy trial (NASCET) begun in

1987 to examine the relationship between carotid stenosis and patient outcome following

surgical resection of symptomatic carotid atherosclerotic plaque by endarterectomy (NASCET

Collaborators 1987) In that trial investigators employed stringent angiographic criteria to

assess preoperative carotid stenoses of 30ndash99 using computed tomographic angiography

(CTA) whereby stenosis was calculated percentage-wise as the minimum linear carotid lumen

diameter divided by the post-stenotic healthy internal carotid artery lumen diameter

Compared to carotid atherosclerosis patients treated with best medical management two-year

20

follow up of NASCET patients randomized to carotid endarterectomy (CEA) found that in

those with high-grade carotid stenosis (70ndash99) CEA significantly reduced the occurrence of

major stroke (number needed to treat NNT=8) (NASCET Collaborators 1991b) However in

those patients with moderate (50ndash69 NNT=20) or mild (30ndash49 NNT=48) stenosis five-

year post-surgical follow-up demonstrated that CEA provided little to no benefit to these

patients (Barnett et al 1998)

In the European carotid surgery trial (ECST) the largest clinical trial to examine the benefit of

CEA in symptomatic patients (Moneta and Masser 1994) stenosis was defined as residual

carotid lumen diameter divided by the estimated lumen diameter at the same site in the

absence of atherosclerotic disease (ECST Collaborators 1991) Despite differing methods for

measurement of stenosis trial design and results were similar to the NASCET study surgical

treatment was found to significantly reduce risk of major stroke in patients with symptomatic

high-grade carotid stenosis randomized to CEA In contrast to the NASCET study ECST also

included those patients with lt30 stenosis though CEA was not found to offer significant

benefit in this group

The results of the asymptomatic carotid atherosclerosis study (ACAS) the largest clinical trial

conducted in asymptomatic patients (Moneta and Masser 1994) provided evidence that CEA

is also beneficial in patients with asymptomatic high-grade carotid atherosclerosis (defined as

60ndash99 stenosis) In that trial CEA was found to provide a 53 relative risk reduction for

major stroke (95 confidence interval 22ndash72) compared to patients managed with best

medical therapy (Mast et al 1996)

21

252 Trial Impacts and Limitations

The criterion of high-grade carotid stenosis gt70 established by the NASCET and ECST

studies remains the principle indication for CEA in symptomatic and to a lesser extent

asymptomatic patients (NASCET Collaborators 1991b ECST Collaborators 1991) For this

reason angiography is now routinely performed in conjunction with clinical assessment to

evaluate the potential benefit of CEA in patients with carotid atherosclerotic plaque

However evaluation of stroke risk by angiography alone is limited by two factors First

angiography does not provide visualization of the entire plaque structure and in this respect

the three most commonly employed techniques for acquiring angiographic data each suffer

from respective limitations conventional x-ray angiography visualizes only the vessel lumen

and is therefore only sensitive for the detection of atherosclerotic plaques imposing high

degree of stenosis CTA provides poor soft-tissue contrast for delineation of plaque sub-

structures that correlate with plaque vulnerability and ultrasonography suffers from high

receiver operator variability and limited penetration deep to sites of plaque calcification

Second the evaluation of carotid stenosis by NASCET or ECST criteria underestimates

plaque burden because of compensatory mechanisms that exist within the vessel wall to

preserve vessel patency despite moderate plaque growth (Glagov et al 1987) Furthermore

because plaque volume and percent stenosis do not correlate within carotid plaques (de

Labroille et al 2009) evaluation of stroke risk may be improved by the assessment of

additional criteria derived from carotid wall imaging performed in addition to or in lieu of

angiography This hypothesis derives from mounting evidence that carotid plaque

composition is associated with plaque vulnerability and subsequent ischemic stroke (Falk

22

1992 Bassiouny et al 1997) Therefore patients previously classified as low- to moderate-

risk for ischemic stroke by angiographic criteria set out by the widely-regarded NASCET or

ECST studies may be at higher risk than previously thought (Price Gardin and Savage 1992)

26 Magnetic Resonance Imaging

Formerly known as nuclear magnetic resonance (NMR) magnetic resonance imaging (MRI)

relies on the atomic property of quantum spin Conventional MRI utilizes the hydrogen 1H an

atom that possesses two non-zero nuclear spin states each of which are characterized by a

local magnetic dipole moment that is influenced by the presence of an external magnetic field

In the case of 1H two alignments are possible that reflect the two possible spin states one

parallel and one anti-parallel to the applied magnetic field The parallel alignment state

possesses less energy causing this more thermodynamically favourable state to predominate

at equilibrium In this state the majority of 1H spins are aligned parallel to the applied

magnetic field thus the net longitudinal magnetization vector (MZ) that represents the sum of

the individual proton states also lies in this direction

Application of radio-frequency (RF) energy equivalent to the energy difference between 1H

spin states causes the majority of spins to align anti-parallel to the magnetic field thus

inverting MZ Following removal of RF energy recovery of MZ toward equilibrium is

characterized by an exponential recovery the half-life of which is termed the spin-lattice

relaxation time T1

The precession of 1H spins may also become aligned in response to the application of RF

energy Similar to the recovery of MZ the loss of phase coherence between proton spins also

23

occurs following the removal of RF energy however this loss is characterized by an

exponential decay the half-life of which is termed the spin-spin relaxation time T2 and is

generally far shorter than the corresponding T1

Due to changes in T1 and T2 between tissues that are dependent on the local magnetic

environment careful timing of the collection of RF energy emitted by 1H protons during their

return to equilibrium allows for image contrast-weighting to be based predominately on

differences in T2 (T2-weighted) T1 (T1-weighted) or proton density (PD-weighted) A

review of spatial encoding and image processing is beyond the scope of this thesis For

further information the reader is directed to an MRI textbook dealing with these topics for

example Huettel Song and McCarthy (2004)

261 Blood Signal Suppression Techniques

Black-blood imaging refers to the suppression of MRI signal from blood flowing into the

imaging volume The inflow of blood into the imaging plane with MZ near its equilibrium

value results in hyperintense signal from the vessel lumen on T1-weighted images which may

confound the interpretation of clinically significant pathologies present within the artery wall

Interpretation may be further confounded by pulsatile flow artifacts that limit the certainty

with which the artery wall can be distinguished however this may be sufficiently overcome

through the combination of cardiac gating and blood suppression (Steinman and Rutt 1998)

Efficient blood suppression has been demonstrated to improve visualization and

reproducibility in the evaluation of carotid plaque (Dong et al 2010) To achieve this one of

two methods is commonly employed Spatial presaturation applied outside the imaging

volume induces a rapid steady-state signal reduction in moving blood prior to its entry into the

24

imaging volume such that its signal is much less than that of the stationary tissue (Brown and

Smelka 2010) In contrast blood suppression may also be achieved via the double-inversion

recovery (DIR) technique which consists of a region-wide 180ordm inversion pulse to invert MZ

of the whole tissue followed immediately by a slice-selective 180ordm inversion pulse to re-invert

MZ in the tissue of interest such that the net change in MZ of the tissue of interest is zero

(Redpath and Smith 1994) It is important to note that the region-wide 180ordm inversion pulse

inverts the MZ of all blood upstream of the imaging thus enabling suppression of inflowing

blood signal from any point outside the imaging volume Since the rate at which MZ recovers

is dependent on T1 the time during recovery at which MZ equals zero can be determined if

the blood T1 is known therefore suppression of inflowing blood can be achieved by proper

timing of the acquisition following the initial inversion pulse This interval is termed the

inversion time TI Steinman and Rutt (1998) demonstrated that DIR is generally superior to

spatial presaturation for blood signal nulling at the carotid bifurcation due to the complex

nature of flow in this region For this reason DIR is most often employed for blood

suppression during MRI of the carotid artery although its combination with spatial

presaturation is also common More advanced IR techniques have also been developed for

specific application to carotid vessel wall imaging such as quadruple IR for simultaneous

DIR in two overlapping planes (Yarnykh and Yuan 2002 Yarnykh and Yuan 2006) however

these techniques are complex and not commonly employed The application of IR techniques

for blood suppression continues to be limited in cases of slow and recirculating flow which

may occur at the carotid bifurcation

25

27 Non-Contrast Enhanced Methods for MRI of Atherosclerosis

In comparison to other imaging modalities the ability of MRI to non-invasively and

reproducibly discriminate atherosclerotic plaque components gives it significant value for

estimation of plaque vulnerability (Clarke et al 2003) Typically characterization of plaque

components is best aided by the review of multiple MRI contrast weightings of sufficiently

high in-plane spatial resolution (lt1 mm) which enables discrimination of plaque components

based on their differing relative signal intensities on T1- T2- or PD-weighted images (Fayad

and Fuster 2000) This is also applied in conjunction with DIR techniques to achieve proper

delineation of the vessel wall Individual characterization of lipid core fibrous cap

intraplaque hemorrhage calcification and looseextracellular matrix is possible with this

approach (Li et al 2010) although more general classification schemes may allow for better

characterization of plaque components Ronen et al (2007) found that plaque components

with similar compositions could be identified with greater certainty if grouped together for

example the authors found greater certainty for the identification of fibrous cap and

looseextracellular matrix together than for each plaque component alone Table 21 provides

a summary of the relative MRI signal intensities of plaque components on multiple contrast

weightings with respect to the signal intensity of sternocleidomastoid muscle

26

Plaque component T1-weighted T2-weighted PD-weighted

Fibrous cap Hyperintense Hyperintense Hyperintense

Lipid core Hyperintense Hypointense Hyperintense

Calcification Hypointense Hypointense Hypointense

Thrombus Hyperintense Hyperisointense Hyperintense

Intraplaque hemorrhage (acute) Hyperintense Hyperisointense Hyperisointense

Intraplaque hemorrhage (recent) Hyperintense Hyperintense Hyperintense

Intraplaque hemorrhage (chronic) Hypointense Hypointense Hypointense

Acute = lt1 week old recent = 1-6 weeks old chronic = gt6 weeks old

Fayad and Fuster (2000) Moody et al (2003) Wang et al (2010)

Chu et al (2004)

Adapted from Fayad and Fuster (2000)

Table 21 MRI signal intensity of plaque components relative to sternocleidomastoid muscle

Several MR imaging features of plaque composition are known to correlate with plaque

vulnerability Lipid core size and fibrous cap thickness are positively and negatively

associated with risk of plaque rupture respectively (Fernandez-Ortiz et al 1994) presumably

due to the thrombogenicity of necrotic lipid pool elements and the structural instability of the

thinned fibrous cap In asymptomatic carotid atherosclerosis lipid core size has been shown

to be the strongest predictor of future plaque surface disruption (Underhill et al 2010) a

potential trigger for thromboemboli formation Additionally investigations into fibrous cap

thinning have revealed that matrix metalloproteinases (MMPs) play a key role in this process

by contributing to the degradation of fibrous tissue and have subsequently been demonstrated

as a suitable target for MRI molecular imaging in animal models (Lancelot et al 2008)

Further studies in animals have revealed that this molecular imaging technique may be

sufficiently sensitive to provide indications of plaque vulnerability in the future (Hyafil et al

2010)

27

A further correlate of plaque vulnerability is the presence of intraplaque hemorrhage (IPH)

thought to be a key event leading to the progression and eventual rupture arising from the

rupture of immature neovessels in the necrotic regions of the plaque (Kolodgie et al 2003

Vermani et al 2005) Repetitive IPH is thought to contribute more significantly to

progression of plaque vulnerability than single events (Wang et al 2010) and multi-contrast

MRI methods have been developed to classify IPH by time (acute = lt1 week recent = 1-6

weeks old = gt6 weeks) since the event (Chu et al 2004) Detection of IPH is also afforded

using 3D coronal T1-weighted MRI which offers high sensitivity specificity intra- and

interobserver agreement due to the short T1 of methemoglobin blood product that

accumulates in the sub-acute phase (Moody 2003 Moody et al 2003) In patients undergoing

carotid endarterectomy positive detection of IPH by this technique is associated with

intraoperative distal embolization (Altaf et al 2007) indicating that IPH is indeed correlated

with plaque rupture risk An additional advantage of the technique employed by Moody et al

(2003) is that their technique is rarely confounded by the presence of plaque calcification

which may also appear hyperintense on some MRI pulse sequences (Bitar et al 2010)

Whole-plaque characteristics are also known to correlate with plaque rupture risk

Phinikaridou et al (2010a) demonstrated that positive wall remodeling of atherosclerotic

vessels defined as artery wall remodeling during plaque progression that does not encroach

upon the lumen is more frequently associated with vulnerable plaque This finding

underscores the limitation of current stroke risk assessment paradigms which use

angiographic estimates of luminal stenosis as the sole criterion for risk stratification

28

28 Use of Contrast Agents for MRI of Atherosclerosis

MRI contrast agents are commonly administered by intravenous injection in cases where

additional contrast between tissues is desired and can provide additional information

regarding pathology Clinical agents are gadolinium-based providing additional contrast by

increasing the T1- and T2-relaxivity of their local environment in proportion to their

concentration (Pintaske et al 2006) Clinical MRI contrast agents are also assumed not to

cross the cellular membrane existing solely within the extracellular space Contrast

enhancement within the tissues is therefore dependent on the concentration of the agent within

two tissue compartments the intravascular space (blood plasma) and the extracellular

extravascular space (EES) the relative contributions of which to any MRI tissue voxel are

inseparable without the use of dynamic imaging and subsequent mathematical modeling (see

2922) Because contrast agents are only administered into the intravascular space their

accumulation in the EES is therefore dependent on both the local tissue permeability that

facilitates their passage across the endothelium (termed flow) and the vascular surface area

and multiplication of these factors yields the bulk volume transfer rate from the intravascular

space to the EES (Tofts 1997) Therefore the degree of tissue enhancement following

contrast injection provides information useful for estimating the degree of vascularity and the

permeability of tissues

281 Contrast-Enhanced MRI

An initial application of gadolinium-based MRI contrast agents for in vivo morphological

characterization of atherosclerotic plaque in humans was by Wasserman et al (2002) who

demonstrated with histological validation that lipid core and fibrous cap identification on T2-

29

weighted images is significantly improved by the administration of contrast presumably due

to differences in vascularity and permeability between these two components Previous

studies in humans that had established associations between lipid core size fibrous cap

thinness and plaque vulnerability were performed on endarterectomy specimens (Fernandez-

Ortiz et al 1994 Carr et al 1996) thus by demonstrating that fibrous cap thickness

measurements are aided by contrast administration Wasserman et al (2002) provided the first

evidence that non-invasive estimation of plaque vulnerability with MRI is possible in vivo In

subsequent research using a larger study population these findings were substantiated by

Kramer et al (2004) who additionally determined that detection of thrombus is also aided by

contrast-enhanced T2-weighted MRI

Plaque inflammation and neovessel proliferation have also been investigated with the use of

contrast agents Yuan et al (2002) demonstrated that areas of strongest contrast enhancement

on T1-weighted MRI of the carotid arteries corresponded to areas of neovasculature Indeed

the neovessel density arising from the vasa vasorum is especially pronounced at the carotid

bifurcation and is thought to be due to the high nutritional requirements of mechano- and

chemoreceptor cells residing within the carotid sinus (Williams and Heistad 1996) The

presence of this well-developed blood supply may in part account for the particular

susceptibility of the carotid arteries to the development of atherosclerosis because the rate of

monocytemacrophage recruitment during pro-inflammatory events is likely to be increased in

the carotid arteries in comparison to less vascularized tissue In paradoxical manner the

inflammatory infiltrate characteristic of atherosclerosis stimulates further angiogenesis

through the release of VEGF by macrophages (Inoue et al 1998) The inflammatory state of

30

the plaque is also augmented by the release of VEGF because endothelial permeability to

circulating monocytes is increased in response to VEGF receptor activation (Bates 2010)

Since vascular permeability to contrast agents is also likely to be increased in this state and

since neovascular density is increased the identification of sites of active inflammation and

neovessel growth has become possible with the use of MRI contrast agents Using

histological validation of MR imaging Sirol et al (2009) demonstrated that increased

macrophage accumulation and neovessel density are associated with more advanced

atherosclerotic plaques in rabbits and that these areas were indeed associated with increased

uptake of gadolinium-based contrast agent Thus contrast-enhanced MRI of the

atherosclerotic plaque may provide additional information useful for estimating plaque

vulnerability However since the acquisition of high resolution images at multiple locations

is time consuming neither extraction of the signal intensity time course nor the quantitative

evaluation of plaque enhancement is possible with conventional contrast-enhanced methods

alone

282 Dynamic Contrast-Enhanced MRI

Dynamic contrast-enhanced MRI (DCE-MRI) refers to rapid serial imaging of a tissue for the

specific purpose of examining voxel-wise signal intensity dynamics before during and after

the administration of a diffusible MRI contrast agent Tofts and Kermode (1991) were among

the first to outline the theory and application of this technique for the study of blood-brain-

barrier breakdown in multiple sclerosis The authors cited the limited usefulness of ldquobinaryrdquo

(presence versus absence) qualitative evaluation of enhancement as a motivation for the

development of their technique This process instead enables quantitative measurement of

31

physiologically relevant parameters that are independent of the method of acquisition Since

this initial work the ability of DCE-MRI to differentiate between benign and malignant

tissues due to differences in vascularity and permeability has found widespread application in

clinical oncology and cancer research (Yankeelov and Gore 2009) However application to

atherosclerosis has thus far been limited

2821 General Theory

The acquisition and analysis of DCE-MRI data aims to extrapolate information regarding the

tissue and its microvasculature from images of limited temporal and spatial resolution

quantitative (in that the derived parameters are reproducible and are representative of true

physiology) Data analysis is guided by one of two general approaches termed the model-

based and nonmodel-based or quantitative and semi-quantitative approaches respectively

each with specific advantages and disadvantages In the model-based approach mathematical

modeling is employed such that MRI signal intensity is used to determine the contrast agent

concentration time course within the tissue thus allowing for derivation of several

physiologically-relevant parameters that each independently relate to vascular permeability

interstitial space and plasma volume In contrast the nonmodel-based approach does not

attempt to calibrate signal intensity to contrast agent concentration and instead

measurements are taken with respect to the raw signal intensity time course or some

normalized variation thereof The physiological relevance of the parameters calculated by

this approach is therefore less apparent however nonmodel-based approaches are

substantially less mathematically and computationally intensive and rely upon fewer

assumptions

32

2822 Model-Based Analysis

Tofts and Kermode (1991) based their analysis model upon the assumption that following the

administration of an MRI contrast agent the signal intensity time course of any one voxel is

related to the distribution of contrast agent within four body compartments which are the 1)

blood plasma 2) whole-body EES 3) kidneys and 4) abnormal tissue of interest termed by

the authors as the ldquolesion leakage spacerdquo (Figure 23) This model also assumes that the

contrast material is injected as a bolus into the blood plasma compartment and is well-mixed

immediately following injection Plasma concentration is therefore highest at the moment of

injection decreasing thereafter in a fashion that is characterized by a biexponential decay

function The initial decrease in plasma concentration is attributed to equilibration of contrast

material between the plasma and the whole-body EES followed by a more shallow decrease

that is attributed to renal excretion when fitted to the biexponential function each is

characterized by the time constants τ1 = 67 minutes and τ2 = 90 minutes respectively (Tofts

and Kermode 1991 Wienmann Laniado and Mutzel 1984) The impact of contrast flux

between the blood plasma and the lesion leakage space is considered to have negligible

impact on the plasma concentration curve (Tofts and Kermode 1991) Consideration of the

relative time scales of τ1 and τ2 derived by Wienmann Laniado and Mutzel (1984) reveals

that the initial decrease in plasma concentration characterized by τ1 and hence a substantial

portion of the extravasation of contrast material into the tissues of interest occurs within a

time scale permissible for MRI scanning (5ndash10 minutes) Thus if the investigator is

concerned only with tissue enhancement then only the time shortly following contrast

injection (lt10 minutes) need be considered a contention that is also important in nonmodel-

33

based analyses This assumption is common in model-based analyses but is valid if and only

if no reflux of contrast occurs from the lesion space into the blood plasma and only while the

plasma contrast agent concentration far exceeds its concentration in the EES during imaging

(Patlak Blasberg and Fenstermacher 1983)

34

Figure 23 Tofts and Kermode (1991) four-compartment model representing the distribution

of a diffusible extracellular contrast material within the body A bolus injection of contrast is

assumed to be well-mixed within the blood plasma compartment immediately following

injection (τ0) The time course of contrast distribution within the whole-body interstitial space

is determined by the rate constant τ1 and excretion of contrast from the body by the kidneys is

determined by the rate constant τ2 The rate of leakage of contrast into the abnormal lesion

leakage space is governed by an unknown rate constant EES = extravascular extracellular

space

35

Determination of contrast agent concentration from raw MRI signal intensity relies upon the

existence of a linear relationship between relaxivity rate and gadolinium concentration

Evidence demonstrates the validity of this relationship for commonly used contrast agents up

to concentrations of 10 mmolL (Pintaske et al 2006) Calibration of relaxivity to

gadolinium contrast agent concentration is given by the equation

[21]

where T1 is the spin-lattice relaxation time of the tissue of interest following injection T10 is

the native spin-lattice relaxation time of the tissue of interest prior to contrast arrival α1 is the

longitudinal (T1) relaxivity of the contrast agent in units Lmmol-1

seconds-1

and Ctissue is the

contrast agent concentration of interest As suggested by equation [21] the tissue T1 values

before and after DCE-MRI must be known which requires that T1-mapping be performed

before and after dynamic imaging

To simplify the analysis of DCE-MRI data with use of these assumptions a two compartment

model is often employed that considers only the contrast flux between the blood plasma

compartment and the abnormal tissue of interest (Brix et al 2004 Figure 24) According to

this model the contrast agent concentration within each time series voxel of the abnormal

tissue is governed by three factors 1) the bulk volume transfer rate ktrans

which describes the

rate of contrast extravasation from the blood plasma into the EES with units minutes-1

2) the

fractional volume of EES contained within each voxel ve a unit-less parameter where 0 le ve

le 1 and 3) the fractional volume of blood plasma contained within each voxel vp a unit-less

36

parameter where 0 le vp le 1 (Tofts et al 1999) The relation between these parameters is

defined by the extended Tofts model given by the equation

[22]

where Ctissue(t) is the time course of the contrast agent concentration within the tissue

Cplasma(t) is the time course of the contrast agent concentration within the blood plasma of an

artery feeding the abnormal tissue of interest termed the arterial input function (AIF) and is

the convolution between the tissue extravasation term and the blood plasma AIF (Tofts et al

1999) The ldquoextensionrdquo refers to the addition of the blood plasma term to account for a non-

negligible fractional plasma volume the effect of which was not considered in the initial

model

Given that proper calibration of the gadolinium concentration time courses of blood plasma

and abnormal tissue has been performed using equation [21] the physiological parameters

ktrans

ve and vp can then be determined by fitting the two compartment model to the acquired

MRI data on a voxel-wise basis To ensure proper estimation of Cplasma(t) careful selection of

the AIF must be made within a voxel or group of voxels known to contain only blood such

that vp = 1 In addition high temporal sampling is required to properly define the AIF Due

to the requirement for convolution of the tissue parameters with the plasma concentration time

course model-based analyses are generally restricted to the use of MRI pulse sequences that

permit bright-blood imaging since the application of any blood suppression technique would

abolish the AIF thereby rendering the model unusable Recent model-based techniques have

37

permitted the estimation of ktrans

ve and vp without the need for an AIF which use instead a

reference region of presumably-healthy tissue (usually muscle) to estimate the unknown

parameters (Yankeelov et al 2005) This has been further applied to circumvent the need for

contrast agent calibration and hence the need for T1-mapping (Walker-Samuel Leach and

Collins 2007) however the added mathematical complexity and potential measurement errors

introduced by these techniques make them particularly unsuitable for standardized clinical

application Furthermore unlike the generalized or extended Tofts models the usefulness of

reference region techniques has not been previously demonstrated for the evaluation of

atherosclerosis

38

Figure 24 Two-compartment model of contrast distribution within the lesion leakage space

of the tissue of interest (Brix et al 2004) Contrast molecules arrive at the tissue contained

within the blood plasma Contrast diffusion into the extravascular extracellular space is

governed by the bulk volume transfer rate ktrans

which is in turn governed by the product of

the vascular permeability of the capillary (dashed line) and the capillary surface area Reflux

of contrast from the extravascular extracellular space back into the blood plasma is governed

by the rate constant ksp Under short duration experiments this reflux may be assumed to be

negligible

39

2823 Nonmodel-Based Analysis

Nonmodel-based approaches refer to the analysis of DCE-MRI data without application of an

a priori mathematical model which offers several advantages The lack of a model enables

parameters to be extracted from the raw signal intensity time course without need for tissue

gadolinium concentration calibration T1 mapping or AIF estimation thereby substantially

reducing both the computational intensity and the expertise required for application of this

technique in comparison to model-based approaches In addition since the AIF need not be

measured nonmodel-based approaches may be used in conjunction with black-blood imaging

techniques that permit high contrast-to-noise ratio (CNR) between the vessel lumen and

arterial wall This technique offers particular advantages for imaging of atherosclerosis due to

reduction of partial volume contribution from blood plasma in the evaluation of juxtaluminal

artery wall voxels

Several nonmodel-based parameters are commonly used in the evaluation of DCE-MRI data

The most common of these are the area under the curve (AUC derived from integration of the

post-contrast signal intensity time course) the early enhancement slope late enhancement

slope peak enhancement and time to peak enhancement Although not previously applied to

the study of atherosclerosis the rate of contrast enhancement immediately following contrast

arrival is useful for nonmodel-based evaluation of cancer where this parameter was found to

exhibit positive correlation with both microvascular density and expression of VEGF in rectal

carcinoma (Zhang et al 2008) and the degree of angiogenesis in prostatic carcinoma (Ren et

al 2008) and was also able to differentiate prostatic carcinoma from benign tissue (Isebaert et

al 2011) Nonmodel-based DCE-MRI techniques are also of prognostic value in the

40

evaluation of human breast cancer (Tuncbilek et al 2011) The rate of signal intensity change

(whether positive or negative) after the early enhancement phase also varies with the degree

of neovasculature in the DCE-MRI evaluation of prostatic carcinomas and benign prostatic

hyperplasia such that the late enhancement rate may offer high enough sensitivity and

specificity for differential diagnosis of these entities (Ren et al 2008) The peak amplitude of

contrast enhancement correlates with both the neovessel count and the expression of VEGF

during DCE-MRI of rectal carcinomas (Zhang et al 2008) Lastly the time from contrast

arrival to peak enhancement termed the time to peak exhibits a negative correlation with

neovessel count and VEGF expression in carcinomas (Zhang et al 2008) and has shown

promise for differentiation of symptomatic and asymptomatic plaques in a rabbit model of

atherosclerosis (Phinikaridou et al 2010b) However beyond these more conventional

parameters a significant advantage of nonmodel-based analysis is that a wide variety of

parameters may be conceived and extracted from the data without need for prior analysis

Nonmodel-based analyses are also advantageous because they are free of biases characteristic

of a priori models Despite this advantage however only two DCE-MRI studies of

atherosclerosis have been conducted using the nonmodel-based approach presumably

because the parameters extracted from this approach have not as of yet been shown to

correlate with known physiological parameters In a previous comparison of model- and

nonmodel-based approaches in an oncological application the nonmodel-based parameter

AUC was shown to be intrinsically linked to all three quantitative parameters ve vp and ktrans

(Walker-Samuel Leach and Collins 2006) However in more recent work (Cheng et al

2009) modified calculations for AUC and initial enhancement derivation have demonstrated

41

strong correlations between simulated estimations of ktrans

and ve respectively suggesting that

nonmodel-based approaches may be more physiologically-relevant than previously thought

Indeed nonmodel-based analyses using combinations of early and late enhancement slope

and peak amplitude have shown to be sufficiently robust to provide differential diagnoses

between benign and malignant cancers in a variety of tissue types (Ren et al 2008 Zhang et

al 2008 Isebaert et al 2011) For these reasons the apparent perception that nonmodel-

based approaches are inferior to their quantitative counterparts because they do not represent

ldquotruerdquo physiology may simply be due to the method by which nonmodel-based parameters

were derived in previous studies

2824 Dynamic Imaging of Atherosclerosis

Despite broad application of DCE-MRI for oncological assessments (Leach et al 2003) its

application to atherosclerosis has been limited As of the writing of this thesis the literature

contains only 12 original contributions that have investigated atherosclerosis using DCE-MRI

in the context of humans or animals 8 of which were performed by the same collaborators

Aoki et al (1999) provided the first qualitative evidence of temporal enhancement

characteristics of the carotid artery wall using dynamic MRI (30-58 secondsphase) which

revealed differing signal intensity time courses between inner hypointense and outer

hyperintense rims within artery walls of normals and those affected by various pathologies

Outer rim enhancement distinct from that of the lumen was characterized by a slow rise

followed by a plateau (peak enhancement within 60-174 seconds post-injection in 71 of

patients) which the authors attributed to a ldquohypervascular adventitiardquo or in some cases highly

vascular atherosclerotic plaque thus reiterating the role for adventitial vasa vasorum and

42

neovasculature in carotid wall enhancement Conversely the inner rims of large

atherosclerotic plaques were often discontinuous or markedly thickened and demonstrated

slow enhancement which the authors suggested was at least partly attributable to low

vascularity within an extensive carotid intima A significant limitation noted by the authors

however was motion artifact caused by arterial pulsation and its potential confounding effect

on the interpretation of the nature of the observed inner-rim hypointensities The use of

cardiac gating was therefore recommended for future studies Indeed prospective digital

cardiac and respiratory gating during DCE-MRI of carotid atherosclerotic plaque has been

shown to improve resolution and decrease image artifacts in mice (Alsaid et al 2007)

An equally important confounder of dynamic imaging in the neck is inter-scan (between

separate imaging phases) artery motion originating from patient translational movement (rigid

motion) or artery movement with respect to other anatomical structures (non-rigid motion)

caused by patient breathing or swallowing These effects may be compounded by low SNR

that is generally characteristic of DCE-MRI scans thus introducing significant voxel-wise

variation in MRI signal intensity Kerwin Cai and Yuan (2002) applied noise filtering and

motion correction to DCE-MRI of the carotid arteries in humans using linear minimum mean

square estimates (LMMSE) and least squared differences between images respectively To

account for non-rigid inter-scan motion coregistration of images was performed only within a

small region of interest (ROI) centered on each carotid artery Using the combined

filteringcoregistration algorithm significant improvements in motion artifact reduction and

outer wall and lumen visibility were achieved

43

In a subsequent study of patients undergoing endarterectomy Kerwin et al (2003) were the

first to conduct model-based analysis of DCE-MRI of carotid atherosclerosis to reveal that

whole-plaque fractional plasma volume is significantly correlated with neovessel area as

determined by histological analysis of endarterectomy specimens In a more rigorous study

(Kerwin et al 2006) both vp and ktrans

were determined by application of the extended Tofts

model to dynamic imaging conducted in patients scheduled for CEA Histological analysis of

specimens revealed significant positive correlations of vp and ktrans

with macrophage

neovasculature and looseextracellular matrix content however the application of

multivariate regression analysis using neovasculature area as a covariate only yielded

significant correlation between ktrans

and macrophage content and abolished any correlation

between vp and other plaque components suggesting that the extent of neovessel growth and

macrophage infiltration play the greatest role in determining the rate of atherosclerotic plaque

enhancement A later study of the adventitial vasa vasorum in carotid plaque (Kerwin et al

2008) found significant independent correlations between ktrans

and neovasculature and

macrophage content clinical markers of inflammation and plaque rupture risk thus

substantiating the findings of their previous work (Kerwin et al 2006) while also

demonstrating that measurement of model-based parameters in the adventitia may also

provide an indication of risk The slow enhancement rate observed in the adventitia also

provides evidence that this was the structure observed within the hyperintense outer rims

described by Aoki et al (1999) In a recent and detailed model-based analysis (Chen et al

2010) derived values of vp and ktrans

were pooled among subjects according to carotid plaque

sub-component including looseextracellular matrix fibrous tissue intraplaque hemorrhage

44

lipid core and calcification Significant differences in average vp and ktrans

were found for all

permutations of these comparisons (except for hemorrhage and calcification which can be

easily distinguished based on T1-weighted signal intensity) indicating that model-based

DCE-MRI may provide sufficient sensitivity to distinguish atherosclerotic plaque sub-

components Most recently Dong et al (2011) demonstrated that ktrans

within carotid plaques

is significantly reduced following one year of aggressive lipid-lowering therapy in patients

with hyperlipidemia but that the reduction in ktrans

was not associated with a change in lipid

core size of the plaque itself These results therefore suggest that DCE-MRI is a more

sensitive metric for the assessment of response to therapy than conventional structural

imaging

As noted above model-based analysis of DCE-MRI data relies on a number of assumptions

that may introduce bias into the derived result and these include the choice of the applied

model Chen et al (2011) recently demonstrated that the applied model may significantly

influence estimations of vp and ktrans

in carotid atherosclerosis and proposed an extended

graphical model based upon the initial work of Patlak Blasberg and Fenstermacher (1983)

that affords increased noise tolerance and immunity against fit failures common to the more

conventional Tofts models A drawback of this work however is that it is likely to contribute

to the heterogeneity of methods already in use for data analysis thus decreasing the potential

for standardized comparisons across studies in the future Currently comparisons of model-

based parameters of atherosclerosis across studies are influenced not only by the choice of

model and its underlying assumptions but also by the choice of contrast agent (Kerwin et al

45

2009) although this contention is not limited to model-based approaches and is likely to also

play a role in nonmodel-based approaches

An additionally significant limitation of using model-based approaches specifically for

application to atherosclerosis is the requirement for bright-blood imaging techniques that

obscure the boundary between the vessel wall and lumen thus preventing accurate analysis of

the juxtaluminal wall In the studies performed by the University of Washington

collaborators whose work accounts for the majority of the model-based DCE-MRI

knowledge of carotid atherosclerosis analyses of the vessel wall have been restricted to areas

sufficiently distant from the lumen so as to prevent partial volume artifact from blood (Dong

et al 2011) To circumvent this limitation Calcagno et al (2008) conducted the first

nonmodel-based analysis of DCE-MRI data acquired in aortic atherosclerotic plaques of

rabbits In their study the authors found significant correlations between AUC measurements

taken at 2 and 7 minutes post-injection 18

F-fluorodeoxyglucose (18

F-FDG) uptake and

histological counts of neovessels within both the intima and the adventitia of the aorta

suggesting that AUC is a sensitive nonmodel-based parameter for detection of neovessel

density and therefore plaque risk in atherosclerotic plaques In a subsequent study Calcagno

et al (2010) demonstrated high inter- and intrascan reproducibility of their technique again in

aortic plaques of rabbits

However although these studies have demonstrated that nonmodel-based analyses is both

feasible and fruitful in the study of aortic plaque in an animal model no study has yet applied

nonmodel-based analysis to the study of carotid atherosclerotic plaque in humans nor has any

46

study compared DCE-MRI parameters nonmodel-based or otherwise between groups of

subjects with carotid atherosclerotic plaque classified as being at high or low risk for

precipitation of cerebral ischemic events

47

Chapter 3 Study Aims and Hypothesis

31 Study Aims

While previous model- and nonmodel-based parameters in atherosclerosis have been shown to

correlate with features of plaque vulnerability these studies have required histological

validation of imaging findings and have therefore focused only on symptomatic patients

undergoing carotid endarterectomy Though this is an excellent population for validation

studies the population of patients with asymptomatic carotid atherosclerosis remains

unevaluated with respect to DCE-MRI approaches Due to this limitation of the current

literature direct comparison of patients with and without symptoms is not available

To address this limitation the current study was designed to provide the first comparison of

DCE-MRI parameters between subjects with high and low risk carotid atherosclerosis and

therefore of those patients at high and low risk for precipitation of ischemic stroke

respectively Therefore a main aim of this study was to evaluate the viability of this

technique for use as a clinical tool for stroke risk assessment To evaluate the concordance

between the commonly accepted standard-of-practice criteria for stroke risk assessment as

defined by the endarterectomy trials of the early 1990s and the more recently evolved method

of determining plaque vulnerability through imaging two sets of criteria were developed to

categorize carotid arteries as high or low risk for precipitation of ischemic events defined as

the clinical criteria and the imaging criteria Comparisons among plaques within each scheme

were made using nonmodel-based analysis because this approach affords reduced complexity

48

and bias in analysis of data in comparison to model-based approaches in the sense that a

priori enhancement behaviours are not assumed

32 Hypothesis

General hypothesis Nonmodel-based DCE-MRI analysis will demonstrate increased

gadolinium uptake in high risk carotid artery plaques compared to low risk plaques

Specific hypothesis In comparison to low risk carotid artery plaques high risk plaques will

demonstrate increased AUC initial and late enhancement rates peak enhancement and early-

late enhancement rate ratio Additionally the time to peak enhancement in high risk plaques

will be shorter in high risk plaques compared to low risk plaques

Previous studies demonstrate that ktrans

and vp measures of tissue permeability and fractional

neovasculature content correlate with the degree of plaque vulnerability (Kerwin et al 2008)

which is defined as the propensity for clinical sequelae arising from thromboemboli

formation Further studies demonstrate that nonmodel-based parameters including AUC also

correlate with features of plaque vulnerability in symptomatic carotid atherosclerotic plaques

(Calcagno et al 2008) while previous non-atherosclerotic studies reveal that other nonmodel-

based metrics namely the early and late enhancement rates and their ratio peak enhancement

and time to peak are useful for oncological assessment of suspected tumours (Isebaert et al

2011 Ren et al 2008 Zhang et al 2008) Finally in trials examining the clinical outcomes

of patients with carotid artery plaque undergoing or not undergoing CEA increasing

reduction in relative stroke risk by CEA was found to be associated with degree of stenosis

and previous ischemic symptoms (ACAS Collaborators 1995 NASCET Collaborators

49

1991b) In light of this assortment of evidence the above hypothesis was formed and applied

to both sets of assessment criteria described in Section 31

50

Chapter 4 Methods

41 Study Design

411 Ethics Approval and Subject Recruitment

This study was approved by the institutional Research Ethics Board of the University Health

Network and was conducted from November 2009 to April 2011 at the Toronto Western

Hospital Toronto Ontario Canada Potential study subjects with asymptomatic or recently

symptomatic carotid atherosclerosis were identified by participating physicians at the Toronto

Western and Toronto General Hospitals under the auspices of the Joint Department of

Medical Imaging and the Departments of Neurology Internal Medicine and Vascular

Surgery Following consultation with their attending interventional neuroradiologist

neurologist internist or vascular surgeon thirty-two subjects (age 716 plusmn 96 years range 58ndash

91 years 22 male) with known carotid artery stenosis or occlusion were approached for study

participation Of those fourteen subjects (age 720 plusmn 90 years range 58ndash85 years 11 male)

provided written informed consent and were enrolled into this study For cases in which

English was not spoken by the study participant consent was obtained through an immediate

family member who acted as a translator A summary of demographics of enrolled subjects is

provided in Table 41

51

412 Subject Inclusion and Exclusion Criteria

Subject inclusion criteria included 1) weight less than 136 kg (300 lbs) due to MRI scanner

limitations 2) ability to provide written informed consent or express consent through the use

of a translator and 3) known unilateral or bilateral symptomatic or asymptomatic carotid

atherosclerosis

Subject exclusion criteria were any of the following 1) history of brain trauma or severe

neurological disease that would confound the evaluation of clinical imaging with respect to

interpretation of previous ischemic changes in brain parenchyma 2) known allergy to MRI

contrast agents or 3) standard contraindications to MRI Individual carotid arteries within

each subject were excluded from analysis if atypical atherosclerosis was suspected (see 413)

Of the fourteen subjects who provided written informed consent (Table 41) four subjects

were excluded from subsequent analysis due to either 1) uninterpretable images resulting

Subject Age Sex Analysis Status Symptomatic Type Location Symptomatic Interval

01 77 M Excludeddagger Yes Stroke Right 3 days

02 63 M Included No

03 70 M Yes Stroke Right 13 days

04 58 M Included Yes Stroke Left 6 days

05 78 M Included No

06 76 M Included No

07 81 M Included Yes Stroke Left 10 hours

08 74 M Included Yes Stroke + TIA Left 1 day

09 85 M Included Yes Stroke Right 1 day

10 59 F Included No

11 67 M No

12 78 M Included Yes TIA Left 11 months

13 61 F Included Yes Stroke + TIA Left 3 days

14 81 F Yes Stroke Right 3 days Presence of recent symptoms of cerebral ischemic

Table 41 Demographics of Enrolled Subjects

ExcludedDagger

ExcludedDagger

Excludeddagger

52

from patient motion during MRI or 2) premature termination of MRI by the patient due to

anxiety including claustrophobia or restlessness A certain proportion of unsuccessful MRI is

to be expected in any subject population however this was expected to be larger in the

present study attributable to the high proportion of patients with neurological impairments in

the context of recent stroke or TIA Indeed three of the enrolled four subjects excluded from

analysis due to insufficient image quality or premature scan termination had experienced a

stroke within the two weeks preceding MRI

413 Exclusion of Atypical Atherosclerosis

Carotid arteries were excluded from analysis if carotid endarterectomy or stenting had been

previously performed (n=1) Arteries were also excluded if their etiology was thought to be

radiation-accelerated atherogenesis (n=1) Due to their proximity to sites susceptible to

tumorous growths in the neck the carotid arteries are often exposed to high doses of radiation

during radiation therapy leading to an abnormally high incidence of atherosclerosis and its

rate of progression in the arteries of patients ipsilateral to previous radiation treatment

(Gianicolo et al 2010) For this reason radiation-accelerated atherogenesis is thought to

represent an atypical form of atherosclerosis that is not present within the radiation-naive

population

414 Sample Size Estimation

Due to lack of previous data comparing high and low risk atherosclerotic plaques evaluated

by nonmodel-based DCE-MRI the use of an a priori sample size calculation was precluded in

the present study However to address this concern a retrospective post hoc sample size

53

calculation was performed using data derived from the current study the results of which are

detailed in 54

42 Carotid Artery Classifications

Carotid arteries (n=8) were excluded from analysis if subject MRI scanning was terminated

prematurely or if images were of insufficient quality Carotid arteries of enrolled subjects

meeting inclusionexclusion criteria and with successful MRI (n=18) were identified as

representing high or low risk for precipitating ischemic stroke on the basis of two independent

classification schemes 1) current standard-of-practice clinical criteria and 2) imaging

criteria

421 Classification by Clinical Criteria

Clinical criteria for high risk arteries (n=8) were defined as 1) cerebral ischemic event (stroke

or transient ischemic attack [TIA]) attributed to carotid atherosclerosis within 1 year

preceding MRI presentation (in all but 1 subject scanning was performed within 2 weeks of

symptom onset) or 2) or severe stenosis (gt70 NASCET collaborators 1987) on CTA

performed as part of clinical management Clinical criteria for low risk arteries (n=7) were

defined as 1) stenosis of 69 or less on CTA performed as part of clinical management 2)

focal hyperdensities within the carotid artery wall on clinical CTA indicative of calcified

atherosclerotic plaque or 3) eccentric or concentric carotid artery wall thickening on clinical

CTA defined as abnormal thickness of the iso- or hypodense region surrounding the carotid

lumen with relation to normal anatomy Occluded arteries (n=3) were excluded from clinical

criteria classification

54

422 Classification by Imaging Criteria

IPH within carotid atherosclerotic plaques is associated with clinical events (Altaf et al

2008) is a mechanism of plaque progression and is an indication of vulnerable plaque (Stary

2000) Imaging criteria for high risk arteries (n=9) were defined as presence of IPH on

carotid vessel MRI which was identified as 1) carotid vessel wall hyperintensity on coronal

3D gradient-echo magnetic resonance direct thrombus imaging (Moody et al 2003 see 433)

greater than that of ipsilateral sternocliedomastoid muscle or 2) co-localized hyperintensity

(greater than sternocliedomastoid muscle) within the vessel wall on T1- and T2-weighted

MRI thought to indicate recent (within 1-6 weeks) evolution of IPH (Chu et al 2004)

Imaging criteria for low risk arteries (n=9) were defined as absence of IPH based on the above

criteria Figure 41 provides an example of IPH in a carotid artery wall as defined by

magnetic resonance direct thrombus imaging (Moody et al 2003 see 433)

55

Figure 41 Example of IPH within the carotid artery wall in a representative subject on

coronal 3D gradient echo MRI Signal hyperintensity within the right carotid artery wall

(single arrows) relative to the sternocleidomastoid muscle (red dashed box) is due to the short

T1 of methemoglobin blood product and is indicative of recent IPH (within 6 months Moody

et al 2003) Similar hyperintensities are not evident within the wall of the left carotid artery

Based on these findings the arteries (subject 06) were classified by imaging criteria as right

carotid = high risk left carotid = low risk R = right side L = left side

56

43 Magnetic Resonance Imaging Protocol

431 Subject Preparation

All MRI studies were performed on a 30-Tesla scanner (Signa HDx GE Healthcare

Milwaukee Wisconsin) using a bilateral 4-channel phased-array surface coil (Machnet BV

Eelde the Netherlands) with two coil elements per side Subjects lay supine on the gantry in

the head-first orientation The left cephalic vein was canalized (the right was canalized if the

attempt to canalise the left was unsuccessful) by the MRI technologist for intravenous

administration of gadolinium-based contrast agent (gadobutrol 10 molL (Gadovist) Bayer

Healthcare AG Berlin Germany) A standard contrast dose of 01 mLkg body weight and

30 mL isotonic saline were loaded into an automated power-injector system (GE Healthcare)

and the infusion rate was set at 2 mLsecond A contrast injection was not performed at this

time The bilateral receiver coils were positioned superficial to the approximate location of

the carotid bifurcation and held in place by an adhesive strap and the subjects head was held

motionless An MRI-compatible pulse oximeter was applied to the index finger of the

subjects right hand for cardiac gating of MRI pulse sequences Finally the chin of each

subject was used as the reference structure for the approximate positioning of the carotid

artery bifurcation at the isocenter of the MRI

432 Localization of the Carotid Bifurcation

An anatomical scout was first prescribed in 3 planes (axial sagittal and coronal) These

images were used to determine the approximate anatomical location of the carotid bifurcation

along the inferior-superior axis A calibration scan was then performed (ASSET [array spatial

sensitivity encoding technique] GE Healthcare) for application of automated signal intensity

57

correction (PURE [phased array uniformity enhancement] GE Healthcare) in all subsequent

series which corrected for spatial-dependent variation in signal-to-noise ratio (SNR) due to

low receiver coil penetration depth Finally axial two-dimensional (2D) spoiled-gradient

recalled-echo (SPGR) time-of-flight (TOF) MR angiography (TOF-MRA) was performed at

the approximate location of the carotid bifurcation with coverage extending 30ndash40 mm

superior and inferior to its location Spatial presaturation was applied superior to the imaging

volume to null venous blood signal Maximum intensity projection (MIP) images

reconstructed from TOF-MRA depicted the course of the common bifurcation and internal

portions of the extracranial carotid arteries The MIP images were then used to prescribe all

subsequent series Table 42 details the MRI scan parameters

58

Series number 1 2 3 4 5 6 7 8 9

Sequence description Calibration TOF scout T2 T1 DCE-MRI

Acquisition plane 3-plane Axial Axial Axial Axial Coronal Axial Axial Axial

Sequence type GRE GRE SPGR FSE-XL FSE-XL SPGR FSE-XL FSE-XL FSE-XL

Acquisition mode 2D 2D 2D 2D 2D 3D 2D 2D 2D

Gradient Mode Zoom Whole Whole Zoom Zoom Zoom Zoom Zoom Zoom

Number of slices 33 38 40 10 10 50 1 1 10

Slice thickness (mm) 50 80 30 25 25 10 30 30 25

Slice overlap (mm) 15

Matrix (phase x frequency) 128 x 256 32 x 32 256 x 256 320 x 320 320 x 320 320 x 320 160 x 160 160 x 160 320 x 320

Field of view (mm) 230 320 180 170 170 230 140 140 170

Phase field of view () 100 100 100 70 70 70 80 80 70

Phase direction Unswap A-P A-P A-P A-P R-L A-P A-P A-P

Repetition time TR (ms) 51 150 163 1500 750 73 750 750 750

Echo time TE (ms) 15 21 37 85 89 21 56 56 89

Inversion time TI (ms) 500 500 Auto 500 125 125

Flip angle (degrees) 30 50 55 90 90 10 90 90 90

Echo-train length ETL 30 6 22 22 6

Number of averages NEX 1 1 1 2 1 3 16 1 1

Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250

Fat suppression No No No Yes Yes SPECIAL Yes Yes Yes

Spatial pre-saturation S I+S I+S I+S I+S I+S I+S

PURE correction No No Yes Yes Yes Yes Yes Yes Yes

Contrast volume (mmolkg) 01 01

Cardiac gating No No No Yes Yes No Yes Yes Yes

R-R interval (cycles) 2 1 1 1 1

Trigger delay (ms) 511 511 511 250 250

Trigger window () 10 10 10 3 10

Trigger level () Auto Auto Auto Auto Auto

Multiphase No No No No No No No Yes No

Total Phases 48

Pre-contrast phases 6

Post-contrast phases 42

Time per phase (s) 10

Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout

Intraplaque hemorrhage

Pre-DCE base

Post-contrast T1

59

433 Carotid Vessel Wall MRI

To aid visualization of carotid atherosclerotic plaque high-resolution black-blood imaging

was employed on T1- and T2-weighted imaging in which the TI was chosen for optimal

blood nulling at 3 Tesla At least ten axial 2D fast spin-echo (FSE) double inversion-recovery

(DIR) T2-weighted images (repetition time TR = 1500 ms echo time TE = 85 ms inversion

time TI = 500 ms voxel size = 053 mm2 x 250 mm) were acquired through the CCA

carotid bifurcation and ICA In the case of extensive carotid plaques scan coverage was

increased accordingly to a maximum of 16 slice locations Spatial presaturation was applied

inferior and superior to the imaging volume to augment double inversion-recovery blood

suppression at the carotid bifurcation High signal from peri-adventitial adipose tissue was

nulled using fat suppression technique (GE Healthcare) To reduce image blurring and

ghosting (defined as image duplication in the phase-encode direction) artifact caused by

arterial pulsation cardiac gating was employed to ensure each sample of k-space occurred

during the same phase of diastole within the cardiac cycle Axial 2D FSE double inversion-

recovery T1-weighted imaging (TR = 750 ms TE = 89 ms TI = 500 ms voxel size = 053

mm2 x 250 mm) was performed in identical fashion to T2-weighted imaging Coronal three-

dimensional (3D) SPGR T1-weighted imaging (TR = 71 ms TE = 23 ms voxel size = 072

mm2 x 100 mm) was then performed for the detection of intraplaque hemorrhage within the

carotid artery wall (Altaf et al 2008) Fat suppression during this sequence was achieved

through the use of spectral inversion at lipids technique (SPECIAL GE Healthcare)

60


To achieve maximum temporal resolution dynamic contrast-enhanced MRI (DCE-MRI) was

limited to one slice This decision was based on the requirement for cardiac gating the

requirement for use of a positive-contrast (T1-weighted) sequence and the requirement for

black-blood imaging to achieve optimal contrast-to-noise ratio (CNR) between the vessel

lumen and arterial vessel all of which substantially prolonged the per-slice imaging time

compared to conventional ungated T2W (susceptibility-weighted) negative-contrast bright-

blood sequences To maximize utility for plaque imaging DCE-MRI imaging was prescribed

in oblique fashion with the slice prescribed appropriately so as to intercept the largest cross-

sectional volume of atherosclerotic plaque in each carotid artery Prior to DCE-MRI one pre-

contrast 2D FSE double inversion-recovery T1-weighted image (TR = 750 ms TE = 56 ms

TI = 500 ms voxel size = 088 mm2 x 300 mm) was acquired to serve as a ldquobaserdquo image for

the coregistration algorithm (see section 532) The imaging parameters were identical to

those applied during DCE-MRI with the exception of the number of excitations (16 pre-

contrast versus 1 DCE-MRI) and inversion time (TI = 500 ms pre-contrast versus 125 ms

DCE-MRI) Since SNR of MRI scales with square root of the number of excitations the SNR

of the pre-contrast image was approximately 4-fold higher than that of the DCE-MRI images

thus providing an optimal template to which DCE-MRI images were aligned during post-

processing The inversion time of the DCE-MRI series (and all subsequent series) was chosen

to be significantly shorter than in pre-contrast images to account for the significant reduction

in T1 relaxation time due to the effect of gadobutrol

61

Prior to the start of the DCE-MRI acquisition the loaded contrast and saline volumes of the

power-injector unit were verified and the system was armed The cardiac-gated 2D FSE

double inversion-recovery T1-weighted DCE-MRI was then triggered through the use of a

Linux script written to control the multiphase aspect of the image acquisition To allow time

for complete imaging of each slice (approximately 6 seconds) scanner reset between phases

(2 seconds) variation in subject heart rate (1 second) and cardiac arrhythmia rejection (1

second) the temporal resolution for each DCE-MRI phase was programmed at 10 seconds

Prior to contrast injection 6 pre-contrast phases (60 seconds) were obtained to establish

baseline signal intensity values To allow time for infusion and distribution to the carotid

arteries by the seventh imaging phase contrast injection was triggered upon completion of the

fifth imaging phase Contrast injections in all subjects were complete within 5 seconds for

contrast bolus and within 20 seconds including saline flush Following contrast arrival at the

carotid arteries on the seventh imaging phase imaging was continued for an additional seven

minutes for a total of 42 post-contrast phases or 48 total phases

435 Post-Contrast Carotid Vessel Wall MRI

Following the completion of DCE-MRI high-resolution post-contrast axial 2D FSE double

inversion-recovery T1-weighted imaging was performed Imaging parameters were identical

to those for pre-contrast T1-weighted imaging (series 5) with the exception of inversion time

(TI = 500 ms pre-contrast versus 125 ms post-contrast)

62

44 Post-Processing of DCE-MRI Data

Offline image post-processing was performed on DCE-MRI data of all subjects for reduction

of noise resulting from subject intrascan translational motion swallowing coughing and

respiration mixing and recirculation effects of contrast shortly after injection signal intensity

variations due to noise within the MR imaging hardware and inter-phase changes in T1

steady-state effects that result from the inherent variability in heart rate and cardiac

arrhythmia rejection rate between phases of the same subject To achieve this DCE-MRI

images were processed using a series of automated Unix-based scripts written for the

purposes of this study

All mathematical calculations and coregistration of imaging data were performed using freely

available image processing software (analysis of functional neuroimages [AFNI] Cox 1996)

Due to their development for the purpose of post-processing functional MRI (fMRI) data

acquired within the brain AFNI programs are not conventionally applied for analysis of data

outside this realm However the robust and open-source nature of their development has

enabled the application of AFNI coregistration programs to other anatomical sites such as the

spinal cord (Wang et al 2006) and the soleus and gastrocnemius muscles of the leg (Bulte et

al 2006) Moreover a comparison study of various freely-available coregistration software

found AFNI software was either equivalent or superior to other MRI coregistration and data

analysis software in several areas including motion correction spatial interpolation

algorithms and computation speed (Oakes et al 2005) Due to these strengths its status as

freely available software and its widespread use for the processing of fMRI data AFNI was

therefore selected for the analysis of DCE-MRI data

63

441 Region of Interest Selection and Cropping

With respect to other structures of the neck inter-phase variation in relative position of the

carotid arteries during DCE-MRI was considerable This was thought to be the result of

changes in tone of the pharyngeal and laryngeal wall musculature that accompanied subject

breathing and swallowing (although subjects were instructed to refrain from swallowing

during DCE-MRI) Due to this confound application of a rigid-body coregistration algorithm

to the entire neck for the purpose of carotid artery coregistration would have yielded poor

results since the carotid arteries represent only a fraction of the total neck area To

circumvent this issue two coordinate sets were identified through user-script interaction each

representing the center of the right and left carotid artery segments of interest (CCA or ICA)

Two square ROI of 40 x 40 voxels (35 x 35 mm) centered on these coordinates were then

constructed within the plane of the image and used to construct cropped images of the highly-

averaged pre-contrast base (series 7) and DCE-MRI data sets for region-specific rigid-body

coregistration The size of these ROI were similar to those selected by Kerwin Cai and Yuan

(2002) who selected in-plane ROI for coregistration centered around the carotid artery and

extending 40 x 40 mm

442 Image Coregistration

Coregistration of DCE-MRI data was performed using the AFNI program tool 3dAllineate

(Saad et al 2009) which allows for alignment of functional data sets based on optimization of

one of several cost function parameters specified by the user Of these mutual information

(MI) has been previously applied to the coregistration of DCE-MRI data with success

(Bruchner Lucht and Brix 2000 Vos et al 2010) owing to the ability of the cost

64

optimization strategy to maximize mutual information across scans that differ in contrast

weightings The MI cost function is given by the equation

[41]

where H(pi) is the source image histogram of the pre-contrast image H(pj) is the target image

histogram of the DCE-MRI image and H(rij) is the joint histogram of the voxel pairs of both

images (Wells et al 1996 Saad et al 2009) The MI cost function is particularly suited for

analysis of DCE-MRI data due to the differing contrast in each of the phase images which

owes to the T1-shortening effect of the gadobutrol bolus as it passes through the tissue

Prior to coregistration a plot of global signal intensity changes within the DCE-MRI data set

was constructed and used to identify global signal-intensity outliers each defined as a

reduction in mean signal intensity in an individual phase image due to subject motion and

based on the output of AFNI program 3DToutcount (Cox 1996) In the case of individual

outliers that did not occur in groups phase images were corrected by substituting the

arithmetic mean of the images immediately preceding and following the outlying phase

image Where an individual outlier occurred as the last image in the DCE-MRI series it was

replaced with a copy of the second-last image in the series Outliers occurring in succession

were not corrected

To begin coregistration the first phase image of the DCE-MRI data set was coregistered to

the base pre-contrast image using 3dAllineate Each subsequent phase image was then

aligned in succession to the pre-contrast base image using identical command line options

65

To ensure proper image alignment multiple iterations of the coregistration technique were

permitted The requirement for successive iterations was determined through evaluation of

the MI cost functional result as output by 3dAllineate In the case that the result lay outside a

user-specified constraint successive coregistration of resultant images (iteration) was

performed until either 1) the minimum user-specified cost functional constraint was

achieved or 2) a maximum user-specified iteration limit was achieved For all subjects the

user-specified final cost constraint for MI was set at minimum of 70 (final mutual

information cost functional equal to or greater than 70 between images) and the iteration

limit was set to 20 iterations In the case where additional iterations did not result in an

improvement of the final cost function additional iterations were not performed regardless of

whether the number of iterations performed was less than the user-specified limit

443 Signal Intensity Normalization of DCE-MRI Data

To construct the normalized DCE-MRI time-series the mean of the first six DCE-MRI phases

was first calculated The mean pre-contrast signal intensity value was then subtracted from

each subsequent phase image on a voxel-wise basis by to yield a normalized time series in

which each voxel value represented the tissue enhancement independent of its corresponding

pre-contrast signal intensity To accommodate inter-subject comparisons of enhancement

time-courses DCE-MRI data were next normalized with respect to average baseline signal

intensity of a 10 x 10 voxel mask selected within the ipsilateral sternocleidomastoid muscle

The voxel-wise normalized signal intensity time course data were then plotted with respect to

this muscle signal intensity

66

444 Curve Fitting of DCE-MRI Signal Intensity Time Course

Fitting of data by minimization of LMMSE is an effective method for reducing voxel-level

noise and improving quality of DCE-MRI time series performed for the examination of

carotid atherosclerotic plaque (Kerwin Cai and Yuan 2002) To remove voxel-wise inter-

phase signal intensity variations due to MRI scanner noise low SNR characteristic of DCE-

MRI and changes in T1 steady-state owing to heart rate and arrhythmia rejection variability a

mathematical model was developed for application to DCE-MRI data based on the known

enhancement characteristics of T1-weighted imaging in response to gadolinium-based

contrast agent dynamics shortly after intravenous administration This behaviour was

evaluated as the summative contribution of three mathematical functions 1) a cumulative

exponential distribution (exponential recovery akin to that which characterizes T1 relaxation)

function with a horizontal asymptote lying approximately coincident to the normalized signal

intensity of the final DCE-MRI image (at tmax) to represent the short-term equilibration of

contrast material concentration within the tissues 2) a gamma variate function to represent

the passage of the highly concentrated first pass of the gadobutrol injection bolus through the

fractional plasma volume of each voxel (model-based DCE-MRI analyses must assume

instantaneous uniform mixing of contrast within blood plasma Tofts and Kermode 1991)

and 3) a quadratic function to represent variable tissue enhancement characteristics The

derived model is given by the equation

[42]

67

where SIfit(t) is the calculated voxel-wise signal intensity of the curve-fitted model at time t

SInormal(tmax) is a variable constrained to within 20 of the voxel-wise signal intensity of the

normalized DCE-MRI time series at time tmax α is a variable scaling factor of the cumulative

exponential distribution term (1 ndash e -tα

) r and β are the variable shape and scale parameters

respectively of the gamma variate term (t rmiddote

-tβ ) and a b and c are variable coefficients of

the quadratic equation term (at2 + bt + c) Application of the curve-fitting model to DCE-

MRI data was achieved using non-linear regression with least squares fitting as afforded by

the AFNI program 3dNLfim (Ward 2000) To fit each time series voxel 100000 random

parameter sets were generated from which the 100 best parameter sets were evaluated based

on LMMSE In each artery fitting was performed only in those phases subsequent to the

arrival of contrast material as specified by the AFNI program 3dToutcount (see 442) All

other pre-contrast phases were assigned a value of zero Figure 42 provides an example

result of the curve-fitting algorithm in a representative artery

45 Calculation of Nonmodel-Based DCE-MRI Parameters

Several nonmodel-based parameters of tissue enhancement were calculated on a voxel-wise

basis from the fitted DCE-MRI data Here it is important to note the distinction between

curve-fitting of MRI-DCE data for purposes of signal intensity noise reduction as detailed in

section 444 and nonmodel-based analysis of tissue enhancement in this same data

Nonmodel-based DCE-MRI analysis refers to the characterization of the T1-weighted

enhancement time course data itself without the use of a priori mathematical models To

emphasize this distinction previous work has referred to the derived DCE-MRI parameters as

ldquosemi-quantitativerdquo (Walker-Samuel Leach and Collins 2006) however this nomenclature

68

may inappropriately convey the impression that these parameters are only partially

quantifiable Figure 43 provides an example of each calculated nonmodel-based parameter

map in a representative carotid artery

69

Figure 42 Result of the curve-fitting algorithm in a representative asymptomatic left carotid

artery (A) Cropped pre-contrast base image depicting extent of carotid atherosclerotic

plaque (blue contour) and vessel lumen (green contour) (B) Curve-fitted model time series

result in 16 contiguous voxels contained within the carotid plaque (red square in frame A)

(C) Curve-fitted result of frame B shown with non-fitted time series (red overlay) Abscissa

DCE-MRI phase number Ordinate MRI normalized signal intensity nSI = normalized

signal intensity

70

Figure 43 DCE-MRI parameter maps derived in a representative carotid artery (A) Pre-

contrast image (B) Boxed region (red) in frame A depicting extent of carotid plaque (blue

contour) and vessel lumen (green contour) (C Cumulative AUC (D) Incremental AUC

(E) Early enhancement rate (F) Maximum enhancement (G) Time to peak (H) Late

enhancement rate (I) Early-late enhancement rate ratio SI = signal intensity (frames A-B)

or normalized SI (frame F) min = minutes AU = arbitrary units 1k = 1000

71

451 Area Under the Curve

The initial area under the gadolinium curve or simply the AUC is a commonly-derived

parameter of nonmodel-based DCE-MRI analyses that bears strong correlation to parameters

derived from conventional quantitative bright-blood DCE-MRI approaches (Walker-Samuel

Leach and Collins 2006) In two previous applications of nonmodel-based analysis to DCE-

MRI data collected in an animal model of atherosclerosis AUC was shown to correlate with

atherosclerotic plaque neovessel count (Calcagno et al 2008) while offering high

reproducibility with respect to inter-scan as well as inter- and intraobserver agreement

(Calcagno et al 2010) Here AUC was calculated as the sum of the positive areas between

the fitted and normalized (pre-contrast signal intensity baseline of zero) time-series curve and

the abscissa given by the equation

[43]

where AUCcumulative (T) is the cumulative AUC at time T with units of minutes and evaluated

over the interval from contrast arrival (time zero) to time T SI fitted (t) is the fitted time-series

curve and T is each of 1ndash7 minutes after contrast arrival Thus seven AUC maps were

constructed termed cumulative AUC each reflecting the cumulative AUC between contrast

arrival and the corresponding evaluation point (Figure 43C) Though its utility thus far

remains unevaluated AUC was also calculated within a moving window in which the

parameter was derived independently within each post-contrast minute given by the equation

72

[44]

where AUCincremental (T) is the AUC within the moving window evaluated over the interval

from time T ndash 1 to time T and with units of minutes SI fitted (t) is the fitted time-series curve

and T is each of 1ndash7 minutes after contrast arrival AUC values for each post-contrast minute

calculated by this method were therefore independent of AUC calculated in previous minutes

(Figure 43D)

452 Early Enhancement Rate

Although not previously applied to the study of atherosclerosis the rate of contrast

enhancement immediately following contrast arrival is useful for nonmodel-based evaluation

of cancer (Zhang et al 2008 Ren et al 2008 Isebaert et al 2011) Because expression of

VEGF and angiogenesis are increased vulnerable atherosclerotic lesions (Inoue et al 1998

Bates 2010) the early enhancement rate may also allow differentiation of symptomatic and

asymptomatic carotid plaque The early enhancement rate was measured here as the slope of

the signal intensity change between normalized baseline (signal intensity = 0) and the first

phase after contrast arrival measured in units minutes-1

(Figure 43E)

453 Maximum Enhancement

Signal intensity maxima in each voxel were measured as the peak signal intensity in each

voxel time series without regard to the phase in which peak enhancement was observed

(Figure 43F)

73

454 Time to Peak

The time to peak parameter differs between symptomatic and asymptomatic plaques in rabbit

atherosclerotic plaques (Phinikaridou et al 2010b) Time to peak was calculated as the time

from contrast arrival to peak signal intensity in minutes post-contrast in the fitted DCE-MRI

data (Figure 43G)

455 Late Enhancement Rate

The rate of late signal intensity change varies with the degree of neovasculature and is useful

for differentiating between prostatic carcinomas and benign prostatic hyperplasia (Ren et al

2008) A similar enhancement relationship may exist between symptomatic and

asymptomatic atherosclerotic plaques considering that plaque vulnerability correlates with

the degree of neovasculature (Inoue et al 1998 Sirol et al 2009) To avoid artificial over- or

underestimation of the late enhancement rate due to large signal intensity changes

immediately following contrast arrival the late enhancement rate was calculated here as the

slope of the fitted signal intensity curve between 2 minutes and 7 minutes in units of

minutes-1

(Figure 43H)

456 Early-Late Enhancement Rate Ratio

In comparison to evaluation using the early enhancement rate alone consideration of the early

and late enhancement rates together has been shown to more accurately distinguish prostatic

carcinomas from benign prostatic hyperplasia in humans (Isebaert et al 2011) Therefore the

early-late enhancement rate ratio was also calculated here by division of the late enhancement

rate by the early enhancement rate to yield a dimensionless quantity (Figure 43I)

74

46 Analysis of Nonmodel-Based DCE-MRI Parameters

461 Region of Interest Selection

Regions of interest (ROI) were determined qualitatively in each carotid artery In every

included carotid artery (n=18) the vessel wall area and vasa vasorum area were drawn on the

pre-contrast base image Vessel wall area was defined as the entire circumference of the

carotid artery wall including those areas not containing a conspicuous atherosclerotic plaque

Vasa vasorum was identified as the outer rim of the vessel wall If uncertainty existed as to

the location of the vasa vasorum post-contrast T1-weighted images were used to identify a

hyperintense outer rim shown by Aoki et al (1999) to be enhancing vasa vasorum In those

carotid arteries with a conspicuous plaque on MRI (n=14) two additional ROI were drawn to

indicate total plaque area and fibrous cap area Carotid plaque within the plaque area ROI

was defined as the presence of a conspicuously abnormal region with eccentric wall

thickening and the presence of one or more clearly defined plaque components including

lipid core fibrous cap calcification or IPH The fibrous cap ROI was defined as the region of

abnormal vessel wall immediately superficial to the carotid artery lumen and contained within

the total plaque area ROI Only juxtaluminal voxels were selected for the fibrous cap ROI

unless comparison between pre- and post-contrast T1-weighted images suggested the

presence of a thickened fibrous cap (Wasserman et al 2002) Figure 44 provides an example

of the selected ROI in a carotid artery with extensive atherosclerotic plaque

462 Group Analysis

Following ROI selection each ROI mask (2-4 total) was applied to every DCE-MRI

parameter map (7 total) to calculate the mean ROI value using AFNI program 3dmaskave

75

(Cox 1996) thus yielding 14-28 measurements per artery examined Group-level analyses

were then performed on the mean DCE-MRI parameters calculated from each artery to test

for statistical differences between high and low risk artery sub-groups as defined in section

421 In addition each ROI was also applied to the curve-fitted DCE-MRI time series data of

each artery to yield an average signal-intensity time-course within the ROI Mean time-

course data from within each artery ROI were then averaged to yield a mean group-level time-

course for each ROI to identify the representative shape of time-course data within the

corresponding plaque areas

76

Figure 44 ROI selection (red overlay) in a representative carotid artery (A) Pre-contrast

image depicting extent of carotid plaque (blue contour) and vessel lumen (green contour)

(B) Vessel wall ROI (C) Vasa vasorum ROI (D) Plaque volume ROI including fibrous

cap (E) Fibrous cap ROI SI = signal intensity 1k = 1000

77

47 Statistical Analysis

471 Evaluation of Curve Fitting Algorithm

To evaluate the goodness of the curve-fitted DCE-MRI data the mean coefficient of

determination R2 was determined in each artery ROI An example of the result of an

individual-level correlation analysis is provided in Figure 45

472 Comparison of High versus Low Risk Arteries

Statistical comparisons of mean DCE-MRI parameters between artery groups were performed

using two-tailed unpaired students t-tests assuming unequal variances A result was

considered significant if plt005

78

Figure 45 Evaluation of the goodness of fit following curve-fitting of DCE-MRI data in a

representative carotid artery (A) Pre-contrast image depicting extent of carotid plaque (blue

contour) and vessel lumen (green contour) (B) Colourized parameter map of the coefficient

of determination as an indication of goodness of fit SI = signal intensity R2 = coefficient of

determination

79

Chapter 5 Results

51 Plaque Characteristics of Subjects with Successful MRI

Of the fourteen subjects enrolled ten successfully completed MRI scanning Subjects 03 and

11 were excluded from analysis due to premature termination of MRI scanning by these

subjects while subjects 01 and 14 completed the session successfully but were excluded from

analysis due to insufficient image quality caused by excessive motion artifact (Table 41)

511 Clinical Criteria

Of the 20 successfully scanned carotid arteries 5 were excluded from analysis due to

occlusion previous CEA or previous radiation treatment (see 423) The remaining 15

carotid arteries were assigned to either the high risk (n=8) or low risk (n=7) artery groups

based on the criteria defined above Table 51 summarizes the carotid plaque characteristics

of the ten successfully scanned subjects

512 Imaging Criteria

Of the 20 successfully scanned carotid arteries 2 were excluded from analysis due to previous

CEA or previous radiation treatment (see 423) The remaining 18 carotid arteries were

assigned to either the high risk (n=9) or low risk (n=9) artery groups based on the criteria

defined above

80

52 AUC Enhancement Rate and Maximum Enhancement Are Increased

in IPH-Positive High Risk Plaques Defined by Imaging Criteria

Analysis of carotid plaques (n=18) revealed significant differences in several measured DCE-

MRI parameters between plaques with and without evidence of IPH as detected by magnetic

resonance direct thrombus imaging (Moody et al 2003) and evaluation with multiple MRI

contrast weightings (designated IPH-positive and IPH-negative respectively) Mean

Subject Artery Risk Stenosis IPH Symptomatic Excluded Reason

02Right High Occluded NA Yes No Yes Occluded

Left Low Mild None visible No No Yes Previous CEA

04Right Low Mild None visible No No No

Left High Severe 759 Yes Yes 6 days No

05Right Low Moderate 382 No No No

Left High Severe 564 Yes No No


Left High Occluded NA Yes No Yes Occluded

07Right Low Moderate 336 Yes Yes 10 hours No



Left High Severe 318 Yes Yes 2 days Yes Previously irradiated

09Right High Severe 93 No Yes 1 day No

Left Low Mild 427 No No No

10Right Low Moderate 426 Yes No No

Left High Severe 240 No No No

12Right High Severe 390 No No No

Left High Severe 477 Yes Yes 11 months No

13Right Low Mild 48 No No No

Left Low Moderate 486 Yes Yes 1 day No

Mild = 0-29 Moderate = 30-69 Severe = 70-99 Occluded = 100

Cross-sectional plaque area measured at the slice location of DCE-MRI analysis

IPH = intraplaque hemorrhage CEA = carotid endarterectomy

Table 51 Carotid artery plaque characteristics of subjects with successful MRI

Plaque area (mm2) Intervaldagger

dagger Interval from ipsilateral symptom onset to MRI scanning

81

normalized signal intensity time course amplitudes of IPH-positive carotid plaques were

significantly higher than IPH-negative plaques in total vessel area total plaque area and

fibrous cap area ROI (plt005 Figure 51)

Mean cumulative (Figure 52) and incremental (Figure 53) AUC were significantly greater in

IPH-positive compared to IPH-negative carotid plaques for all ROI except vasa vasorum

indicating greater overall enhancement in IPH-positive plaques Linear regression analysis

yielded significantly greater rates of increase in cumulative AUC in IPH-positive compared to

IPH-negative plaques for all ROI (plt001) A tendency toward higher mean incremental

AUC at late post-contrast minutes was noted however comparisons between minutes 1 and 7

within the total plaque area total vessel area and vasa vasorum ROI demonstrated only

marginal significance (006ltplt009) Mean early enhancement rates of IPH-positive plaques

were significantly greater than IPH-negative plaques for total vessel area and total plaque area

ROI (p=0017 and p=0015 respectively Figure 54) Mean maximum enhancement

amplitude was also greater in IPH-positive plaques for total vessel and plaque area (p=0009

and p=0018 respectively Figure 55) Mean late enhancement rate was higher in IPH-

positive plaques for total vessel area (p=0025) but not for any other ROI (Figure 56) No

significant differences between IPH-positive and -negative plaques were found for time to

peak or early-late enhancement rate ratio

82

Figure 51 Mean normalized MRI-DCE signal intensity time course of IPH-positive and

IPH-negative carotid atherosclerotic plaques in 4 ROI (A) total vessel wall area (B) total

plaque area (C) fibrous cap area and (D) adventitial vasa vasorum Normalized signal

intensity time course amplitudes of IPH-positive carotid plaques (blue squares) were

significantly greater () than IPH-negative plaques (orange diamonds) at all time points

following contrast arrival (red arrows) in total vessel area total plaque area and fibrous cap

area ROI (plt005) Signal intensity time courses between IPH-positive and -negative groups

were not significantly different within the vasa vasorum ROI Error bars are standard error of

the mean (SEM) SI = signal intensity IPH = intraplaque hemorrhage

83

Figure 52 AUC of IPH-positive and -negative carotid plaques in 4 ROI (A) total vessel

wall area (B) total plaque area (C) fibrous cap area and (D) adventitial vasa vasorum

Cumulative AUC were significantly greater () in IPH-positive carotid plaques (blue squares)

than in IPH-negative plaques (orange diamonds) at every post-contrast minute in total vessel

area total plaque area and fibrous cap area (plt005) except for the first post-contrast minute

in the fibrous cap area ROI which reached only marginal significance (p=0062) Cumulative

AUC between IPH-positive and -negative plaques were not significantly different within the

vasa vasorum Data points at post-contrast minute 1 are more clearly resolved in Figure 53

for all ROI Error bars are SEM SI = signal intensity IPH = intraplaque hemorrhage

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Minutes post-contrast

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84

Figure 53 Incremental AUC of IPH-positive and -negative carotid plaques in 4 ROI (A)

total vessel wall area (B) total plaque area (C) fibrous cap area and (D) adventitial vasa

vasorum Incremental AUC were significantly greater () in IPH-positive carotid plaques

(blue squares) than in IPH-negative plaques (orange diamonds) at every post-contrast minute

in total vessel area total plaque area and fibrous cap area (plt005) except for the first post-

contrast minute in the fibrous cap area ROI which reached only marginal significance

(p=0062) Incremental AUC between IPH-positive and -negative plaques were not

significantly different within the vasa vasorum Error bars are SEM SI = signal intensity

IPH = intraplaque hemorrhage

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85

Figure 54 Box-and-whisker plot of early enhancement rates of IPH-positive and -negative

carotid plaques in 4 ROI (A) total vessel wall area (B) total plaque area (C) fibrous cap

area and (D) vasa vasorum Early enhancement rates were significantly greater () in IPH-

positive versus IPH-negative plaques for total vessel area and total plaque area (plt0015) but

not fibrous cap area or vasa vasorum ROI Rectangle = 025-075 interquartile range

horizontal line = median error bars = extreme values IPH = intraplaque hemorrhage

3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15

IPH-positive IPH-negative

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86

Figure 55 Box-and-whisker plot of the maximum enhancements of IPH-positive and -

negative carotid plaques in 4 ROI (A) total vessel wall area (B) total plaque area (C)

fibrous cap area and (D) vasa vasorum Maximum enhancements were significantly greater

() in IPH-positive versus IPH-negative plaques for total vessel area and total plaque area

(plt0018) but not fibrous cap area or vasa vasorum ROI Rectangle = 025-075 interquartile

range horizontal line = median error bars = extreme values IPH = intraplaque hemorrhage

10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87

Figure 56 Box-and-whisker plot of late enhancement rate of IPH-positive and -negative


area and (D) vasa vasorum Late enhancement rate was significantly greater () in IPH-

positive versus IPH-negative plaques for total vessel area (p=0025) but not total plaque area

fibrous cap area or vasa vasorum ROI Rectangle = 025-075 interquartile range horizontal

line = median error bars = extreme values IPH = intraplaque hemorrhage

-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88

54 DCE-MRI Parameters Are Not Different Between High Risk and Low

Risk Carotid Plaques Defined by Clinical Criteria

No significant differences were detected between the high (n=8) and low (n=7) risk artery

groups within any of the four regions of interest for any of the measured parameters

In carotid plaques without occlusion (n=15) average plaque cross-sectional area measured at

the location of DCE-MRI scanning was 340 plusmn 223 mm2 Plaque cross-sectional area was

positively correlated with the degree of stenosis (R2=036 plt003) and was not different

between the right and left carotid arteries of subjects

A Fishers exact test did not reveal significant association between the presence or absence of

intraplaque hemorrhage and symptoms of cerebral ischemia (Table 51)

To test whether data from the clinical criteria grouping were sufficiently powered to yield a

statistically significant result a post hoc sample size calculation was performed using the

cumulative AUC at post-contrast minute 7 (Figure 52) Using derived mean group values

associated standard errors of the mean and a statistical power threshold of 080 (80) post

hoc sample size calculation revealed a required sample size of 8 subjects

55 Curve-Fitting Using AFNI Provides Excellent Noise Reduction

Model-based data fitting was performed using AFNI program 3dNLfim in each successfully

scanned carotid artery (n=18) Goodness of fit was measured as the mean coefficient of

determination evaluated in each artery ROI Goodness of fit was high in every artery and ROI

evaluated (range of R2 09533 to 09972) Average quality of data fitting was not

89

significantly different between ROI groups Table 52 summarizes the coefficient of

determination measured in each artery

Subject Artery Vessel Area Plaque Area Fibrous Cap Vasa Vasorum

02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874


Each entry represents the average R2 value within the region of interest

90

Chapter 6 Discussion

The present study is the first to evaluate the use of DCE-MRI as a quantitative method for

differentiation of human carotid atherosclerotic plaques believed to be at high versus low risk

for precipitating cerebral ischemic events The major findings of this study are two-fold 1)

IPH-positive carotid plaques exhibit greater AUC early and late enhancement rate and peak

enhancement than IPH-negative plaques and 2) recently symptomatic or severely stenotic

(gt70) plaques do not exhibit differences in DCE-MRI parameters compared to

asymptomatic or moderately stenotic (lt70) plaques

61 Increased Enhancement Characteristics in High versus Low Risk

Plaques Defined on Imaging Criteria by Presence of IPH

This study is the first to demonstrate that nonmodel-based DCE-MRI parameters are different

between IPH-positive and -negative carotid artery plaques due to significantly increased

gadolinium uptake of IPH-positive compared to -negative plaques Specifically IPH-positive

plaques exhibited greater cumulative and incremental measures of AUC early and late

enhancement rates and peak enhancement Table 61 summarizes the comparisons performed

between IPH-positive and -negative plaques for each DCE-MRI parameter and ROI and their

statistical outcomes

91

These findings are consistent with the study hypothesis that IPH-positive plaques exhibit

increased enhancement rate peak and AUC than IPH-negative plaques Presence of IPH on

carotid vessel wall MRI is generally considered an indication of plaque vulnerability

(Kolodgie et al 2003) and is a likely indication of risk for subsequent cerebral ischemic

events Although previous MRI studies of dynamic plaque enhancement have not considered

IPH as a criterion for definition of vulnerable (high risk) versus stable (low risk) plaques two

recent studies have used the presence of intraluminal thrombus as a related definition of

vulnerability

In an MRI study of experimentally-induced aortic atherosclerosis in rabbits Phinikaridou et

al (2010a) found greater enhancement in plaques with evidence of thrombus following

pharmacological triggering with snake venom and histamine and noted that this correlated

with increased neovascularization and inflammation on histology In a related abstract

ROI Time to peak

Total vessel wall NS NS

Total plaque NS NS NS

Fibrous cap NS NS NS NS NS

NS NS NS NS NS NS NS

ROI = region of interest AUC = area under the curve NS = not significant

Early-late ratio = early-late enhancement rate ratio

Table 61 Summary of significant differences between high versus low risk plaques defined by imaging criteria

Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92

(Phinikaridou et al 2010b) rabbit aortic plaques underwent dynamic contrast-enhanced

imaging for qualitative evaluation of the enhancement time course Plaques in that study were

defined post hoc as vulnerable or stable based on the presence of intraluminal thrombus and

platelet aggregation or vessel wall disruption on histology Interestingly the authors noted

qualitative differences in the shape of the gadolinium uptake curves of vulnerable and stable

plaques which included greater peak enhancement and initial enhancement rate in vulnerable

compared to stable plaques findings that corroborate those of the present study

Although this study is the first to quantitatively compare DCE-MRI parameters between high

and low risk plaques its findings are consistent with the current state of knowledge relating to

plaque vulnerability Previous model-based DCE-MRI studies of human carotid

atherosclerosis indicate that the degree of plaque enhancement is most strongly associated

with the proliferation of neovasculature and infiltration of inflammatory cells both of which

are indices of the high risk vulnerable plaque (Kerwin et al 2006) IPH is thought to arise

within the plaque and contribute to increased gadolinium uptake (enhancement) through the

following series of events

1) Macrophages resident within the developed atherosclerotic plaque (AHA stage IV or

beyond see 2312) release the cytokine VEGF which stimulates the growth of

neovessels and augments vascular permeability (Inoue et al 1998)

2) This results in the increased accumulation of macrophages and the initiation of a vicious

cycle of ever-increasing angiogenesis and inflammation

93

3) Eventually the fragile and numerous neovasculature rupture leading to the rapid

accumulation of intraplaque hemorrhage or thrombus within the plaque (Vermani et al

2005)

4) A correspondingly rapid increase in the inflammatory state of the plaque occurs due to the

presence of the hemorrhage

In this sense the progression of plaque vulnerability is likely to be characterized better as a

series of punctuated equilibria than as a slow evolution Additionally IPH represents a much

greater stimulus for the accumulation of inflammatory cells and promotion of angiogenesis

and therefore also represents a stimulus for increasing fractional plasma volume and vascular

permeability of the surrounding plaque It is therefore likely that the response to the presence

of IPH within the plaque is this mechanism that allowed for the differentiation of IPH-positive

versus IPH-negative carotid plaques in the present study This is likely most true for AUC

which demonstrates an intractable relationship with both fractional plasma volume and

vascular permeability (Walker-Samuel Leach and Collins 2006) Both cumulative and

incremental AUC were greater in IPH-positive compared to IPH-negative plaques in the

present study

Concerning other measured DCE-MRI parameters it is unclear as to why the early-late

enhancement rate ratios or times to peak were not different between high and low risk plaque

for either set of criteria However the early-late enhancement rate ratio has not been

previously evaluated for the study of atherosclerosis and atherosclerotic plaque

pathophysiology is likely different from that of prostatic carcinomas the context in which the

94

early-late enhancement rate ratio was previously applied (Isebaert et al 2011) Additionally

though time to peak was noted to occur earlier in vulnerable plaques evaluated by

Phinikaridou et al (2010b) this is the only research to note this qualitative difference during

dynamic MRI performed in rabbits and more work will likely be required to determine the

reason for this difference

An interesting and welcome finding of the current study is that those ROI that were largest

and easiest to identify (total plaque and vessel area) also provided the greatest number of

individually significant measurements between high and low risk plaques In particular

examination of findings in the total plaque area ROI indicates the greatest difference in AUC

among ROI This suggests that carotid plaque vulnerability measurements using DCE-MRI in

the future may be sufficiently evaluated by using automatic selection of ROI that encompass

the entire plaque or vessel wall area potentially reducing errors resulting from manual

drawing of these ROI

62 No Difference Between Carotid Plaques Defined as High and Low

Risk by Clinical Criteria

Nonmodel-based DCE-MRI parameters were not different between high and low risk plaques

classified by clinical criteria Criteria for high risk plaques included ipsilateral symptoms of

cerebral ischemia within 1 year or high grade carotid stenosis gt70 while criteria for

classification as low risk included stenosis lt70 and absence of cerebral ischemic symptoms

Each of these aforementioned high risk criteria is considered an acceptable standard for

clinical stroke risk evaluation and it was therefore hypothesized in the current study that

95

plaques considered high risk by these clinical criteria would demonstrate greater AUC

enhancement rates and early-late enhancement rate ratio than plaques considered to be at

low-risk However this hypothesis is not supported by the current findings

A number of interpretations may account for the observed results One such interpretation is

that carotid artery plaques were improperly classified as high or low risk A conceivable

source of error in this classification scheme is that by necessity the scheme must assume that

cerebral ischemic events are attributed to thromboemboli arising from the ipsilateral carotid

plaque Accordingly several situations are conceivable in which a particular carotid plaque

might be mistakenly identified as the ldquoculpritrdquo lesion

The aforementioned assumption is necessary because it is likely impossible to identify ndash after

the fact ndash the precise route of passage taken by the embolus from its source thus the most

likely source (ipsilateral) must be chosen However owing to collateralization of brain blood

flow provided by the Circle of Willis vessels it is conceivable (though highly unlikely) that

an embolus formed in a particular high risk carotid plaque (for example the right carotid

artery) may travel to the contralateral (left) cerebral hemisphere thereby resulting in an

improper classification of the ipsilateral (left) carotid artery as high risk Not only might this

situation increase the risk of a type I error (false positive) with regard to classification of an

artery as high risk but the risk of a type II error (false negative) in the classification of the

contralateral carotid artery as low risk may also increase

Other more likely sources of error may also result in mischaracterization of low risk carotid

plaques as high risk Although the most complete information available was reviewed when

96

determining carotid artery plaque risk it is possible that other potential embolic sources

mistakenly attributed to carotid plaque were overlooked A common cause of ischemic stroke

and TIA is cardioemboli whereby emboli form in the heart due to a cardiac condition such as

atrial fibrillation or valvular disease and subsequently travel into the brain resulting in

ischemia (Amarenco et al 2009) For this reason it is standard practice during clinical

examination for stroke or TIA to rule out potential sources of cardioemboli prior to

forwarding a diagnosis of symptomatic carotid atherosclerosis (Amarenco et al 2009)

However if a cardioembolic source was mistakenly overlooked ischemic events may have

been attributed to the carotid artery ipsilateral to the event which would have resulted in the

improper classification of that carotid artery as high risk An additional source of cerebral

emboli is intracranial carotid atherosclerosis which may be overlooked on CTA if no

calcification is present the consequences of which would be similar to those for cardioemboli

Thromboemboli are not the sole source of cerebral ischemia Hemodynamic impairment

resulting from carotid stenoses may also lead to ischemic symptoms within the ipsilateral

hemisphere Although not included in the analysis of clinical criteria it is pertinent to note

that of the three occluded carotid arteries examined in this study as part of the imaging

criteria occlusion was associated with cerebral ischemia in only one case This indicates the

existence of considerable inter-subject variability with respect to the impact of carotid

atherosclerosis on cerebral hemodynamics Thus it is possible that classification of some

plaques as high risk may have been inappropriate if symptoms were the result of a

hemodynamic insufficiency rather than thromboembolus however proper risk classification

97

was not possible in the absence of hemodynamic assessment of the cerebral vasculature distal

to the carotid stenosis no such assessment was performed as part of this study

A similar consideration is that asymptomatic carotid arteries exhibiting stenosis gt70 may

not have represented high risk for thromboemboli formation Data from the ACAS study

indicate that patients with asymptomatic carotid stenosis gt60 who undergo CEA benefit

from a 59 reduction in 5-year absolute stroke risk (ACAS Collaborators 1995) indicating

that a small but significant proportion (approximately 5) of asymptomatic plaques causing

gt60 stenosis will become symptomatic within 5 years However it is impossible to

determine from these data whether plaques causing this degree of stenosis represent an

immediate threat or a growing risk A likely scenario is that many of the asymptomatic

plaques enrolled into the ACAS study were in fact low risk at the time of enrollment and

progressed further during the study period to evolve into those at high risk for formation of

thromboemboli however there is unfortunately no evidence to support this contention In

the present study high risk carotid stenosis was defined as gt70 for both symptomatic and

asymptomatic carotid arteries in accordance with data from the NASCET study which

demonstrated greatest benefit for CEA in patients with symptomatic carotid atherosclerosis

causing gt70 stenosis (NASCET Collaborators 1991b) It is possible though that even with

this more stringent criterion for definition of high risk asymptomatic carotid plaque arteries

may have not been at risk for thromboemboli formation thereby resulting in improper

classification of arteries truly at low risk for precipitation of cerebral ischemia

98

A final consideration is that the definition of high risk carotid plaque in the present study

required either high grade stenosis or recent clinical symptoms therefore it was not possible

to determine whether asymptomatic plaques causing lt70 stenosis were at imminent risk for

thromboemboli formation If this were the case plaques at high risk for their first clinical

event may have been improperly classified as low risk

63 Interpretation of Differences in Findings Between Clinical and

Imaging Criteria for Definition of High and Low Risk Carotid

Plaques

Given the multiple opportunities for improper classification of high and low risk vessels by

the clinical criteria discussed above it is possible that no difference exists between the two

groups of patients classified by clinical criteria even after consideration that these analyses

might be underpowered (see 65) Since the conclusion of the NASCET and ECST studies

almost 20 years ago (NASCET Collaborators 1991b ECST Collaborators 1991) imaging

research has evolved a more comprehensive understanding of atherosclerotic plaque risk in

which plaque vulnerability (defined as risk for plaque rupture precipitating symptoms) is the

most important factor for determining ischemic stroke risk However a significant barrier to

progress in this area has been in formulating the specific criteria that provide the best

indication of vulnerability For this reason physicians have no choice but to continue to

utilize the current standard-of-practice criteria available to them for characterization of stroke

risk degree of carotid stenosis Although likely outdated determination of stroke risk by

stenosis remains the only clinical criteria that has been validated by large randomized

controlled trials for both symptomatic (NASCET Collaborators 1991b ECST Collaborators

1991) and asymptomatic patients (ACAS Collaborators 1995)

99

64 Methodological Considerations

This study introduces two novel methodologies for the analysis of nonmodel-based DCE-MRI

of atherosclerosis namely the use of the freely available software package AFNI (Cox 1996)

for voxel-wise curve-fitting of signal intensity time course data and voxel-wise normalization

of the signal intensity time course of carotid artery voxels with respect to the ipsilateral

sternocleidomastoid muscle each discussed below The goals of these applications were two-

fold 1) to overcome the significant ldquobarrier to entryrdquo posed by the mathematical complexity

of model-based approaches for analysis of data from human subjects and 2) to evaluate a

method for standardization of nonmodel-based DCE-MRI to allow for comparison across

future studies

Notwithstanding the current study evaluation of DCE-MRI data in human atherosclerosis has

been limited solely to model-based approaches of the four previous nonmodel-based DCE-

MRI studies of atherosclerosis (Calcagno et al 2008 Calcagno et al 2010 Phinikaridou et al

2010a Phinikaridou et al 2010b) all were performed in rabbit models Unfortunately

model-based approaches appear to be accessible only to those investigators with expertise in

mathematical modeling as it is those investigators who most thoroughly understand the

application of these techniques and the software that must be employed for their application

(whether proprietary or developed ldquoin-houserdquo) On the other hand AFNI software is widely

used for processing of functional MRI data and its use is understood by an accordingly large

number of investigators Therefore for the analysis of nonmodel-based DCE-MRI data

AFNI is likely to be more accessible to researchers and clinicians with little or no experience

in dynamic MRI methods than proprietary software

100

In the present study a curve-fitting algorithm was developed using AFNI to achieve noise

reduction of signal intensity time course data Curve-fitting was achieved by computing the

linear minimum mean squares estimates (LMMSE) between the computed best fit curve and

the measured data Evaluation of the goodness of fit using the coefficient of determination

revealed highly consistent curve-fitting both spatially (high R2 values across different ROI)

and across subjects (high R2 values across the same ROI in different carotid arteries) Overall

fitting of data was excellent the lowest mean R2 computed within any ROI was 09533 (Table

52) suggesting that the overall degree of voxel-level noise during scanning was low If true

future analyses may not require the use of noise fitting to achieve robust measurement of

DCE-MRI parameters thus further improving the accessibility of the technique Regardless

the current findings are in accordance with previous work demonstrating that fitting of DCE-

MRI data by LMMSE is an excellent method for reduction of noise across individual phases

of dynamic scanning (Kerwin Cai and Yuan 2002)

An unfortunate limitation of the DCE-MRI literature in atherosclerosis is that there is

currently no standardization of methods to ensure reliable comparison of results across

studies The choice of a model and its inherent assumptions influences the success and

accuracy of model-based DCE-MRI (Chen et al 2011) Yet since quantitative physiological

parameters such as vp and ktrans

are derived from the data model-fitting failures can be

detected with reasonable certainty by comparison to those values reported in previous studies

Conversely parameters derived from nonmodel-based approaches do not benefit from clear

physiological relevance and thus because the units of the derived parameters and their scale

are dependent on the methods employed for their derivation the onus is upon the investigator

101

to ensure that appropriate methods are followed and reported to allow for future comparison

across studies

In two quantitative nonmodel-based DCE-MRI studies of atherosclerosis conducted in rabbits

(Calcagno et al 2008 Calcagno et al 2010) AUC was derived by integration of the signal

intensity time course which the authors attempted to standardize by subtracting the pre-

contrast baseline signal intensity from post-contrast phases on a voxel-wise basis to ensure the

analysis would not be confounded by the T1-weighted contrast of the images While this was

indeed necessary and was also performed in the present study data in the previous studies

were not normalized with respect to a standard tissue thereby preventing direct comparison of

findings between the aforementioned studies and the present study Yet regardless of whether

reliable comparisons could be made between the present study and those mentioned above it

should be noted that these comparisons would be of limited use since the present study was

performed in humans and those mentioned above were performed in rabbits

In this study voxel-wise normalization of signal intensity was achieved by division of each

post-contrast phase by the mean baseline signal intensity of 100 sternocleidomastoid muscle

voxels The sternocleidomastoid muscle was chosen as an appropriate reference tissue

because it is routinely used for clinical and research purposes to define MRI signal hyper- and

hypointensities Additionally the T1 and T2 properties of muscle and hence its appearance

on T1- and T2-weighted MRI are unlikely to be related to the presence of atherosclerotic

disease among radiation-naive subjects (previously irradiated arteries were excluded from

evaluation in this study) To allow for comparison across nonmodel-based DCE-MRI studies

102

in the future including comparison to the present study I suggest that this method of signal

intensity normalization be adopted

An important consideration when applying this standardization is that a calibration must be

completed to allow for evaluation and correction of MRI signal intensity spatial variations due

to limited penetration of the surface coils If this calibration is not conducted then the authors

risk confounding their data by introducing variation in measured signal intensity (and

subsequently DCE-MRI parameters) that is dependent upon the depth of the carotid arteries

and the sternocleidomastoid muscle with respect to the neck surface The impact of this

should not be underestimated especially since considerable anatomical variation in carotid

artery depth exists among subjects In the present study spatial variations in MRI signal

intensity due to coil penetration were corrected a priori using proprietary technology (PURE

GE Healthcare) however numerous analogous technologies for a priori signal intensity

calibration are available through a number of MRI vendors Additional post hoc calibration is

also possible if an appropriately low-resolution scan has been acquired prior to DCE-MRI

scanning

65 Study Limitations

The most significant limitation of this study is the large number of subjects arteries that were

excluded from analysis due to 1) insufficient imaging quality due to subject motion within the

MRI (n=4) 2) incomplete MRI scanning due to premature scan termination by the patient

(n=4) 3) previous neck radiation therapy (n=1) 4) previous carotid endarterectomy (n=1) or

5) carotid artery occlusion (clinical criteria only n=3) Together these constitute 13 of the

103

possible 28 arteries (14 subjects total) of those who provided written informed consent and

who underwent MRI An additional 18 subjects (36 arteries) were approached to participate

but declined enrollment Despite this limitation however significant and consistent

differences (such as those measured within AUC which demonstrated significance in every

post-contrast minute) were observed between high (n=9) and low (n=9) risk plaques defined

by imaging criteria suggesting that sufficient data were also collected to have detected a

difference between high (n=8) and low (n=7) risk plaques defined by clinical criteria

A similar limitation was that only a low proportion (44) of subjects approached for study

enrollment (n=32) provided written informed consent (n=14) The potential number of

carotid arteries (n=36) lost in this regard was therefore substantial Although the demographic

information and clinical history of subjects declining enrollment were not recorded it was

noted that these subjects tended to be those most recently admitted to hospital for stroke or

TIA especially for those most severely impacted by sensory motor or neurological

impairments Candid conversations with potential subjects and their families revealed the

most common concern to be the anticipated length of the imaging protocol (approximately 60-

70 minutes) It was also noted that these subjects were more likely to decline study

enrollment if a number of MRI or CT scans had been recently performed for diagnostic

purposes Considerations of these factors will likely prove fruitful when attempting to

increase the proportion of successfully enrolled subjects in future studies

No previous study has attempted to compare atherosclerotic plaques at high and low risk for

precipitating ischemic events using DCE-MRI analysis techniques Therefore a substantial

104

limitation of the present study was that an a priori sample size calculation could not be

performed thus raising the concern that statistical power was insufficient to detect a

significant difference between high and low risk plaques defined by the clinical criteria

However post hoc analysis of data derived from cumulative AUC at the 7th

post-contrast

minute in this criteria grouping revealed that a sample size of 8 subjects was sufficient for

detection of significant differences between risk groups Given that this sample size

requirement (8 high risk and 8 low risk arteries) is very close to the actual number of subjects

used in the present study (8 high risk and 7 low risk arteries) it is likely that the data of the

present study were sufficiently powered to detect a significant difference although none was

found

In a study by Calcagno et al (2010 published after the current research was initiated) in

which the authors conducted reproducibility studies of the nonmodel-based DCE-MRI

parameter AUC reproducibility data were utilized to estimate required sample sizes for a

range of estimated differences in cumulative AUC between groups measured within the 7th

post-contrast minute and summarized in Figure 8 of that publication In that data estimated

required sample sizes to detect a statistically significant result ranged from 5 subjects to detect

a 35 difference to 31 subjects to detect a 10 difference Interestingly a sample size of

only 7 subjects was found to be sufficiently powered to detect significant differences between

groups of greater than 20 (Calcagno et al 2010) The data of those authors therefore

suggest that the difference in DCE-MRI parameters of high and low risk plaques classified by

clinical criteria was less than 20

105

A further limitation of the current study arises from the method of DCE-MRI itself and the

application of nonmodel-based analysis in particular Due to the requirement for black-blood

imaging to allow for delineation of the carotid vessel wall the DCE-MRI acquisition was

limited to only one slice of coverage This resulted from the temporal constraints imposed by

the combination of T1-weighted imaging 2D fast spin-echo technique cardiac gating and

DIR for post-contrast blood suppression To ensure that the most pertinent information was

gathered the one available DCE-MRI imaging slice was prescribed through the region of

greatest plaque extent However the statistical power of the present studys data would be

increased greatly if MRI protocol were improved to allow for increased number of slice

prescriptions while maintaining temporal resolution such that the entire plaque volume could

be studied Improvements of this kind would thus allow for more comprehensive evaluation

of spatial differences in DCE-MRI parameters within the same plaque

An additional limitation of the current study is that ROI were drawn manually in each carotid

artery Although the vast majority of data processing for this study was performed using

highly automated Unix-based scripting it is possible that some ROI were drawn improperly

especially the smaller ROI This may have led to errors in the accuracy of identification of

the plaque fibrous cap and vasa vasorum ROI which may in turn provide a clue as to why

significant differences were not identified between most of the DCE-MRI parameters in either

risk classification scheme for these ROI It should be noted however that inaccuracies in the

drawing of ROI represent a systematic error since the same ROI were used for comparison of

high and low risk arteries in both classification schema this potential source of error cannot

account for the differences in results observed between these two classifications

106

66 Future Directions

Due to the novelty of many aspects of this thesis future studies concerned with the

examination of nonmodel-based DCE-MRI of atherosclerosis should attempt to corroborate

the major findings of the present work

Given the information gained from the present study regarding the low proportion of

successfully-enrolled subjects feasibility of future work may be substantially increased by

reducing the length of the scan protocol thus addressing a major concern of those subjects

declining to participate in the present study Overall scan length may be reduced by

decreasing both the total number of scanning series and the length of the DCE-MRI series

itself A revised scanning protocol would likely include only those pulse sequences necessary

for evaluation of IPH status and derivation of DCE-MRI parameters Additionally given that

significant differences between cumulative and incremental AUC were detected within the

second post-contrast minute of scanning it is likely that future studies may reduce the length

of the DCE-MRI scan without impacting the sensitivity of the technique These changes

could reduce the protocol length to approximately 30 minutes from its current length of 60-70

minutes Similarly the feasibility of future studies could also be increased by trading DCE-

MRI temporal resolution for increased slice coverage In this way the robustness of the data

can be maintained while enabling greater coverage of the carotid atherosclerotic plaque

Further to this studies should be designed so as to allow for comparison between model- and

nonmodel-based methods for analysis of DCE-MRI data I suggest the implementation of a

prospective randomized cross-over study in which DCE-MRI scanning is performed in

107

subjects with carotid atherosclerosis on two separate occasions on the first of which the

subject would be randomized to begin DCE-MRI analysis with either the model- or

nonmodel-based approach Where possible imaging findings would be validated through the

use of histological examination of subject endarterectomy specimens This study would

require the development of an additional MRI protocol and analysis method for analysis of

model-based DCE-MRI data however comparison between these methods might prove

invaluable for determining the true physiological relevance of nonmodel-based parameters

which thus far remains unclear even in light of the present work

In addition a prospective observational study to evaluate the relationship between nonmodel-

based DCE-MRI parameters and the presence of IPH in subjects with asymptomatic carotid

atherosclerosis may yield new information regarding best practices for evaluation of stroke

risk Although current MRI methods are able to accurately distinguish the presence of IPH

(and therefore vulnerable plaque) none have yet been able to quantifiably demonstrate stroke

risk As demonstrated in the present work AUC early and late enhancement rate and peak

enhancement are all increased in IPH-positive plaque and it is also known that presence of

IPH is associated with stroke risk (Kolodgie et al 2003 Vermani et al 2005) however no

quantitative measure for the severity of vulnerability currently exists Application of the

current nonmodel-based DCE-MRI technique for the evaluation and long-term observation of

asymptomatic plaques may identify a quantitative threshold for stroke risk in IPH-positive

plaques which would allow physicians to tailor their management of carotid atherosclerosis

patients according to an accurate and reproducible metric of absolute stroke risk

108

67 Conclusions

This thesis provides the first application of nonmodel-based DCE-MRI for the evaluation of

carotid atherosclerotic plaque in humans and demonstrates the usefulness of this technique

for the discrimination of high versus low risk carotid plaque based on the presence of IPH In

contrast DCE-MRI was not able to discriminate between high versus low risk plaques

defined by current criteria for clinical assessment of ischemic stroke risk presumably due to

the inconsistent relationship between the degree of luminal stenosis imposed by carotid

plaque and its vulnerability as well as the inability of this method to reliably differentiate

between embolic and hemodynamic events These findings highlight the need for

reassessment of current stroke risk evaluation paradigms and provide justification for a

change in focus of these assessments from clinical- to imaging-based methods for better

evaluation of plaque vulnerability

109

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Insull W Jr The pathology of atherosclerosis plaque development and plaque responses to

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ii

Nonmodel-Based Dynamic Contrast-Enhanced Magnetic

Resonance Imaging for the Assessment of High versus Low

Risk Carotid Atherosclerosis


Master of Science


2011

Abstract

Background Parameters of carotid atherosclerosis dynamic contrast-enhanced MRI (DCE-

MRI) are associated with stroke risk indices but studies have only evaluated symptomatic

arteries I hypothesized that DCE-MRI parameters are different between carotid

atherosclerotic plaques at high and low risk for precipitating ischemic stroke Methods High

and low risk carotid plaques undergoing nonmodel-based DCE-MRI (n=18) were compared

using two independent schema 1) clinical standard (high risk defined as ipsilateral strokeTIA

lt1 week old or stenosis gt70) 2) MRI standard (high risk defined as presence of intraplaque

hemorrhage [IPH]) Results IPH-positive plaques (n=9) exhibited greater area under the

curve early and late enhancement rate and peak enhancement than IPH-negative plaques

(n=9) (plt005 for all) High (n=8) and low (n=7) risk plaques defined by clinical criteria were

not differentiated by any DCE-MRI parameters Conclusions Nonmodel-based DCE-MRI

discriminates high versus low risk carotid plaque based on the presence of IPH but not by

clinical criteria

iii

Acknowledgments






mentor



Crawley








iv





this project





project









v





love and support

vi

Dedication


vii

Table of Contents

Acknowledgments iii


List of Tables x

List of Figures xi








22 Atherogenesis 9









24 Stroke 18











viii




31 Study Aims 47

32 Hypothesis 48


41 Study Design 50























454 Time to Peak 73






ix


















67 Conclusions 108

References 109

x

List of Tables







xi

List of Figures











hemorrhage






xii

List of Equations







xiii



2D Two Dimensional







AT2 Angiotensin II


CA Contrast Agent











xiv




ET Endothelin



FSE Fast Spin Echo
















xv

NO Nitric Oxide




p Probability

PD Proton Density




RF Radiofrequency



SI Signal Intensity







TE Echo Time

TI Inversion Time


xvi

TOF Time of Flight


TR Repetition Time

TTP Time to Peak




1






















2























3








4


21 Vascular Anatomy




















5

















mediated pathway





6














carotid arteries






or 5th



7








carotid body











8










9

22 Atherogenesis






















10























11























12









13






14























15














2311 Early Lesions









16





et al 1992)


















17























18





subsections

24 Stroke






2006 HSFC 2011b)

242 Types of Stroke









19








risk

251 Clinical Trials














20






















21























22



















relaxation time T1



23























24






















25


















26











Chu et al (2004)















2010)

27






















28























29























30














alone









31






2821 General Theory
















assumptions

32























33





34








space

35






[21]




seconds-1

and Ctissue is the









which describes the


2) the



36



[22]







model



ktrans









37










atherosclerosis

38









negligible

39












artery wall voxels











40























41
























42






















43









with macrophage



















44


were found for all











imaging













45















F-FDG) uptake and









46




47


31 Study Aims



















48



32 Hypothesis




















49



50

Chapter 4 Methods

41 Study Design
















51





atherosclerosis










02 63 M Included No



05 78 M Included No

06 76 M Included No




10 59 F Included No

11 67 M No





ExcludedDagger

ExcludedDagger

Excludeddagger

52

















population





53


detailed in 54







criteria













54













55








56























57











58

Series number 1 2 3 4 5 6 7 8 9






Number of slices 33 38 40 10 10 50 1 1 10




Field of view (mm) 230 320 180 170 170 230 140 140 170




Echo time TE (ms) 15 21 37 85 89 21 56 56 89





Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250







Trigger delay (ms) 511 511 511 250 250




Total Phases 48




Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout


Pre-DCE base

Post-contrast T1

59





















60






















61




















62























63























64



[41]














were not corrected




65






















66




















[42]

67

























68




69







signal intensity

70







71












[43]








72

[44]





(Figure 43D)










(Figure 43E)




(Figure 43F)

73

454 Time to Peak




data (Figure 43G)










minutes-1

(Figure 43H)







74




















462 Group Analysis



75









76





77










78





determination

79

Chapter 5 Results
















defined above

80


































81



















82










83










0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84










0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85







3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15


Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86







10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87







-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88





















89




02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874



90


















91








vulnerability





ROI Time to peak








Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92





















93



2005)













present study






94






















95






















96






















97





















98








Plaques
















99










future studies













100
























101


across studies




















102















scanning







103






















104





post-contrast






found












105























106






















107






















108

67 Conclusions












109

References




2007581157-1163







200847337-342




November 9 2009







Stroke 198920844-849







1425



Vasc Surg 199726585-594

110


201087262-271






















2008281311-1317





February 2009





111






print]



Imaging 200930864-872






2003501199-1208


















112






1994231562-1569


Sci 598 17-28 1990






1375











Sunderland MA 2004









113



1998982108-2116















plaque Circ 2003107851-856



2006241459-468





Imaging 20093035-40





Opin Lipidol 12 383-389 2001




114



20041091016-1021



Stroke 2006371103-1105







2009351436-1442














201031168-176



201083729-735






115




Group JAMA 19912663289-3294







1409




20031073047-3052


Surg 19948403-408



Engl J Med 1991a325445-453







Stroke 1991b22816-817







Imaging 2010a3323-332

116







200641213-221



Stroke 1992231752-1760










200757874-880





200944839-848









117





1992











200950S376-S381




Imaging 1997791-101







367







118







Phys Med Biol 2010551719-1734












Med Biol Soc 2006110291031





Cannon RO 3rd









1996653-57



119







107














2008271309-1316

iii

Acknowledgments






mentor



Crawley








iv





this project





project









v





love and support

vi

Dedication


vii

Table of Contents

Acknowledgments iii


List of Tables x

List of Figures xi








22 Atherogenesis 9









24 Stroke 18











viii




31 Study Aims 47

32 Hypothesis 48


41 Study Design 50























454 Time to Peak 73






ix


















67 Conclusions 108

References 109

x

List of Tables







xi

List of Figures











hemorrhage






xii

List of Equations







xiii



2D Two Dimensional







AT2 Angiotensin II


CA Contrast Agent











xiv




ET Endothelin



FSE Fast Spin Echo
















xv

NO Nitric Oxide




p Probability

PD Proton Density




RF Radiofrequency



SI Signal Intensity







TE Echo Time

TI Inversion Time


xvi

TOF Time of Flight


TR Repetition Time

TTP Time to Peak




1






















2























3








4


21 Vascular Anatomy




















5

















mediated pathway





6














carotid arteries






or 5th



7








carotid body











8










9

22 Atherogenesis






















10























11























12









13






14























15














2311 Early Lesions









16





et al 1992)


















17























18





subsections

24 Stroke






2006 HSFC 2011b)

242 Types of Stroke









19








risk

251 Clinical Trials














20






















21























22



















relaxation time T1



23























24






















25


















26











Chu et al (2004)















2010)

27






















28























29























30














alone









31






2821 General Theory
















assumptions

32























33





34








space

35






[21]




seconds-1

and Ctissue is the









which describes the


2) the



36



[22]







model



ktrans









37










atherosclerosis

38









negligible

39












artery wall voxels











40























41
























42






















43









with macrophage



















44


were found for all











imaging













45















F-FDG) uptake and









46




47


31 Study Aims



















48



32 Hypothesis




















49



50

Chapter 4 Methods

41 Study Design
















51





atherosclerosis










02 63 M Included No



05 78 M Included No

06 76 M Included No




10 59 F Included No

11 67 M No





ExcludedDagger

ExcludedDagger

Excludeddagger

52

















population





53


detailed in 54







criteria













54













55








56























57











58

Series number 1 2 3 4 5 6 7 8 9






Number of slices 33 38 40 10 10 50 1 1 10




Field of view (mm) 230 320 180 170 170 230 140 140 170




Echo time TE (ms) 15 21 37 85 89 21 56 56 89





Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250







Trigger delay (ms) 511 511 511 250 250




Total Phases 48




Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout


Pre-DCE base

Post-contrast T1

59





















60






















61




















62























63























64



[41]














were not corrected




65






















66




















[42]

67

























68




69







signal intensity

70







71












[43]








72

[44]





(Figure 43D)










(Figure 43E)




(Figure 43F)

73

454 Time to Peak




data (Figure 43G)










minutes-1

(Figure 43H)







74




















462 Group Analysis



75









76





77










78





determination

79

Chapter 5 Results
















defined above

80


































81



















82










83










0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84










0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85







3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15


Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86







10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87







-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88





















89




02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874



90


















91








vulnerability





ROI Time to peak








Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92





















93



2005)













present study






94






















95






















96






















97





















98








Plaques
















99










future studies













100
























101


across studies




















102















scanning







103






















104





post-contrast






found












105























106






















107






















108

67 Conclusions












109

References




2007581157-1163







200847337-342




November 9 2009







Stroke 198920844-849







1425



Vasc Surg 199726585-594

110


201087262-271






















2008281311-1317





February 2009





111






print]



Imaging 200930864-872






2003501199-1208


















112






1994231562-1569


Sci 598 17-28 1990






1375











Sunderland MA 2004









113



1998982108-2116















plaque Circ 2003107851-856



2006241459-468





Imaging 20093035-40





Opin Lipidol 12 383-389 2001




114



20041091016-1021



Stroke 2006371103-1105







2009351436-1442














201031168-176



201083729-735






115




Group JAMA 19912663289-3294







1409




20031073047-3052


Surg 19948403-408



Engl J Med 1991a325445-453







Stroke 1991b22816-817







Imaging 2010a3323-332

116







200641213-221



Stroke 1992231752-1760










200757874-880





200944839-848









117





1992











200950S376-S381




Imaging 1997791-101







367







118







Phys Med Biol 2010551719-1734












Med Biol Soc 2006110291031





Cannon RO 3rd









1996653-57



119







107














2008271309-1316

iv





this project





project









v





love and support

vi

Dedication


vii

Table of Contents

Acknowledgments iii


List of Tables x

List of Figures xi








22 Atherogenesis 9









24 Stroke 18











viii




31 Study Aims 47

32 Hypothesis 48


41 Study Design 50























454 Time to Peak 73






ix


















67 Conclusions 108

References 109

x

List of Tables







xi

List of Figures











hemorrhage






xii

List of Equations







xiii



2D Two Dimensional







AT2 Angiotensin II


CA Contrast Agent











xiv




ET Endothelin



FSE Fast Spin Echo
















xv

NO Nitric Oxide




p Probability

PD Proton Density




RF Radiofrequency



SI Signal Intensity







TE Echo Time

TI Inversion Time


xvi

TOF Time of Flight


TR Repetition Time

TTP Time to Peak




1






















2























3








4


21 Vascular Anatomy




















5

















mediated pathway





6














carotid arteries






or 5th



7








carotid body











8










9

22 Atherogenesis






















10























11























12









13






14























15














2311 Early Lesions









16





et al 1992)


















17























18





subsections

24 Stroke






2006 HSFC 2011b)

242 Types of Stroke









19








risk

251 Clinical Trials














20






















21























22



















relaxation time T1



23























24






















25


















26











Chu et al (2004)















2010)

27






















28























29























30














alone









31






2821 General Theory
















assumptions

32























33





34








space

35






[21]




seconds-1

and Ctissue is the









which describes the


2) the



36



[22]







model



ktrans









37










atherosclerosis

38









negligible

39












artery wall voxels











40























41
























42






















43









with macrophage



















44


were found for all











imaging













45















F-FDG) uptake and









46




47


31 Study Aims



















48



32 Hypothesis




















49



50

Chapter 4 Methods

41 Study Design
















51





atherosclerosis










02 63 M Included No



05 78 M Included No

06 76 M Included No




10 59 F Included No

11 67 M No





ExcludedDagger

ExcludedDagger

Excludeddagger

52

















population





53


detailed in 54







criteria













54













55








56























57











58

Series number 1 2 3 4 5 6 7 8 9






Number of slices 33 38 40 10 10 50 1 1 10




Field of view (mm) 230 320 180 170 170 230 140 140 170




Echo time TE (ms) 15 21 37 85 89 21 56 56 89





Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250







Trigger delay (ms) 511 511 511 250 250




Total Phases 48




Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout


Pre-DCE base

Post-contrast T1

59





















60






















61




















62























63























64



[41]














were not corrected




65






















66




















[42]

67

























68




69







signal intensity

70







71












[43]








72

[44]





(Figure 43D)










(Figure 43E)




(Figure 43F)

73

454 Time to Peak




data (Figure 43G)










minutes-1

(Figure 43H)







74




















462 Group Analysis



75









76





77










78





determination

79

Chapter 5 Results
















defined above

80


































81



















82










83










0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84










0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85







3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15


Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86







10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87







-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88





















89




02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874



90


















91








vulnerability





ROI Time to peak








Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92





















93



2005)













present study






94






















95






















96






















97





















98








Plaques
















99










future studies













100
























101


across studies




















102















scanning







103






















104





post-contrast






found












105























106






















107






















108

67 Conclusions












109

References




2007581157-1163







200847337-342




November 9 2009







Stroke 198920844-849







1425



Vasc Surg 199726585-594

110


201087262-271






















2008281311-1317





February 2009





111






print]



Imaging 200930864-872






2003501199-1208


















112






1994231562-1569


Sci 598 17-28 1990






1375











Sunderland MA 2004









113



1998982108-2116















plaque Circ 2003107851-856



2006241459-468





Imaging 20093035-40





Opin Lipidol 12 383-389 2001




114



20041091016-1021



Stroke 2006371103-1105







2009351436-1442














201031168-176



201083729-735






115




Group JAMA 19912663289-3294







1409




20031073047-3052


Surg 19948403-408



Engl J Med 1991a325445-453







Stroke 1991b22816-817







Imaging 2010a3323-332

116







200641213-221



Stroke 1992231752-1760










200757874-880





200944839-848









117





1992











200950S376-S381




Imaging 1997791-101







367







118







Phys Med Biol 2010551719-1734












Med Biol Soc 2006110291031





Cannon RO 3rd









1996653-57



119







107














2008271309-1316

v





love and support

vi

Dedication


vii

Table of Contents

Acknowledgments iii


List of Tables x

List of Figures xi








22 Atherogenesis 9









24 Stroke 18











viii




31 Study Aims 47

32 Hypothesis 48


41 Study Design 50























454 Time to Peak 73






ix


















67 Conclusions 108

References 109

x

List of Tables







xi

List of Figures











hemorrhage






xii

List of Equations







xiii



2D Two Dimensional







AT2 Angiotensin II


CA Contrast Agent











xiv




ET Endothelin



FSE Fast Spin Echo
















xv

NO Nitric Oxide




p Probability

PD Proton Density




RF Radiofrequency



SI Signal Intensity







TE Echo Time

TI Inversion Time


xvi

TOF Time of Flight


TR Repetition Time

TTP Time to Peak




1






















2























3








4


21 Vascular Anatomy




















5

















mediated pathway





6














carotid arteries






or 5th



7








carotid body











8










9

22 Atherogenesis






















10























11























12









13






14























15














2311 Early Lesions









16





et al 1992)


















17























18





subsections

24 Stroke






2006 HSFC 2011b)

242 Types of Stroke









19








risk

251 Clinical Trials














20






















21























22



















relaxation time T1



23























24






















25


















26











Chu et al (2004)















2010)

27






















28























29























30














alone









31






2821 General Theory
















assumptions

32























33





34








space

35






[21]




seconds-1

and Ctissue is the









which describes the


2) the



36



[22]







model



ktrans









37










atherosclerosis

38









negligible

39












artery wall voxels











40























41
























42






















43









with macrophage



















44


were found for all











imaging













45















F-FDG) uptake and









46




47


31 Study Aims



















48



32 Hypothesis




















49



50

Chapter 4 Methods

41 Study Design
















51





atherosclerosis










02 63 M Included No



05 78 M Included No

06 76 M Included No




10 59 F Included No

11 67 M No





ExcludedDagger

ExcludedDagger

Excludeddagger

52

















population





53


detailed in 54







criteria













54













55








56























57











58

Series number 1 2 3 4 5 6 7 8 9






Number of slices 33 38 40 10 10 50 1 1 10




Field of view (mm) 230 320 180 170 170 230 140 140 170




Echo time TE (ms) 15 21 37 85 89 21 56 56 89





Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250







Trigger delay (ms) 511 511 511 250 250




Total Phases 48




Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout


Pre-DCE base

Post-contrast T1

59





















60






















61




















62























63























64



[41]














were not corrected




65






















66




















[42]

67

























68




69







signal intensity

70







71












[43]








72

[44]





(Figure 43D)










(Figure 43E)




(Figure 43F)

73

454 Time to Peak




data (Figure 43G)










minutes-1

(Figure 43H)







74




















462 Group Analysis



75









76





77










78





determination

79

Chapter 5 Results
















defined above

80


































81



















82










83










0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84










0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85







3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15


Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86







10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87







-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88





















89




02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874



90


















91








vulnerability





ROI Time to peak








Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92





















93



2005)













present study






94






















95






















96






















97





















98








Plaques
















99










future studies













100
























101


across studies




















102















scanning







103






















104





post-contrast






found












105























106






















107






















108

67 Conclusions












109

References




2007581157-1163







200847337-342




November 9 2009







Stroke 198920844-849







1425



Vasc Surg 199726585-594

110


201087262-271






















2008281311-1317





February 2009





111






print]



Imaging 200930864-872






2003501199-1208


















112






1994231562-1569


Sci 598 17-28 1990






1375











Sunderland MA 2004









113



1998982108-2116















plaque Circ 2003107851-856



2006241459-468





Imaging 20093035-40





Opin Lipidol 12 383-389 2001




114



20041091016-1021



Stroke 2006371103-1105







2009351436-1442














201031168-176



201083729-735






115




Group JAMA 19912663289-3294







1409




20031073047-3052


Surg 19948403-408



Engl J Med 1991a325445-453







Stroke 1991b22816-817







Imaging 2010a3323-332

116







200641213-221



Stroke 1992231752-1760










200757874-880





200944839-848









117





1992











200950S376-S381




Imaging 1997791-101







367







118







Phys Med Biol 2010551719-1734












Med Biol Soc 2006110291031





Cannon RO 3rd









1996653-57



119







107














2008271309-1316

vi

Dedication


vii

Table of Contents

Acknowledgments iii


List of Tables x

List of Figures xi








22 Atherogenesis 9









24 Stroke 18











viii




31 Study Aims 47

32 Hypothesis 48


41 Study Design 50























454 Time to Peak 73






ix


















67 Conclusions 108

References 109

x

List of Tables







xi

List of Figures











hemorrhage






xii

List of Equations







xiii



2D Two Dimensional







AT2 Angiotensin II


CA Contrast Agent











xiv




ET Endothelin



FSE Fast Spin Echo
















xv

NO Nitric Oxide




p Probability

PD Proton Density




RF Radiofrequency



SI Signal Intensity







TE Echo Time

TI Inversion Time


xvi

TOF Time of Flight


TR Repetition Time

TTP Time to Peak




1






















2























3








4


21 Vascular Anatomy




















5

















mediated pathway





6














carotid arteries






or 5th



7








carotid body











8










9

22 Atherogenesis






















10























11























12









13






14























15














2311 Early Lesions









16





et al 1992)


















17























18





subsections

24 Stroke






2006 HSFC 2011b)

242 Types of Stroke









19








risk

251 Clinical Trials














20






















21























22



















relaxation time T1



23























24






















25


















26











Chu et al (2004)















2010)

27






















28























29























30














alone









31






2821 General Theory
















assumptions

32























33





34








space

35






[21]




seconds-1

and Ctissue is the









which describes the


2) the



36



[22]







model



ktrans









37










atherosclerosis

38









negligible

39












artery wall voxels











40























41
























42






















43









with macrophage



















44


were found for all











imaging













45















F-FDG) uptake and









46




47


31 Study Aims



















48



32 Hypothesis




















49



50

Chapter 4 Methods

41 Study Design
















51





atherosclerosis










02 63 M Included No



05 78 M Included No

06 76 M Included No




10 59 F Included No

11 67 M No





ExcludedDagger

ExcludedDagger

Excludeddagger

52

















population





53


detailed in 54







criteria













54













55








56























57











58

Series number 1 2 3 4 5 6 7 8 9






Number of slices 33 38 40 10 10 50 1 1 10




Field of view (mm) 230 320 180 170 170 230 140 140 170




Echo time TE (ms) 15 21 37 85 89 21 56 56 89





Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250







Trigger delay (ms) 511 511 511 250 250




Total Phases 48




Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout


Pre-DCE base

Post-contrast T1

59





















60






















61




















62























63























64



[41]














were not corrected




65






















66




















[42]

67

























68




69







signal intensity

70







71












[43]








72

[44]





(Figure 43D)










(Figure 43E)




(Figure 43F)

73

454 Time to Peak




data (Figure 43G)










minutes-1

(Figure 43H)







74




















462 Group Analysis



75









76





77










78





determination

79

Chapter 5 Results
















defined above

80


































81



















82










83










0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84










0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85







3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15


Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86







10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87







-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88





















89




02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874



90


















91








vulnerability





ROI Time to peak








Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92





















93



2005)













present study






94






















95






















96






















97





















98








Plaques
















99










future studies













100
























101


across studies




















102















scanning







103






















104





post-contrast






found












105























106






















107






















108

67 Conclusions












109

References




2007581157-1163







200847337-342




November 9 2009







Stroke 198920844-849







1425



Vasc Surg 199726585-594

110


201087262-271






















2008281311-1317





February 2009





111






print]



Imaging 200930864-872






2003501199-1208


















112






1994231562-1569


Sci 598 17-28 1990






1375











Sunderland MA 2004









113



1998982108-2116















plaque Circ 2003107851-856



2006241459-468





Imaging 20093035-40





Opin Lipidol 12 383-389 2001




114



20041091016-1021



Stroke 2006371103-1105







2009351436-1442














201031168-176



201083729-735






115




Group JAMA 19912663289-3294







1409




20031073047-3052


Surg 19948403-408



Engl J Med 1991a325445-453







Stroke 1991b22816-817







Imaging 2010a3323-332

116







200641213-221



Stroke 1992231752-1760










200757874-880





200944839-848









117





1992











200950S376-S381




Imaging 1997791-101







367







118







Phys Med Biol 2010551719-1734












Med Biol Soc 2006110291031





Cannon RO 3rd









1996653-57



119







107














2008271309-1316

vii

Table of Contents

Acknowledgments iii


List of Tables x

List of Figures xi








22 Atherogenesis 9









24 Stroke 18











viii




31 Study Aims 47

32 Hypothesis 48


41 Study Design 50























454 Time to Peak 73






ix


















67 Conclusions 108

References 109

x

List of Tables







xi

List of Figures











hemorrhage






xii

List of Equations







xiii



2D Two Dimensional







AT2 Angiotensin II


CA Contrast Agent











xiv




ET Endothelin



FSE Fast Spin Echo
















xv

NO Nitric Oxide




p Probability

PD Proton Density




RF Radiofrequency



SI Signal Intensity







TE Echo Time

TI Inversion Time


xvi

TOF Time of Flight


TR Repetition Time

TTP Time to Peak




1






















2























3








4


21 Vascular Anatomy




















5

















mediated pathway





6














carotid arteries






or 5th



7








carotid body











8










9

22 Atherogenesis






















10























11























12









13






14























15














2311 Early Lesions









16





et al 1992)


















17























18





subsections

24 Stroke






2006 HSFC 2011b)

242 Types of Stroke









19








risk

251 Clinical Trials














20






















21























22



















relaxation time T1



23























24






















25


















26











Chu et al (2004)















2010)

27






















28























29























30














alone









31






2821 General Theory
















assumptions

32























33





34








space

35






[21]




seconds-1

and Ctissue is the









which describes the


2) the



36



[22]







model



ktrans









37










atherosclerosis

38









negligible

39












artery wall voxels











40























41
























42






















43









with macrophage



















44


were found for all











imaging













45















F-FDG) uptake and









46




47


31 Study Aims



















48



32 Hypothesis




















49



50

Chapter 4 Methods

41 Study Design
















51





atherosclerosis










02 63 M Included No



05 78 M Included No

06 76 M Included No




10 59 F Included No

11 67 M No





ExcludedDagger

ExcludedDagger

Excludeddagger

52

















population





53


detailed in 54







criteria













54













55








56























57











58

Series number 1 2 3 4 5 6 7 8 9






Number of slices 33 38 40 10 10 50 1 1 10




Field of view (mm) 230 320 180 170 170 230 140 140 170




Echo time TE (ms) 15 21 37 85 89 21 56 56 89





Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250







Trigger delay (ms) 511 511 511 250 250




Total Phases 48




Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout


Pre-DCE base

Post-contrast T1

59





















60






















61




















62























63























64



[41]














were not corrected




65






















66




















[42]

67

























68




69







signal intensity

70







71












[43]








72

[44]





(Figure 43D)










(Figure 43E)




(Figure 43F)

73

454 Time to Peak




data (Figure 43G)










minutes-1

(Figure 43H)







74




















462 Group Analysis



75









76





77










78





determination

79

Chapter 5 Results
















defined above

80


































81



















82










83










0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84










0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85







3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15


Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86







10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87







-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88





















89




02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874



90


















91








vulnerability





ROI Time to peak








Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92





















93



2005)













present study






94






















95






















96






















97





















98








Plaques
















99










future studies













100
























101


across studies




















102















scanning







103






















104





post-contrast






found












105























106






















107






















108

67 Conclusions












109

References




2007581157-1163







200847337-342




November 9 2009







Stroke 198920844-849







1425



Vasc Surg 199726585-594

110


201087262-271






















2008281311-1317





February 2009





111






print]



Imaging 200930864-872






2003501199-1208


















112






1994231562-1569


Sci 598 17-28 1990






1375











Sunderland MA 2004









113



1998982108-2116















plaque Circ 2003107851-856



2006241459-468





Imaging 20093035-40





Opin Lipidol 12 383-389 2001




114



20041091016-1021



Stroke 2006371103-1105







2009351436-1442














201031168-176



201083729-735






115




Group JAMA 19912663289-3294







1409




20031073047-3052


Surg 19948403-408



Engl J Med 1991a325445-453







Stroke 1991b22816-817







Imaging 2010a3323-332

116







200641213-221



Stroke 1992231752-1760










200757874-880





200944839-848









117





1992











200950S376-S381




Imaging 1997791-101







367







118







Phys Med Biol 2010551719-1734












Med Biol Soc 2006110291031





Cannon RO 3rd









1996653-57



119







107














2008271309-1316

viii




31 Study Aims 47

32 Hypothesis 48


41 Study Design 50























454 Time to Peak 73






ix


















67 Conclusions 108

References 109

x

List of Tables







xi

List of Figures











hemorrhage






xii

List of Equations







xiii



2D Two Dimensional







AT2 Angiotensin II


CA Contrast Agent











xiv




ET Endothelin



FSE Fast Spin Echo
















xv

NO Nitric Oxide




p Probability

PD Proton Density




RF Radiofrequency



SI Signal Intensity







TE Echo Time

TI Inversion Time


xvi

TOF Time of Flight


TR Repetition Time

TTP Time to Peak




1






















2























3








4


21 Vascular Anatomy




















5

















mediated pathway





6














carotid arteries






or 5th



7








carotid body











8










9

22 Atherogenesis






















10























11























12









13






14























15














2311 Early Lesions









16





et al 1992)


















17























18





subsections

24 Stroke






2006 HSFC 2011b)

242 Types of Stroke









19








risk

251 Clinical Trials














20






















21























22



















relaxation time T1



23























24






















25


















26











Chu et al (2004)















2010)

27






















28























29























30














alone









31






2821 General Theory
















assumptions

32























33





34








space

35






[21]




seconds-1

and Ctissue is the









which describes the


2) the



36



[22]







model



ktrans









37










atherosclerosis

38









negligible

39












artery wall voxels











40























41
























42






















43









with macrophage



















44


were found for all











imaging













45















F-FDG) uptake and









46




47


31 Study Aims



















48



32 Hypothesis




















49



50

Chapter 4 Methods

41 Study Design
















51





atherosclerosis










02 63 M Included No



05 78 M Included No

06 76 M Included No




10 59 F Included No

11 67 M No





ExcludedDagger

ExcludedDagger

Excludeddagger

52

















population





53


detailed in 54







criteria













54













55








56























57











58

Series number 1 2 3 4 5 6 7 8 9






Number of slices 33 38 40 10 10 50 1 1 10




Field of view (mm) 230 320 180 170 170 230 140 140 170




Echo time TE (ms) 15 21 37 85 89 21 56 56 89





Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250







Trigger delay (ms) 511 511 511 250 250




Total Phases 48




Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout


Pre-DCE base

Post-contrast T1

59





















60






















61




















62























63























64



[41]














were not corrected




65






















66




















[42]

67

























68




69







signal intensity

70







71












[43]








72

[44]





(Figure 43D)










(Figure 43E)




(Figure 43F)

73

454 Time to Peak




data (Figure 43G)










minutes-1

(Figure 43H)







74




















462 Group Analysis



75









76





77










78





determination

79

Chapter 5 Results
















defined above

80


































81



















82










83










0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84










0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85







3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15


Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86







10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87







-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88





















89




02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874



90


















91








vulnerability





ROI Time to peak








Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92





















93



2005)













present study






94






















95






















96






















97





















98








Plaques
















99










future studies













100
























101


across studies




















102















scanning







103






















104





post-contrast






found












105























106






















107






















108

67 Conclusions












109

References




2007581157-1163







200847337-342




November 9 2009







Stroke 198920844-849







1425



Vasc Surg 199726585-594

110


201087262-271






















2008281311-1317





February 2009





111






print]



Imaging 200930864-872






2003501199-1208


















112






1994231562-1569


Sci 598 17-28 1990






1375











Sunderland MA 2004









113



1998982108-2116















plaque Circ 2003107851-856



2006241459-468





Imaging 20093035-40





Opin Lipidol 12 383-389 2001




114



20041091016-1021



Stroke 2006371103-1105







2009351436-1442














201031168-176



201083729-735






115




Group JAMA 19912663289-3294







1409




20031073047-3052


Surg 19948403-408



Engl J Med 1991a325445-453







Stroke 1991b22816-817







Imaging 2010a3323-332

116







200641213-221



Stroke 1992231752-1760










200757874-880





200944839-848









117





1992











200950S376-S381




Imaging 1997791-101







367







118







Phys Med Biol 2010551719-1734












Med Biol Soc 2006110291031





Cannon RO 3rd









1996653-57



119







107














2008271309-1316

ix


















67 Conclusions 108

References 109

x

List of Tables







xi

List of Figures











hemorrhage






xii

List of Equations







xiii



2D Two Dimensional







AT2 Angiotensin II


CA Contrast Agent











xiv




ET Endothelin



FSE Fast Spin Echo
















xv

NO Nitric Oxide




p Probability

PD Proton Density




RF Radiofrequency



SI Signal Intensity







TE Echo Time

TI Inversion Time


xvi

TOF Time of Flight


TR Repetition Time

TTP Time to Peak




1






















2























3








4


21 Vascular Anatomy




















5

















mediated pathway





6














carotid arteries






or 5th



7








carotid body











8










9

22 Atherogenesis






















10























11























12









13






14























15














2311 Early Lesions









16





et al 1992)


















17























18





subsections

24 Stroke






2006 HSFC 2011b)

242 Types of Stroke









19








risk

251 Clinical Trials














20






















21























22



















relaxation time T1



23























24






















25


















26











Chu et al (2004)















2010)

27






















28























29























30














alone









31






2821 General Theory
















assumptions

32























33





34








space

35






[21]




seconds-1

and Ctissue is the









which describes the


2) the



36



[22]







model



ktrans









37










atherosclerosis

38









negligible

39












artery wall voxels











40























41
























42






















43









with macrophage



















44


were found for all











imaging













45















F-FDG) uptake and









46




47


31 Study Aims



















48



32 Hypothesis




















49



50

Chapter 4 Methods

41 Study Design
















51





atherosclerosis










02 63 M Included No



05 78 M Included No

06 76 M Included No




10 59 F Included No

11 67 M No





ExcludedDagger

ExcludedDagger

Excludeddagger

52

















population





53


detailed in 54







criteria













54













55








56























57











58

Series number 1 2 3 4 5 6 7 8 9






Number of slices 33 38 40 10 10 50 1 1 10




Field of view (mm) 230 320 180 170 170 230 140 140 170




Echo time TE (ms) 15 21 37 85 89 21 56 56 89





Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250







Trigger delay (ms) 511 511 511 250 250




Total Phases 48




Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout


Pre-DCE base

Post-contrast T1

59





















60






















61




















62























63























64



[41]














were not corrected




65






















66




















[42]

67

























68




69







signal intensity

70







71












[43]








72

[44]





(Figure 43D)










(Figure 43E)




(Figure 43F)

73

454 Time to Peak




data (Figure 43G)










minutes-1

(Figure 43H)







74




















462 Group Analysis



75









76





77










78





determination

79

Chapter 5 Results
















defined above

80


































81



















82










83










0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84










0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85







3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15


Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86







10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87







-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88





















89




02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874



90


















91








vulnerability





ROI Time to peak








Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92





















93



2005)













present study






94






















95






















96






















97





















98








Plaques
















99










future studies













100
























101


across studies




















102















scanning







103






















104





post-contrast






found












105























106






















107






















108

67 Conclusions












109

References




2007581157-1163







200847337-342




November 9 2009







Stroke 198920844-849







1425



Vasc Surg 199726585-594

110


201087262-271






















2008281311-1317





February 2009





111






print]



Imaging 200930864-872






2003501199-1208


















112






1994231562-1569


Sci 598 17-28 1990






1375











Sunderland MA 2004









113



1998982108-2116















plaque Circ 2003107851-856



2006241459-468





Imaging 20093035-40





Opin Lipidol 12 383-389 2001




114



20041091016-1021



Stroke 2006371103-1105







2009351436-1442














201031168-176



201083729-735






115




Group JAMA 19912663289-3294







1409




20031073047-3052


Surg 19948403-408



Engl J Med 1991a325445-453







Stroke 1991b22816-817







Imaging 2010a3323-332

116







200641213-221



Stroke 1992231752-1760










200757874-880





200944839-848









117





1992











200950S376-S381




Imaging 1997791-101







367







118







Phys Med Biol 2010551719-1734












Med Biol Soc 2006110291031





Cannon RO 3rd









1996653-57



119







107














2008271309-1316

x

List of Tables







xi

List of Figures











hemorrhage






xii

List of Equations







xiii



2D Two Dimensional







AT2 Angiotensin II


CA Contrast Agent











xiv




ET Endothelin



FSE Fast Spin Echo
















xv

NO Nitric Oxide




p Probability

PD Proton Density




RF Radiofrequency



SI Signal Intensity







TE Echo Time

TI Inversion Time


xvi

TOF Time of Flight


TR Repetition Time

TTP Time to Peak




1






















2























3








4


21 Vascular Anatomy




















5

















mediated pathway





6














carotid arteries






or 5th



7








carotid body











8










9

22 Atherogenesis






















10























11























12









13






14























15














2311 Early Lesions









16





et al 1992)


















17























18





subsections

24 Stroke






2006 HSFC 2011b)

242 Types of Stroke









19








risk

251 Clinical Trials














20






















21























22



















relaxation time T1



23























24






















25


















26











Chu et al (2004)















2010)

27






















28























29























30














alone









31






2821 General Theory
















assumptions

32























33





34








space

35






[21]




seconds-1

and Ctissue is the









which describes the


2) the



36



[22]







model



ktrans









37










atherosclerosis

38









negligible

39












artery wall voxels











40























41
























42






















43









with macrophage



















44


were found for all











imaging













45















F-FDG) uptake and









46




47


31 Study Aims



















48



32 Hypothesis




















49



50

Chapter 4 Methods

41 Study Design
















51





atherosclerosis










02 63 M Included No



05 78 M Included No

06 76 M Included No




10 59 F Included No

11 67 M No





ExcludedDagger

ExcludedDagger

Excludeddagger

52

















population





53


detailed in 54







criteria













54













55








56























57











58

Series number 1 2 3 4 5 6 7 8 9






Number of slices 33 38 40 10 10 50 1 1 10




Field of view (mm) 230 320 180 170 170 230 140 140 170




Echo time TE (ms) 15 21 37 85 89 21 56 56 89





Bandwidth (kHz) 3125 3125 3125 3125 6250 3125 3125 3125 6250







Trigger delay (ms) 511 511 511 250 250




Total Phases 48




Scan time (minsec) 026 025 250 430 515 337 114 800 515


Anatomical scout


Pre-DCE base

Post-contrast T1

59





















60






















61




















62























63























64



[41]














were not corrected




65






















66




















[42]

67

























68




69







signal intensity

70







71












[43]








72

[44]





(Figure 43D)










(Figure 43E)




(Figure 43F)

73

454 Time to Peak




data (Figure 43G)










minutes-1

(Figure 43H)







74




















462 Group Analysis



75









76





77










78





determination

79

Chapter 5 Results
















defined above

80


































81



















82










83










0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


Cum

ula

tive

AU

C (

min

-1)

0 1 2 3 4 5 6 7

0

10

20

30

40

50

60

70

80

90


A B

DC IPH-positive

IPH-negative

84










0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


Incre

me

nta

l AU

C (

min

-1)

0 1 2 3 4 5 6 7

6

7

8

9

10

11

12

13

14

15


IPH-positive

IPH-negative

A B

DC

85







3

5

7

9

11

13

15

Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15

3

5

7

9

11

13

15


Ea

rly E

nh

an

ce

me

nt R

ate

(m

in-1

)

3

5

7

9

11

13

15


A B

DC

86







10

15

20

25

30

35

40

Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40

10

15

20

25

30

35

40


Ma

xim

um

En

ha

nce

me

nt

10

15

20

25

30

35

40


A B

DC

87







-4

-2

0

2

4

6

8

10

12

Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

-4

-2

0

2

4

6

8

10

12

-4

-2

0

2

4

6

8

10

12


-4

-2

0

2

4

6

8

10

12


Late

En

ha

ncem

en

t R

ate

(seco

nd

s-1

)

A B

DC

88





















89




02Right 09961 09968 09972 09963

Left 09945 09947

04Right 09956 09966

Left 09964 09965 09964 09969

05Right 09930 09926 09871 09950

Left 09946 09955 09942 09939

06Right 09829 09845 09823 09766

Left 09790 09790 09711 09788

07Right 09784 09816

Left 09707 09707 09552 09849

08Right 09651 09646

Left 09874 09775 09533 09898

09Right 09837 09893 09776 09848

Left 09577 09564 09660 09654

10Right 09941 09942 09920 09944

Left 09901 09906 09888 09920

12Right 09923 09919 09923 09928

Left 09861 09853 09875 09881

13Right 09867 09897 09889 09877

Left 09938 09955 09943 09931

Average 09859 09866 09828 09874



90


















91








vulnerability





ROI Time to peak








Cumulative AUC

Incremental AUC

Early enhancement

rate

Late enhancement

rate

Peak enhancement

Early-late ratio

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Greater plt005

Vasa vasorum

92





















93



2005)













present study






94






















95






















96






















97





















98








Plaques
















99










future studies













100
























101


across studies




















102















scanning







103






















104





post-contrast






found












105























106






















107






















108

67 Conclusions












109

References




2007581157-1163







200847337-342




November 9 2009







Stroke 198920844-849







1425



Vasc Surg 199726585-594

110


201087262-271






















2008281311-1317





February 2009





111






print]



Imaging 200930864-872






2003501199-1208


















112






1994231562-1569


Sci 598 17-28 1990






1375











Sunderland MA 2004









113



1998982108-2116















plaque Circ 2003107851-856



2006241459-468





Imaging 20093035-40





Opin Lipidol 12 383-389 2001




114



20041091016-1021



Stroke 2006371103-1105







2009351436-1442














201031168-176



201083729-735






115




Group JAMA 19912663289-3294







1409




20031073047-3052


Surg 19948403-408



Engl J Med 1991a325445-453







Stroke 1991b22816-817







Imaging 2010a3323-332

116







200641213-221



Stroke 1992231752-1760










200757874-880





200944839-848









117





1992











200950S376-S381




Imaging 1997791-101







367







118







Phys Med Biol 2010551719-1734












Med Biol Soc 2006110291031





Cannon RO 3rd









1996653-57



119







107














2008271309-1316

Documents

Nonmodel-Based Dynamic Contrast-Enhanced Magnetic ... · Background: Parameters of carotid atherosclerosis dynamic contrast-enhanced MRI (DCE-MRI) are associated with stroke risk