Measurement of T 1 in the Vessel Wall Using MRI

Transcription

1 Measurement of T 1 in the Vessel Wall Using MRI by Rahul Sarkar A thesis submitted in conformity with the requirements for the degree of Master of Science Medical Biophysics University of Toronto Copyright by Rahul Sarkar 2011

2 Measurement of T 1 in the Vessel Wall with MRI Rahul Sarkar Master of Science Medical Biophysics University of Toronto 2011 Abstract This thesis presents a high-resolution volumetric technique to measure the longitudinal relaxation time T 1 in the vessel wall using MRI. The method of Variable Flip Angles (VFA) was applied using a new strategy for flip angle selection that allows measurement of T 1 with high accuracy (< 10% mean error) and precision (T 1 -to-noise ratio > 10) over the wide range of anticipated values ( ms) in the vessel wall. This strategy was validated in simulation, phantom and volunteer spinal cord experiments. Initial validation of vessel wall T 1 measurements was performed in ex-vivo thoracic aorta samples from cholesterol-fed rabbits. For in-vivo vessel wall T 1 mapping, the technique was augmented with spatial saturation bands for flow suppression and applied to the carotid arteries of three volunteers. Preliminary results from volunteers suggest that this approach may be useful in characterizing T 1 changes associated with high-risk atherosclerotic disease. ii

3 Acknowledgments I would like to thank the extraordinary group of people that have my time in graduate school truly memorable. My supervisor, Dr. Alan Moody, for the unique opportunity to explore science beyond the traditional boundaries that separate disciplines. Whether interpreting clinical findings or developing new computational models, his valuable insight always guided me in traversing unfamiliar terrain, and his enthusiasm in my ideas consistently encouraged me to keep exploring. My friends and colleagues in VBIRG, as much for helping me tackle scientific challenges as for distracting me from them when it was necessary. I learned more our conversations than I ever could from the pile of textbooks stacked high on my desk. To my family, I am indebted to you for all of your love and support: Pavneet, for supporting me through the most difficult stretches of my graduate school journey. Whether keeping me company during late-night experiments at the hospital or listening attentively to practice runs of my oral presentations, your support behind the scenes made my work possible, and your belief in me continues to empower my efforts. Ava and Neil, for your unconditional pride in me, and for the example of courage and strength that you have set for me to follow. Much of what I have learned about a life well-lived has come from your example. And finally, Ma and Baba, for your unrelenting dedication to my happiness. Through a lifetime of hard work and considerable challenges, you have created every opportunity for me to pursue my passions, and have seen me through every turn. I sincerely hope that this thesis serves as a strong testament to your incredible efforts as parents. iii

4 Table of Contents Table of Contents...iv List of Tables...vii List of Abbreviations...viii List of Figures...x Chapter 1 Introduction Atherosclerosis Plaque Formation Vulnerable Plaque Neovascularization and Intraplaque Hemorrhage Microvascular Permeability in Early Disease Imaging of High-risk Vessel Wall Disease Imaging Targets for Vulnerable Plaque Ultrasound Computed Tomography (CT) Positron Emission Tomography (PET) Magnetic Resonance Imaging (MRI) T 1 Imaging and Quantification Physical Description of T T 1 Weighted Imaging T1 Measurement Motivation for Vessel Wall T1 Measurement Needs for Vessel Wall T 1 Measurement Approach...17 iv

5 1.3.6 Challenges Hypothesis Overview of Thesis...22 Chapter 2 Spatial T 1 Mapping Technique for the Vessel Wall Introduction Theory Basic VFA Theory Proposed Flip Angle Selection Strategy Materials and Methods Simulations B 1 Inhomogeneity Correction Phantom Experiments Validation in Ex-vivo Rabbit Thoracic Aortas Volunteer Experiments Results Simulation Results Phantom Results Ex-Vivo Rabbit Thoracic Aorta Results Results of Volunteer Experiments Discussion Significance of T 1 variation in the vessel wall Study relevance and limitations Flip angle selection strategy Ex-vivo vessel wall T 1 measurement In-vivo carotid T 1 measurement Conclusion...63 v

6 Chapter 3 Summary and Future Directions Thesis Summary Future Directions Improvements in SNR Pulsatile Vessel Wall Motion Compensation Application to Large Study Population Histological Validation Conclusion...70 References...71 vi

7 List of Tables Table 2.1. IR- and WRA-measured T 1 values from each thoracic aorta specimen, with perivascular fat and artifactual saline excluded...47 Table 2.2. WRA-measured T 1 values from volunteer spinal cords. Gray matter and white matter T 1 s both show good agreement with reported IR and conventionally-optimized VFA values [101] Table 2.3. Pre- and post-contrast T 1 measurements taken over the whole vessel for each volunteer carotid artery...51 vii

8 List of Abbreviations Abbreviation Meaning Page (definition AFI actual flip-angle imaging 34 or first use) CT computed tomography 6 CTA computed tomography angiography 6 CVD cardiovascular disease 1 DCE-MRI dynamic contrast-enhanced magnetic resonance imaging 9 FDG fluorodeoxyglucose 7 FFE fast field echo (imaging) 34 FOV field of view 16 IMT intima-media thickness 5 IPH intraplaque hemorrhage 3 IR inversion recovery 14 IVUS intravascular ultrasound 6 LDL low-density lipoprotein 1 M 0 longitudinal magnetization 10 MMP matrix-degrading metalloproteinases 3 MRIPH magnetic resonance-depicted intraplaque hemorrhage 9 NMR nuclear magnetic resonance 10 viii

9 PET positron emission tomography 7 PD proton density 7 RBC red blood cell 3 RF radiofrequency (pulse) 11 ROI region of interest 34 S*DR product of signal and dynamic range 32 SNR signal-to-noise ratio 20 SPGR spoiled gradient-recalled echo imaging 9 SR saturation recovery 14 T 1 longitudinal relaxation time 11 T 1 NR T 1 -to-noise ratio 33 T 1target target T 1 (for flip angle optimization in method of variable flip angles) 29 T 1 W T 1 -weighted (imaging) 9 TIA transient ischemic attack 2 TR repetition time 12 VEGF vascular endothelial growth factor 5 VFA (method of) variable flip angles 15 WRA wide-range angle (set) 22 ix

10 List of Figures Figure 1.1. A plot of the relative SPGR signal vs. flip angle for different T 1 s (legend) and fixed TR and M 0. The SPGR signal S is normalized to S 0, which represents the maximum SPGR signal obtainable (i.e. using α = 90 0 and TR >> T1). At low flip angles (< 3 o ), signal is consistent across all T 1 s; at higher flip angles, shorter T 1 species produce greater signal than longer T 1 species...13 Figure 1.2. Illustration of IR-based T 1 measurement. Magnetization is inverted and then sampled at a fixed times (four shown here) during longitudinal relaxation. For each inversion pulse, one sample is taken, and longitudinal magnetization is allowed to fully recover (TR 5*T 1 ) before the next inversion pulse. T 1 is estimated from fitting the sampled data to the relaxation curve Figure 2.1. Flip angle optimization for different choices of T 1target changes the relative signal at each sample point (colored markers) for different T 1 s (represented by differently coloured curves). Since T 1 is estimated from the slope of a linearized form of these curves, the sample point with lower signal in each pair dominates the error in T 1 estimation. Considering only the lower signal sample points in each pair, optimization for a shorter T 1target (left) provides greater signal for shorter T 1 s but lower signal for longer T 1 s when compared to optimization for a longer T 1target (right). Optimization for a longer T 1target provides greater uniformity among the lower signal points across all T 1 s Figure 2.2. Optimization for different choices of T 1target changes the dynamic range between sample points for different T 1 s. Optimization for T 1target = 500ms (left) provides a greater dynamic range (i.e. separation between sample points) for shorter T 1 s than for longer T 1 s; this bias is reversed when optimizing for T 1target = 3000ms (right), providing greater dynamic range for longer T 1 s than for shorter T 1 s. Note that T 1 is estimated from subtle differences in slope between the plotted lines that are not visually apparent in this representation Figure 2.3. Plot of the product of minimum signal and dynamic range (S*DR) across the biological range based on optimization for different choices of T 1target. Optimization for progressively longer choices of T 1target within the biological range ( ms) provides greater uniformity of the S*DR product across the entire range...32 x

11 Figure 2.4. Phantom experiment designed to test wide-range T 1 mapping capability of the WRA set. Left: The phantom apparatus containing 12 samples of varying MnCl 2 concentrations was scanned using the birdcage head coil; 11 samples yielding T 1 s in the range of ms were used for analysis. Right: Corresponding slices from 3D T 1 W FFE phantom images taken at flip angles of 3 o (top) and 15 o (middle) and the WRA T 1 map produced from them (bottom). Regions of interest used for T 1 estimation in each sample are shown overlaid in red Figure 2.5. The inverse of T 1, also known as the longitudinal relaxation rate R 1, demonstrates a linear relationship with the MnCl 2 concentration across all phantom samples...36 Figure 2.6. Experimental setup for ex-vivo vessel wall validation. Thoracic aorta samples from each of the four rabbits were placed in respective compartments of the phantom apparatus (left). The samples were then suspended in perfluorocarbon within a sealed container prior to scanning (right)...37 Figure 2.7. Prescription of spinal cord VFA acquisitions; volumes were approximately centered at the C3 disc level Figure 2.8. Prescription for volunteer carotid WRA acquisitions. 40 slices were acquired through a 10cm imaging slab (red), with spatial saturation bands (blue) placed above and below to suppress the signal from flowing blood Figure 2.9. Screenshot of graphical user interface used for volunteer carotid data processing. Low- (left column) and high-angle (right column) acquisitions obtained pre- (top row) and postcontrast (bottom row) were all registered to the reference low-angle pre-contrast acquisition (top left). ROIs manually drawn on the reference acquisition were propagated to the other registered acquisitions, before application of the resampled B 1 map and generation of resulting pre- and post-contrast T 1 maps Figure Example of the appearance of a low signal region on the low-angle WRA acquisition found to correspond to a region of low M 0. Lower SNR results in uncertainty regarding accuracy and precision of T 1 measurements from these regions xi

12 Figure Profiles for mean error (left) and T 1 NR (right) across the biological range based on different choices of T 1target at SNR = 475. Optimization for T 1target = 3000ms (the WRA set; green) provides the most uniform profiles across the biological range...43 Figure Representative T 1 profile through water phantom of first phantom experiment. B 1 correction is applied to the WRA profile (red) to yield a corrected WRA profile (green), which shows good agreement with the reference IR profile (blue) (Corrected WRA: 385 +/- 13 ms vs. IR: 397 +/- 1 ms) Figure WRA-measured T 1 values plotted against reference IR-measured T 1 values for each T 1 sample in the second phantom experiment. Error bars show two standard deviations of the WRA-measured values. The WRA approach showed high accuracy (mean error < 8.2%) and precision (T 1 NR > 7.3) across all samples Figure Each excised thoracic aorta sample is shown alongside a representative T 1 W-FFE image slice, reference IR T1 map, and WRA T1 map. Note that Vessels 2-4 were each cut in half for easier placement in the phantom apparatus; both halves of each vessel were imaged sideby-side. Good overall agreement between the IR and WRA techniques is observed within the vessel walls of each sample. An example of perivascular fat (blue arrow, labeled PF) is shown on the IR T 1 map of Vessel 2; this region is spatially displaced due to chemical shift artifact in the WRA map. An example of artifactual saline (pink arrow, marked AS) is shown on the IR T1 map of Vessel 3; this is also visible on the corresponding region of the WRA T 1 map Figure T 1 histogram profiles for each vessel (top panel) and across all vessels (bottom panel) show good agreement for peak locations between techniques. The reduced precision of the WRA technique results in broadening of peaks and higher noise floor compared with the reference IR technique...48 Figure Spinal cord B 1 map (left) and WRA T 1 map (right) from a volunteer. ROIs drawn for gray matter (red) and lateral column white matter (blue) on the low-angle WRA acquisition are propagated to the B 1 and T 1 maps (overlayed) Figure Pre-contrast 3D T 1 map generation for a single volunteer artery. Several regions of apparently low T 1 in the unfiltered T 1 map (top left) correspond to regions of low M 0 (top right), resulting in lower SNR. Exclusion of low M 0 regions from analysis produces filtered T 1 and M 0 xii

13 maps (center row), from which overall and slice-by-slice T 1 distributions are produced (bottom row)...50 Figure Pre-contrast (top panel) and post-contrast (bottom panel) T 1 distributions taken over the whole vessel for each volunteer carotid artery. Considerable overlap is observed among preand post-contrast distributions across all vessels. Marked T 1 shortening is observed in postcontrast distributions from healthy volunteer arteries Figure Matching slices from the MRIPH scan (left) and T 1 map (center) in the diseased artery. Hyperintensity corresponding to IPH on the MRIPH scan correlates with regions of low T 1 seen on the map. The T 1 distribution of the slice map (right) shows several low T 1 pixels (< 500ms; red box) that represent IPH region pixels Figure 3.1. Lumen Boundary Motion Trace (bottom right) of a column of pixels over 20 acquired phases of the cardiac cycle at the lumen boundary near the carotid bifurcation of a healthy volunteer (grayscale top left, color scaled bottom left). Simulated ECG signal shown (top right) for reference. Relatively little boundary motion is observed in the latter half of the cycle...67 Figure 3.2. Increasing Gating Window As the gating window increases, apparent vessel wall size increases. Over a limited range, 50% of the end diastolic cycle, a low blur region exists for all volunteers xiii

14 1 1 Introduction 1.1 Atherosclerosis Chapter 1 Introduction Cardiovascular diseases (CVDs) are the number one cause of death worldwide [1]. The World Health Organization reports that an estimated 17.1 million people died from CVDs in 2004, representing 29% of all deaths in that year. Almost 13 million of these deaths were due to heart attack and stroke, both of which are generally the result of arterial occlusions that obstruct the flow of blood to the heart and brain respectively. Such occlusions are most commonly caused by the rupture of complex plaques that are formed within the arterial walls as a result of atherosclerosis, the underlying vascular pathology behind many clinical presentations of CVDs Plaque Formation Atherosclerosis has long been believed to involve the passive accumulation of fatty material on the surface of the arterial lumen, and occlusions were considered to be the eventual result of this surface accumulation. The last two decades of research in vascular pathology have completely revolutionized our understanding of the atherosclerotic process. We now consider it to be a disease of the vessel wall itself rather than of the lumen, and one that relies heavily on inflammatory mechanisms for its progression. While a variety of risk factors have been identified, initiation of the disease process is generally linked to high circulating levels of low density lipoproteins (LDLs) [2]. Under these conditions, LDLs begin to deposit within the intima of the vessel wall, where they undergo subsequent oxidation and glycation reactions. The presence of these chemically-modified LDLs initiates a local inflammatory response [3], causing activation of endothelial cells and recruitment of leukocytes into the intima from circulation [4-6]. Once in the intima, these monocytes mature into active macrophages that then ingest the chemically-modified LDLs with the aid of surface scavenger receptors [2]. This process continues until macrophages become highly filled with lipids, at which point they are referred to as foam cells due to their foamy appearance under a microscope [7]. Along with T lymphocytes that are similarly recruited into the intima, the collection of foam cells forms an early-stage atherosclerotic plaque known as the fatty streak.

15 2 The disease progresses when chemokines cause smooth muscle cells of the media to migrate to the top of the intima, multiply and synthesize components of the extracellular matrix [2]. The cells and matrix coalesce to form a fibrous cap that covers the growing lipid core underneath. The integrity of the fibrous cap is important for the stability of the plaque, as it serves as a major barrier between flowing blood and pro-thrombotic lipid core. With the fibrous cap intact, plaque growth occurs as a long period of outward expansion of the vessel wall followed a later period involving inward growth that narrows the lumen [8]. Given the continued stability of the plaque during this growth process, the lumen may become highly stenosed and begin to cause transient ischemia of downstream tissues. Patients with this pathological progression often have associated clinical presentations such as angina pectoris, transient ischemic attacks (TIAs), or intermittent claudication depending on the plaque location. Without treatment, the occlusion may eventually block blood flow through the vessel to the extent that downstream tissue death occurs and organ function becomes compromised Vulnerable Plaque As per the described pathophysiology, stenosis tends to present in later stages of disease and provides some prognostic value in assessing risk of future vascular events. The North American Symptomatic Carotid Endarterectomy Trial (NASCET) showed that surgical removal of plaque from a diseased carotid artery reduces the risk of stroke and death up to 2 years in patients with 70-99% stenosis, whereas surgery is less beneficial for those with 50-69% stenosis and performs no better than medical therapy for those with less than 50% stenosis [9]. Similar results have been shown in asymptomatic populations with carotid stenosis of 60% or greater, where surgery has reduced the 5-year risk of stroke compared with medical therapy [10]. Yet despite the increased risk of stroke associated with carotid stenosis, the majority of stroke cases likely occur in the presence of low-to-moderate grade stenosis due to their greater prevalence [11]. In terms of pathophysiology, this represents a greater prevalence of unstable or vulnerable plaques that rupture prior to causing high-grade stenosis. Therefore, alternative markers to stenosis are needed to identify such individuals who would benefit from surgical management. Pathologic studies over the past several years have identified a number of features of plaque composition that are predictive of future symptomatic presentation. Bassiouny et al. compared 99 endarterectomy specimens from symptomatic and asymptomatic patients and found a greater

16 3 frequency of certain morphological characteristics in those from the symptomatic group, including close proximity of the lipid core to the lumen, significantly greater macrophage infiltration into the fibrous cap, and the presence of fibrous cap disruptions or ulcerations [12]. The association of fibrous cap degradation and increased macrophage infiltration is attributed to the production of matrix-degrading metalloproteinases (MMPs) by macrophages, which results in the breakdown of extracellular matrix and can compromise the integrity of the cap [13]. Macrophages also contribute to the growth of the lipid core, which has been found to be larger in disrupted and high-risk plaques compared with lower risk fibro-calcific plaques [14,15]. The connection between macrophage activity and high-risk morphological plaque features has led to studies examining inflammatory changes following therapeutic interventions. Reduction of MMP activity has been demonstrated as a mechanism of action in lipid-lowering therapy using statins, which has been shown to reduce cardiovascular events and mortality in several trials [16-18]. In addition to lower MMP activity, carotid plaques have been shown to have fewer macrophages, T cells and lipids, reduced lipid oxidation, and increased collagen content following statin therapy, further emphasizing the role of inflammation in plaque destabilization [19] Neovascularization and Intraplaque Hemorrhage In healthy vessels, the intimal layer of the vessel wall receives oxygen by diffusion from the lumen, whereas the outer layers are nourished by the network of small arteries and veins comprising the vasa vasorum. Pathological thickening of the vessel wall with progressive disease results in impaired oxygen and nutrient diffusion, and new microvessels begin to sprout from the vasa vasorum predominantly via hypoxia-dependent angiogenic pathways in order to compensate for these changes [20]. When compared with adventitial microvasculature in healthy vessel walls, disease-induced neovasculature shows abnormal endothelial cell morphology and aberrant inter-endothelial cell junctions, representing a fragile and leakageprone ultrastructure [21]. Histological evidence from advanced, lipid-rich plaques has established these highly permeable neovessels as pathways for macrophage infiltration [22-24]. The permeability of neovessels has also been linked to the extravasation of red blood cells (RBCs), leading to intraplaque hemorrhage (IPH) and accelerated disease progression [25]. This process involves the accumulation of ceroid, an insoluble mixture of oxidized lipids and proteins

17 4 which mark the sites of previous oxidative events, as well as lysis of the cholesterol-rich RBC membrane and subsequent lipid deposition [26]. Perivascular foam cells have been found to frequently contain platelets and RBC products such as hemoglobin and iron, suggesting that phagocytosis of RBCs and platelets following local hemorrhages leads to iron deposition, macrophage activation and foam cell formation, promoting lipid accumulation within the vessel wall [27]. Ferric iron resulting from the oxidative denaturation of hemoglobin catalyzes the formation of free radicals that contribute to LDL oxidation, further driving the inflammatory progression of the disease [28,29]. The pathological acceleration driven by the extravasation of lipid and pro-inflammatory blood products from neovessels into the vessel wall suggests that the presence and degree of neovasculature may serve as a marker of plaque vulnerability. In 2003, Purushothaman et al. reported results on the study of 262 human autopsy aortic plaques that were quantified for inflammation, fibrous cap thickness, lipid area and the degree of plaque neovasculature present in order to compare their statistical power to predict plaque disruption [15]. Of the various pathological features, plaque neovascularization was found to be the most powerful independent predictor of risk, even greater than the presence of a thin fibrous cap or high inflammatory cell count quantified by light microscopy. In addition to establishing plaque neovasculature as a powerful biomarker of high-risk disease, these results further the evidence that the danger from neovessels involves the leakage of inflammatory cells as well as substrates and catalysts of lipid oxidation Microvascular Permeability in Early Disease Observations from several studies over the last decade have suggested that the neovascularization process begins much earlier than previously thought. Studies of cholesterolfed swine have demonstrated that coronary neovascularization may accompany the early process of outward growth associated with vessel wall remodeling. Neovascularization has been observed as early as 4-6 weeks in this animal model, preceding the appearance of endothelial dysfunction by several weeks [30]. Early human carotid lesions have been shown to contain inflammatory cells and apolipoproteins A-I and B at sites of neovascularization, suggesting that local lipid deposition and inflammation associated with neovessels may be among the earliest features of atheroma [31]. This appears to be especially true in the presence of diabetes, where a

18 5 variety of molecular mechanisms have been implicated in hyperglycemia-induced vascular permeability as early as 2 weeks after the onset of diabetes in an animal model [32, 33]. This increased permeability is associated with the expression of vascular endothelial growth factor (VEGF), the main promoter of angiogenesis and neovascularization responsible for diabetic microangiopathy [34]. In advanced disease, plaques from patients with diabetes show increased microvessel content, macrophages/t-lymphocyte cells, and intraplaque hemorrhage [35]. The association of this inflammatory microangiopathic process with accelerated plaque rupture helps to explain the increased mortality due to atherosclerosis in the diabetic population relative to non-diabetics [36]. In both groups, neovascularization and the presence of perivascular blood products represent useful pathological markers associated with early stages of high-risk disease. 1.2 Imaging of High-risk Vessel Wall Disease Imaging Targets for Vulnerable Plaque Several markers of plaque vulnerability were presented in the previous section, including morphological (thin/ulcerated fibrous cap, large lipid core) and functional (macrophage activity, intraplaque hemorrhage, neovascularization) markers. All of these features have been mechanistically related to neovascularization, which represents the strongest independent predictor of risk among pathological plaque features. A variety of imaging tools exist that can identify one or more of these high-risk markers in patients. A brief summary of these tools and their current capabilities and limitations is presented the following subsections Ultrasound Ultrasound imaging is widely used in the evaluation of vascular disease. Its main applications currently lie in the detection of flow velocity alterations including high blood flow velocities associated with severe stenosis, determination of plaque surface structure, and monitoring the intima media thickness (IMT). Doppler ultrasound, a technique that is widely used for the detection of stenotic and occlusive flow alterations in the peripheral, pulmonary and carotid vasculature, may also have utility in assessing localized mechanical properties associated with vulnerable plaque. In particular, Tissue Doppler Imaging has been shown to identify carotid wall motion features associated with disease in several case studies; however, this technique is limited

19 6 at present due to high variability resulting from physiological differences between subjects and technical sources of error [37, 38]. Efforts to use conventional ultrasound to investigate plaque morphology have focused on correlating plaque echogenicity with histological criteria (i.e. hemorrhage, lipid accumulation), as well as detecting plaque surface ulcerations and characterizing fibrous cap thickness. Studies have shown that high-resolution B-mode scanning is sensitive to echomorphological features that correlate with histopathologic criteria [39]; however, a lack of specificity and the presence of shadowing artifacts due to calcification has made identification of morphological risk markers difficult and limited the utility of plaque echogenicity in this regard [40]. Early reports on the use of B-mode imaging in differentiating between smooth, irregular and ulcerative plaque surfaces showed good results in postmortem carotid artery specimens [41], but showed a sensitivity of only 47% in detecting ulcerative plaques in-vivo [39]. While recent advancements in ultrasound technology such as compound B- mode imaging may improve the detection of plaque ulceration, this remains to be investigated. A recent study demonstrated the feasibility of carotid fibrous cap thickness measurement using ultrasound, and showed good discrimination between symptomatic and asymptomatic atheromas based on mean fibrous cap thickness [42]. Developing applications in functional plaque imaging using microbubble contrast agents may allow assessment of plaque neovascularization. Recent studies have shown that contrast-enhanced harmonic ultrasound can detect the presence of neovascularization, describing a positive correlation with the histological density of neovessels [43, 44]. The use of intravascular ultrasound (IVUS), a technology where a miniature ultrasound probe is attached to the distal end of a catheter to allow imaging from within the lumen, allows improved detection of ultrasound plaque features relative to conventional ultrasound. Spectral analysis of IVUS signals provides more accurate assessment of plaque morphology, particularly the size of the lipid core and thickness of the fibrous cap, in both coronary and carotid plaques [45, 46, 47]. Like conventional ultrasound, detection of neovascularization using IVUS is feasible using contrast harmonic imaging, but the ability to effectively characterize the degree of neovascularization or detect related functional markers such as IPH is currently limited [48, 49] Computed Tomography (CT) The role of computed tomography in atherosclerosis imaging has centered on assessment of luminal narrowing using CT angiography (CTA). Compared with alternative modalities such as ultrasound and MR, relatively few studies have investigated the use of CT in evaluating vessel

20 7 wall features. As contrast in CT imaging relies on differential attenuation of X-rays among tissues, plaque components with overlapping ranges of radiodensities (e.g. lipid core, hemorrhage, connective tissue) may be indistinguishable from one another [50]. Hence, past studies examining plaque morphology using CT have often focused on detection of calcifications, which typically exhibit significantly greater attenuation coefficients than other plaque components and are thus more easily detectable [51]. With the introduction of newer generation multidetector-row CT scanners, recent studies have re-examined the utility of CT in imaging vulnerable plaque [50, 52]. In a prospective study of eight symptomatic patients, histological comparison of excised carotid plaque specimens with pre-operative carotid CTA images showed perfect agreement for calcifications, and demonstrated good correlations for the detection of ulcerations and measurement of fibrous cap thickness [50]. Correlations between histology and CTA were also good for large lipid cores and large hemorrhages, but smaller regions (< 5 pixels at 0.5mm 2 in-plane resolution) of lipid and hemorrhage could not be reliably distinguished due to overlaps in their Hounsfield densities. A retrospective study of 136 patients that underwent the same CTA protocol found significant associations between acute carotid stroke and several CTA-detected wall features, including increased wall volume, thin fibrous cap, more lipid clusters, and closer proximity of lipid clusters to the lumen [52]. Validation of these results in a larger prospective study is required, but these initial results suggest that CT may have greater clinical utility in characterizing late-stage atherosclerotic disease than its current limited role in stenotic assessment. At present, the major limitation of CTA towards detecting vulnerable plaque at earlier stages is its inability to distinguish between smaller areas of different high-risk soft-tissue components Positron Emission Tomography (PET) Positron emission tomography (PET) is well suited towards imaging functional markers of highrisk disease that are invisible to other modalities. In particular, the use of the glucose analogue [ 18 F]-fluorodeoxyglucose ( 18 FDG) has been shown to identify plaque inflammation. A pioneering study of eight patients with symptomatic carotid disease who were imaged using 18 FDG-PET and co-registered CT showed that symptomatic plaques are visible on 18 FDG-PET images acquired 3 hours post- 18 FDG injection. Symptomatic lesions showed 27% greater 18 FDG accumulation rate than contralateral asymptomatic lesions, and normal carotid arteries showed no 18 FDG uptake. Furthermore, autoradiography of excised plaques confirmed

21 8 accumulation of deoxyglucose in macrophage-rich areas of the plaque [53]. Subsequent studies have similarly shown good agreement between 18 FDG uptake and plaque macrophage content in symptomatic patient groups (r = 0.78, p < 0.001) [54,55]. 18 FDG uptake has also been shown to be significantly higher in lipid-necrotic core than in collagenous or calcified plaque regions as classified by appearance on CT and MR [56]. However, at least one study examining 18 FDG uptake in apolipoprotein E-deficient mice (i.e. with impaired lipoprotein catabolism) has shown a lack of correlation with atherosclerotic plaque, and identified brown fat as a dominant source of 18 FDG signal in vivo [57]. Studies are needed to unequivocally link 18 FDG uptake to plaque macrophage content have yet to be published, due in part to experimental difficulties such as rapid tracer decay and lack of fluorescent analogues for correlation with flow cytometry or fluorescence microscopy [58]. The future of PET in vulnerable plaque imaging may lie in the use of novel tracers such as 64 Cu-labelled magnetofluorescent nanoparticles, which have been shown to improve sensitivity to inflammation in pre-clinical models, and offer the potential for cross-modality imaging and confirmatory microscopy studies [58]. At present, studies are needed to validate the effectiveness of PET/CT using alternative tracers to 18 FDG in patient populations Magnetic Resonance Imaging (MRI) Magnetic resonance imaging (MRI) has been the most widely used modality to date for imaging features of vulnerable plaque, owing largely to its ability to differentiate between plaque features under different imaging contrasts. The use of multi-contrast imaging protocols including a combination of time of flight, T 1 -, T 2 -, proton density (PD)- and intermediate-weighted acquisitions has become an established method for detection of a variety of vulnerable plaque features. In-vivo imaging using multi-contrast protocols has been shown to identify the lipidnecrotic core and intraplaque hemorrhage with a sensitivity of 85% and a specificity of 92% [59], thin or ruptured fibrous cap with a sensitivity of 81% and specificity of 90% [60], and large (> 2mm 2 ) calcifications with a sensitivity of 84% and a specificity of 91% [61]. A number of studies have correlated these MR-detected features with clinical outcomes. In comparing symptomatic and asymptomatic populations, Yuan et al. showed that patients with a ruptured fibrous cap were 23 times more likely to have had a recent cerebrovascular event than those with thick fibrous caps as identified by multicontrast MRI [62]. A prospective study of subjects with asymptomatic 50 to 79% carotid stenosis also showed significant associations between

22 9 subsequent symptoms during follow-up and baseline multicontrast MRI identification of thin/ruptured fibrous cap, as well as with intraplaque hemorrhage, large lipid-necrotic core and larger maximum wall thickness (in decreasing order of correlation strength) [63]. The specific utility of T 1 -weighted (T 1 W) imaging in detecting vulnerable plaque features has gained prominence in recent years. Gadolinium-based contrast agents result in selective enhancement of certain plaque tissues relative to others under T 1 W imaging, allowing improved discrimination between components. Acquisition of T 1 -weighted images before and after contrast agent administration has been shown to help differentiate lipid-necrotic core from surrounding fibrous tissue [64] and can improve delineation of the fibrous cap [65]. Studies have also shown an association between contrast enhancement and the degree of macrophage infiltration of the plaque [66,67], and contrast enhancement has been subsequently linked to neovascularization from the adventitial vasa vasorum [64]. The rate of enhancement found under dynamic contrastenhanced MRI (DCE-MRI) studies has also been strongly associated with both macrophage and neovascular densities [67,68]. In the absence of exogenous contrast agents, hyperintensity on T 1 W carotid plaque images has been shown to correlate with higher levels of ischemic events in patients with varying degrees of carotid stenosis [69]. This hyperintensity has been histologically associated with the presence of intraplaque hemorrhage and is due to the T 1 -shortening effect of paramagnetic methemoglobin within the hemorrhage [70]. In order to exploit this endogenous T 1 marker, rapid 3D T 1 W fatsuppressed spoiled gradient-echo (SPGR) imaging has been employed obtain heavily T1- weighted images that are sensitive to intraplaque hemorrhage [71]. This approach is known by several names, including Magnetic Resonance Direct Thrombus Imaging (MRDTI), Magnetization prepared gradient recalled echo (MPRAGE), and MR-depicted intraplaque hemorrhage (MRIPH); it will be referred to as MRIPH for the purposes of this thesis. The MRIPH technique has shown a negative predictive value of 100% for future cerebrovascular events in both symptomatic and asymptomatic patient populations, providing higher sensitivity and demonstrating a higher hazard ratio for intraplaque hemorrhage when compared with its detection using multicontrast protocols [70, 72]. Recent efforts by several groups have sought to further improve sensitivity to hemorrhage and differentiate other image features such as calcium and lipid core on the basis of differential T 1 contrast in these images [73, 74]. With further advances in sensitivity to T 1 contrast, this approach has the potential to replace current

23 10 multicontrast protocols for a variety of applications, potentially resulting in a significant reduction in imaging times [75]. 1.3 T 1 Imaging and Quantification This section will begin with a brief description of classical MR physics relevant to the understanding of T 1, followed by a discussion of the motivation for measuring T 1 in the vessel wall. Existing methods for quantitative T 1 estimation will then be introduced, as well as the main challenges that limit the application of these approaches towards the vessel wall Physical Description of T 1 MRI relies on the principle of nuclear magnetic resonance (NMR), which allows for electromagnetic excitation and subsequent relaxation of certain substances under given conditions. In particular, NMR requires that substances have a net nuclear spin due to the presence of unpaired nucleons, each of which possesses a spin of magnitude ½ and orientation of either + or [76]. When placed in an external magnetic field B 0, such a substance is capable of absorbing energy from an applied electromagnetic field B 1 of a specific frequency, known as the Larmor frequency, given by: (1.1) ω 0 = γ B 0 where represents the gyromagnetic ratio of the given substance. In most conventional MRI imaging, the substance of interest is hydrogen ( 1 H), for which = MHz / Tesla [76]. In the presence of the B 0 field, the spin vectors of the 1 H atoms tend to align either parallel or antiparallel to B 0, representing high and low energy states respectively. If we consider a group of 1 H atoms in a particular region of B 0, the net magnetic field due to their collective spin vectors can be represented by a magnetization vector. At room temperature, the number of low energy spins N low is slightly greater than the number of high energy spins N high, resulting in a magnetization vector with magnitude proportional to (N low N high ) and aligned anti-parallel to the direction of B 0. In the absence of an applied B 1 field, this represents the equilibrium magnetization vector with magnitude M 0. If we define a 3D coordinate system such that the direction of this vector is along the + z-axis, we may say that M z = M 0.

24 11 When the B 1 field is applied at the Larmor frequency, 1 H nuclei in the low energy state are able to absorb energy from the applied field and enter the high energy state. The excited spins also exhibit phase coherence in the presence of B 1, resulting in the appearance of a transverse component of magnetization which forms the basis of the recorded signal used to produce MR images. After applying the B 1 field for a certain period of time, the magnetization vector lies fully in the transverse plane with no net magnetization along the z-axis, i.e. M z = 0 [76]. If the B 1 field is then removed, the phase coherence of the transverse magnetization vector will be lost over time due to spin-spin interactions known as T 2 relaxation mechanisms; these are outside of the scope of this thesis and will not be discussed in detail. At the same time, much of the absorbed energy will be lost due to molecular motion with the surrounding lattice, eventually restoring the thermal equilibrium state where M z = M 0. This process is governed by the following equation: (1.2) M z (t) = M 0 (1 e t T 1 ) where t = 0 represents the time immediately after saturation at which the B 1 field is turned off, and T 1 is a time constant representing the time at which the difference between M z and M 0 has been reduced by a factor of e [76]. T 1 is said to represent the longitudinal relaxation time, such that substances requiring more time to return to the thermal equilibrium state (M z = M 0 ) following excitation of finite duration by the B 1 field (referred to as a radiofrequency (RF) pulse) have longer T 1 s. T 1 can vary greatly between substances of different physical states (e.g. solid/liquid, viscosity, temperature, etc.), and considerable variation exists between different body tissues. At a B 0 field strength of 3 Tesla, fat has a relatively short T 1 ~ 300ms, skeletal muscle has an intermediate T 1 ~ 1200ms, and cerebrospinal fluid has a longer T1 > 3000ms [77, 78]. The presence of paramagnetic substances, i.e. those with unpaired electron spin, can also act to shorten T 1 in relaxing tissues due to dipole relaxation by the electron magnetic moment and transfer of unpaired electron density to the relaxing nuclei [79] T 1 Weighted Imaging Differences in T 1 may be exploited to produce desired contrast between tissues of interest in MRI. In conventional MR imaging, the recorded signal intensity is proportional to the

25 12 magnitude of the longitudinal relaxation and may also be weighted to reflect differences in T 1 and/or transverse relaxation times (denoted by T 2 and T 2 *). It is instructive to consider the example of two substances X and Y, where substance X has a shorter relaxation time (i.e. shorter T 1 ) than substance Y. After the RF pulse places the magnetization fully in the transverse plane, both substances would have transverse magnetization vectors equal to their respective equilibrium magnetizations. If the MR signal was recorded at this time, the difference in the recorded signals from the substances would reflect differences in their equilibrium magnetizations only, e.g. those due to the differences in their respective densities of 1 H nuclei. If instead of recording the signal at this time, however, a time delay was allowed such that the longitudinal magnetization of substance X recovered fully while that of substance Y was still recovering, application of a second identical RF pulse would result in the same transverse magnetization vector for substance X but a smaller one for substance Y compared to after the first RF pulse. Recording the MR signal at this point would result in signal intensities for each substance that reflect not only their equilibrium magnetizations, but also the differences in their longitudinal relaxation times. In practice, popular MR imaging sequences rely on repeated cycles of RF pulse application and signal recording or readout (generally one readout per line of the image) in order to form an image based on steady-state magnetization. In the case of gradient-echo imaging, the RF pulse is typically not applied to the point of placing the magnetization fully in the transverse plane; instead, the pulse is applied with a prescribed magnetic field strength B 1 and for a specified duration t p, which has the effect of rotating the magnetization vector away from the z-axis by a flip angle α given by [80]: (1.3) α = γ B 1 t p The time delay between B 1 pulses that allows some recovery of the longitudinal magnetization is a scan parameter known as the repetition time (TR). If the transverse component of the magnetization is destroyed (or spoiled ) through dephasing gradients before each subsequent B 1 pulse, the transverse magnetization at the time of readout will achieve a steady-state after several B 1 pulses. This spoiled gradient-echo (SPGR) steady state signal will be given by [81]:

26 13 (1.4) S = M 0 sinα 1 e TR T 1 1 cosα e TR T 1 In practice, the TR is often chosen to be as short as possible to allow for the shortest possible scan times given sufficient signal-to-noise ratio. Given a fixed TR and M 0, a plot of this signal for varying α appears in Fig. 1.1 below: Figure 1.1. A plot of the relative SPGR signal vs. flip angle for different T 1 s (legend) and fixed TR and M 0. The SPGR signal S is normalized to S 0, which represents the maximum SPGR signal obtainable (i.e. using α = 90 0 and TR >> T1). At low flip angles (< 3 o ), signal is consistent across all T 1 s; at higher flip angles, shorter T 1 species produce greater signal than longer T 1 species. As illustrated by Fig. 1.1, α may be chosen to produce a desired difference in signal between tissues of different T 1 s assuming that they have the same equilibrium magnetization. Under

27 14 these conditions, the pixel intensities of a resulting image will show shorter T 1 species as being hyperintense relative to longer T 1 species, thus achieving a T 1 weighting in the image T1 Measurement While T 1 weighted images provide contrast that reflect differences between tissue T 1 s, they provide no absolute information about the longitudinal relaxation times present. Several techniques have been developed to actually measure T 1 s, and to produce spatial T 1 maps with pixel values equal to measured T 1 s. The traditional methods for T 1 estimation have been based on inversion recovery (IR) and saturation recovery (SR), where signal samples are taken at fixed intervals during the recovery of longitudinal magnetization after the application of either an inversion or saturation pulse respectively. The case of IR-based T 1 measurement is shown for a given T 1 species in Fig. 1.2 below: Figure 1.2. Illustration of IR-based T 1 measurement. Magnetization is inverted and then sampled at a fixed times (four shown here) during longitudinal relaxation. For each inversion pulse, one sample is taken, and longitudinal magnetization is allowed to fully recover (TR 5*T 1 ) before the next inversion pulse. T 1 is estimated from fitting the sampled data to the relaxation curve. Here, samples are taken at four specific sample times (or inversion times, TI), which may then by fit to the relaxation curve to extract T 1. IR and SR methods typically require long TRs of approximately 5 times the estimated T 1 for each signal readout, in order to ensure near-complete

28 15 relaxation before the next readout. This requirement results in long overall acquisition times using these methods (e.g. > 30 minutes for whole brain T 1 maps) which greatly limits their clinical applicability [81]. Accelerated techniques based on the Look-Locker scheme involve sampling the recovering magnetization at several inversion times during a single TR using small flip angle pulses that perturb the recovery in a well-characterized way [82]; however, 3D implementations of such approaches have generally required sampling multiple lines of the image at once, introducing errors due to spin-spin dephasing and lowering the effective resolution of the image [83]. More recently, rapid techniques of T 1 mapping based on SPGR imaging have been developed. Given the dependence of the SPGR signal on T 1, multiple SPGR acquisitions at different flip angles (the method of Variable Flip Angles (VFA)) and/or TRs may be fit to the SPGR equation, generally after linear reformulation or after simplifying assumptions are made [81, 84]. This technique benefits from acceleration made possible by SPGR, particularly towards requiring high-resolution 3D imaging, but is subject to inaccuracies and imprecision due to lower dynamic range and SNR when compared with IR and SR techniques [81, 84] Motivation for Vessel Wall T1 Measurement As described in Section 1.2.5, MR contrast based on the differences in T 1 offers diverse and useful information in the identification of vulnerable plaque. T 1 -weighted imaging has been proven to be an effective tool in exploiting these differences towards detecting vulnerable plaque features such as intraplaque hemorrhage, inflammation and neovasculature. However, there are certain technical limitations of T 1 weighted imaging that can diminish its utility in vessel wall assessment for a variety of applications. In particular, T 1 -weighted imaging has two main limiting characteristics: 1) mixed image contrast due to incomplete T 1 weighting, and 2) dependence of output on choice of scan parameters (e.g. TR, flip angle). In recent years, a number of clinical applications that have traditionally relied on T 1 -weighted imaging have benefitted from the application of spatial T 1 mapping techniques in order to overcome these limitations [85, 86]. When compared to T 1 weighted imaging, spatial T 1 mapping has the ability to improve detection of clinical features of interest, and the physical measurements of T 1 produced can provide more useful prognostic information than the relative signal intensities of T 1 weighted images. Here we

29 16 discuss the motivation for spatial T 1 mapping over conventional T 1 weighted imaging of the vessel wall in terms of three main potential benefits: 1) greater sensitivity and specificity of T 1 - detectable markers of vulnerability; 2) improved risk stratification of subjects demonstrating these markers; and 3) the ability to perform comparative studies. 1) Greater T 1 sensitivity and specificity. The ability of any MR imaging technique to detect T 1 -based features of interest depends on the degree of T 1 weighting achievable. For example, the SPGR-based MRIPH technique is more heavily T 1 -weighted than the T 1 W fast spin echo (FSE) imaging incorporated into multicontrast protocols, which accounts for its greater demonstrated sensitivity to intraplaque hemorrhage [70, 72]. As described in the previous section, the signal produced by T 1 weighted imaging techniques is proportional to the equilibrium magnetization M 0. Because of this dependence on M 0, it is impossible to achieve complete T 1 weighting using conventional T 1 weighted imaging techniques. Therefore, a long T 1 region with high proton density may appear isointense with a nearby shorter T 1 region with lower proton density, potentially obscuring the presence of microhemorrhage within the vessel wall. This problem is further compounded by the dependence of the T 1 contrast on the choice of imaging parameters, which are typically optimized for certain T 1 s of interest. Because the degree of T 1 shortening caused by paramagnetic markers of vulnerable plaque such as intraplaque hemorrhage and contrast perfusion depends on their local concentrations, a gradient of T 1 s is possible, making it difficult to select optimal parameters. Spatial T 1 mapping offers theoretical advantages of independence from both M 0 and the choice of imaging parameters, although these can still serve as potential sources of error in T 1 mapping techniques (see Section 1.3.6). Practically, spatial T 1 mapping may offer completely T 1 weighted vessel wall images with measurement error corresponding to the signal-to-noise ratio requirements of the approach. The resulting improvement in T 1 contrast could allow detection of more subtle T 1 changes, and the lack of dependence on M 0 would inherently improve the specificity of detected changes. 2) Improved risk stratification. In T 1 weighted images, the intensity values of the pixels present relative differences between tissues within the field of view (FOV) under the mixed contrast achieved by the pulse sequence. Therefore, analysis of vulnerable risk markers in T 1 weighted vessel wall images is typically qualitative in terms of image contrast. In detecting intraplaque hemorrhage using MRIPH scans, for example, images are reported as being

30 17 either positive or negative for the presence of intraplaque hemorrhage based upon visual assessment by a radiologist [70]. This qualitative assessment is based on perceived hyperintensity relative to the surrounding vessel wall, and provides only a binary assessment of risk. Unlike T 1 weighted images, spatial T 1 maps provide physical measurements of T 1, and thus provide a gradient of information for each scan that can be studied and correlated to outcomes and risk factors. Instead of a binary decision of placing a patient in either a highor low- risk group as per the reading of MRIPH scans, T 1 maps may allow a finer degree of risk stratification i.e. numerical risk score, based on the degree of T 1 shortening observed. 3) Comparative studies. Contrast in T 1 weighted images is mixed not only in the sense of the physical tissue characteristics such as T 1 and proton density, but also in terms of variables associated with the scanning equipment and procedures themselves. As noted earlier, T 1 weighted image contrast is influenced by the equilibrium magnetization M 0, which can vary with not only differences in proton density, but also due to coil factors such as spatial profiles of receive sensitivity and B 1 transmission. Furthermore, the pixel values may also be scaled by auto-calibration features of the particular scanner, such as receiver gain and dynamic range correction. Many of these factors differ from scan to scan and between scanners, and their influence limits the ability to perform any comparative analysis between images acquired during different sessions. Since spatial T 1 maps record physical measurements of T 1, a wide variety of comparative studies may be performed. For example, longitudinal studies may be performed to track progressive changes in vessel wall T 1 in the same patient over time, in order to study the association of T 1 with clinical and pathological changes in the patient. Data may be readily compared between different groups in imaging studies, including data acquired at different sites and scanners. Such comparative studies have the potential to provide new insights into the natural history of vessel wall disease and provide new tools in diagnostic, prognostic, and therapy-response imaging Needs for Vessel Wall T 1 Measurement Approach In order for spatial T 1 mapping to provide the proposed advantages over existing T 1 weighted vessel wall imaging approaches, it must satisfy specific criteria that are important for their utility in the assessment of vulnerable disease. These needs are as follows: 1) measurement over wide

31 18 range of possible T 1 s; 2) high spatial resolution; 3) volumetric coverage; and 4) suppression of signal from flowing blood. 1) Wide-range T 1 measurement. Due to a variety of technical challenges associated with T 1 measurement from the vessel wall (discussed in Section 1.3.6), no studies have previously characterized the range of T 1 values possible in health and disease. Therefore, in designing an approach for vessel wall T 1 measurement, consideration must be given to physical environment of the vessel wall and pathological changes associated with progressive disease, in order anticipate the range of possible T 1 s. Under T 1 -weighted imaging, the healthy vessel wall often appears isointense with muscle, which has a T 1 ~ ms at 3T, suggesting similar estimates for healthy vessel wall [87]. T 1 shortening due to gadolinium (indicating neovascularization and/or inflammation in post-contrast images) or methemoglobin (indicating neovascularization and/or intraplaque hemorrhage in pre-contrast images) may shorten this T 1 by up to several hundred milliseconds, depending upon their local concentrations. Inflammation may also be associated with edema, which may plausibly lead to a lengthening of T 1 due to the deposition of fluid with longer T 1 s. The absence of data regarding T 1 s in the vessel wall requires that a suitable technique allow for reliable measurement of T 1 s across the entire range of plausible values within the vessel wall; this will be taken to be the biological range of T 1 s (~ ms) for the purposes of this thesis, in consideration of the anticipated pathological changes described. This is an important consideration, as popular T 1 mapping approaches for other clinical applications have been shown to have a limited range of effective T 1 estimation [84]. 2) High spatial resolution. The vessel wall represents one of the smallest anatomical imaging targets for clinical MRI. Its small dimensions require high spatial resolution in the crosssectional plane of the vessel in order to resolve features of interest. In healthy subjects and those in earlier stages of disease, high resolution is required to resolve the vessel wall itself, which is relatively thin in the absence of disease. For those in later stages of disease, the vessel wall is considerably thicker; high resolution then becomes important for resolving focal markers of vulnerability within the vessel wall. Several imaging studies across multiple vascular beds have shown a mean wall thickness of approximately 1mm in non-diseased adult arterial walls, while diseased walls have been found to be up to 6mm thick [88-90]; this presents 1mm as a reasonable candidate for cross-sectional pixel dimension. The importance

32 19 of high resolution along the longitudinal axis of the vessel wall depends upon the vascular tree being imaged. When imaging tortuous vessels, high resolution is necessary to avoid partial volume effects in the slice direction of axial acquisitions; however, this requirement is less stringent when imaging relatively straight stretches of vessel, as typically found in the common carotid artery. Given that T 1 measurement is more technically constrained than T 1 weighted imaging, the resolution achieved by the MRIPH sequence (0.5x0.5x1mm) [70] represents an ideal resolution for a spatial T 1 mapping approach. However, since neovascularization, inflammation, and microhemorrhage are associated with systemic atherosclerosis even at earlier stages of disease, it may be possible to take T 1 measurements from coarser sample regions (compared to typical imaging voxel sizes) to gain representative estimates of T 1 changes associated with these disease markers. 3) Volumetric coverage. Progressive atherosclerosis is associated with increasingly focal disease, eventually resulting in plaque formation at sites along the length of the vessel. The accuracy of 2D vessel wall MRI is strongly dependent upon the correct placement of the imaging slice, such that the disease features of interest lie within the imaging field of view. Implementing a 3D approach to spatial T 1 mapping expands the coverage of the measurements along the length of the vessel to ensure that such features are not missed due to slice placement. As no previous studies have examined the association of T 1 measurements with vessel wall disease, the development of a volumetric technique to measure T 1 would afford the opportunity to track pathological T 1 changes from early systemic disease to late focal disease. A volumetric T 1 dataset would also allow comparison with spatial profiles of other measures associated with atherosclerotic disease, such as wall shear stress, in order to associate related features over the course of the disease. 4) Flow suppression. Depending upon the MR imaging technique used, flowing blood through the vessel may have the potential to cause artifacts that can degrade vessel wall images. The two main classes of techniques used in spatial T 1 mapping are inversion recovery and SPGR imaging, both of which are associated with the appearance of bright blood due to the in-flow effect in axial vessel wall images. In the case of SPGR imaging, static tissues achieve a steady-state magnetization, but flowing blood does not; instead, each B 1 pulse excites fresh blood with fully relaxed magnetization that has entered the imaging slice/volume, creating a proton density weighted signal for the blood [80]. The result is the appearance of high

33 20 intensity for the blood, which can make discrimination of the wall from the vessel lumen difficult. Therefore, T 1 weighted SPGR vessel wall imaging uses flow suppression techniques to null the signal from blood [73]. The same requirement is necessary for spatial T 1 mapping in order to avoid the effect of errors due to flow artifacts on vessel wall T 1 measurements Challenges Despite the potential benefits, there are considerable challenges associated with performing spatial T 1 mapping for the vessel wall. Traditional approaches for T 1 mapping are associated with long acquisition times, which limit their utility in the clinical setting. Accelerated T 1 mapping methods have been developed, though the reduction of scan time has typically come at the expense of signal-to-noise ratio (SNR) and spatial resolution, both of which are particularly important considerations when scanning the relatively small vessel wall. Higher-field imaging is able to provide an increase in SNR that can mitigate these issues; however, B 1 inhomogeneity becomes at significant problem at higher field strengths, resulting in imperfect knowledge of the transmitted flip angle and concomitant errors in T 1 measurements. Therefore, an accurate highfield T 1 mapping approach requires some method of B 1 inhomogeneity correction. In the following subsections, we consider each of these challenges. 1) Scan time, resolution and SNR. Much of the challenge faced by MRI scientists that have sought to develop clinical methods for T 1 measurement has been in the long acquisition times associated with accurate quantitative imaging. These long acquisition times have implications for the SNR and achievable resolution of the image; specifically, SNR is directly proportional to each voxel dimension and to the square root of the acquisition time. Therefore, longer scan times allow for higher resolution or an increase in SNR; shorter scan times require either lower resolution or a drop in SNR; and higher resolution for a fixed field of view generally leads to a drop in SNR. MRI imaging techniques are generally optimized for specific applications through tradeoffs between scan time, resolution and SNR, but the long acquisition times associated with traditional IR- and SR-based T 1 measurement restrict the flexibility available in the optimization process. For measuring T 1 in the vessel wall, the combined needs of high resolution, clinically-feasible scan times and volumetric imaging suggest the use of SPGR-based VFA T 1 mapping, which has shown the greatest efficacy in

34 21 rapid 3D T 1 mapping among existing approaches [84]. However, the choice of flip angles is a strong determining factor for accuracy and precision in VFA T 1 mapping approaches, which is often limited to a small range of T 1 s of interest. The application of VFA T 1 mapping to study the vessel wall in health and disease requires that the technique allows effective T 1 mapping across the anticipated range of values and accounts for SNR variation due to inhomogeneity of M 0 across the vessel wall. 2) B 1 inhomogeneity. At typical imaging field strengths, the effect of the B 1 field is not completely homogeneous across the tissues being imaged. Physically, this inhomogeneity is the result of two main effects. First, tissue conductivity allows for the generation of current in the presence of the alternating B 1 field; by Maxwell s laws, this current opposes the changing B 1 field and can result in shielded regions where the effective B 1 is reduced. Secondly, the wavelength and speed of light are reduced in a strongly dielectric medium, such as most water-containing tissues; this may result in the formation of standing wave patterns of constructive or destructive interference within the image when the size of the object is on the order of one wavelength. B 1 inhomogeneity may also stem from technical reasons, such as variability in slice profiles. Since the flip angle of the magnetization depends upon B 1 (as described in Section 1.3.2), B 1 inhomogeneity can result in differences between the prescribed and effective flip angles. While this inhomogeneity is generally slowly varying relative to the image resolution, it presents an important source of error in T 1 mapping techniques, particularly in SPGR-based T 1 mapping techniques which rely on accurate knowledge of the effective flip angle. Methods of spatially mapping the B 1 field have been developed to address this problem. Conventional double-angle -based methods of B 1 mapping enable calculation of B 1 from the ratio of two spin-echo or spoiled-gradient echo images, where one is acquired at twice the flip angle of the first [91]; however, these methods involve long acquisition times due to the long TRs necessary to ensure the absence of T 1 -effects in the B 1 estimation. Faster methods of B 1 mapping based on rapid steady-state imaging have been developed more recently, and have been shown to improve the accuracy of spatial T 1 maps [92-95].

35 Hypothesis A method for accurately and reproducibly measuring T 1 from the vessel wall can be created. This method should satisfy the following criteria (as detailed in Section 1.3.5): wide range of T 1 estimation ( ms), high spatial resolution ( 1mm cross-sectional dimension), volumetric coverage, and flow suppression. 1.5 Overview of Thesis As described, spatial T 1 mapping has the potential to provide valuable new information towards the detection and characterization of vulnerable atherosclerosis. However, the vessel wall is a difficult target for T 1 mapping due its small size, requiring high resolution and careful consideration of SNR. Traditional T 1 mapping techniques based on IR and SR require prohibitively long acquisition times, and related accelerated approaches based on Look-Locker sequences are not well suited to volumetric imaging. While VFA-based T 1 mapping approaches have been proven effective for rapid volumetric T 1 mapping, they require several (>2) SPGR acquisitions in order perform wide-range T 1 mapping, where each SPGR acquisition may require several minutes due to the resolution and SNR requirements for the vessel wall. This thesis will introduce a new T 1 mapping approach based upon the VFA method that is designed to address the constraints imposed by the vessel wall. This technique incorporates a new strategy for flip angle selection known as the Wide Range Angle (WRA) set, which is optimized for the wide range of biological T 1 s anticipated in the vessel wall while requiring only 2 SPGR acquisitions. Chapter 2 will outline a theoretical description of the VFA method and present simulation results that systematically investigate the effect of flip angle choices on the accuracy and precision of T 1 measurements. These results are used to identify the WRA set as the most suitable candidate set for vessel wall T 1 mapping. Experiments designed to test the WRA set along with suitable methods for B 1 inhomogeneity correction are described in phantoms and in healthy volunteer spinal cords, along with their results validating the widerange T 1 mapping capability of the WRA set. Initial vessel wall T 1 mapping experiments performed in ex-vivo rabbit thoracic aorta samples are presented, with the results showing good agreement with reference IR T 1 measurements. The chapter concludes with a description of initial in-vivo vessel wall T 1 mapping experiments in volunteer carotid arteries, using spatial saturation bands outside of the imaging volume for flow suppression. The results of these initial

36 23 carotid experiments suggest the utility of this approach in detecting T 1 shortening associated with both contrast perfusion and intraplaque hemorrhage. Chapter 3 will describe some potential future improvements to the vessel wall T 1 mapping technique, including pulsatile vessel wall motion compensation, and also explore the possibilities of utilizing this technique in characterizing other forms of vessel wall pathology.

37 24 Chapter 2 Spatial T 1 Mapping Technique for the Vessel Wall 2 Spatial T 1 Mapping Technique for the Vessel Wall 2.1 Introduction The progression of atherosclerotic disease from early stages towards high-risk, rupture-prone plaque relies heavily on inflammatory responses to the presence of oxidized LDL cholesterol within the vessel wall. As described in Chapter 1, neovascularization within the vessel wall has emerged as a very strong pathological marker of risk in recent years [15]. Neovessels have been shown to have a highly fragile ultrastructure, including abnormal endothelial cell morphology and aberrant inter-endothelial cell junctions [21]. As a result, neovessels serve as a direct entry pathway into vessel wall for both leukocytes and the LDL cholesterol substrate upon which they act [22-24]. Additionally, neovessels are responsible for the extravasation of RBCs into the vessel wall, where the RBC undergo subsequent breakdown and result in the deposition of two important pro-atherosclerotic products: first, the lysis of cholesterol-rich RBC membranes represents an important source of lipid deposition [26]; second, freed hemoglobin can undergo oxidative denaturation to form methemoglobin, which compounds the oxidative stress in the wall [27, 96]. Detection of inflammation and RBC products associated with neovascularization have each been shown to be predictive of high-risk disease, providing evidence for the mechanistic relationship between them [19, 27, 70]. Furthermore, recent evidence has suggested neovascularization and associated extravasation of blood cells may be among the earliest features of atheroma [30, 31]. Given the strong association between the presence of leaky neovasculature and risk of future plaque disruption, the development of techniques to effectively detect neovessels and extravasated blood products may have considerable utility towards the assessment of vascular health. Non-invasive imaging using T 1 -weighted MRI has previously demonstrated the ability to identify high-risk features of complicated plaques, including neovascularization, inflammation and intraplaque hemorrhage. Neovascularization can be detected using gadolinium-based T 1 - contrast agents, which are paramagnetic and cause local T 1 -shortening in neovessels-rich areas of the vessel wall, thus appearing as enhanced on post-contrast T 1 -weighted images [64]. Dynamic contrast-enhanced MRI (DCE-MRI) studies have shown that the rate of enhancement following

38 25 contrast administration is associated with neovascular density [67, 68]. The rate of contrast enhancement has also been associated with the degree of macrophage infiltration into the vessel wall, suggesting that enhancement may be indicative of the neovascular permeability [66, 67]. Non-contrast T 1 -weighted MRI has shown excellent sensitivity for the detection of intraplaque hemorrhage in complicated carotid plaque, and the absence of MR-detected hemorrhage has demonstrated strong negative predictive value for future cerebrovascular events [70, 72]. Neovascularization, inflammation and intraplaque hemorrhage are thus all detectable as T 1 markers under protocols combining pre- and post-gadolinium contrast imaging. Despite the demonstrated utility of T 1 -weighted imaging in detecting plaque features associated with neovascularization, the sensitivity of such techniques relies on the achievable T 1 contrast in the images. For example, the use of more heavily T 1 -weighted SPGR imaging over conventional spin-echo based T 1 imaging improved the sensitivity for intraplaque hemorrhage from 81% to 100% in symptomatic carotid arteries [59, 70]. In T 1 -weighted imaging, the influence of the equilibrium magnetization term M 0 introduces a mixed contrast that inherently limits the achievable sensitivity to T 1 markers. Additionally, the contrast produced in T 1 -weighted images relies on selection of imaging parameters that are optimized to highlight differences between anticipated tissue T 1 s; however, no previous studies have examined T 1 s of the vessel wall in health and disease, making it difficult to optimize contrast parameters for specific T 1 s. An alternative approach to T 1 -weighted imaging for the detection of T 1 markers is spatial T 1 mapping, whereby images are generated with pixel values equal to measured tissue T 1 s. In representing T 1 measurements, spatial T 1 maps provide the maximum possible contrast based solely on differences in T 1, thus providing maximum sensitivity to T 1 based markers. A spatial T 1 mapping approach for the vessel wall may allow detection of more subtle changes in T 1 associated with lower concentrations of paramagnetic T 1 markers, potentially allowing the detection of low-density neovascularization and microhemorrhage at earlier stages of the atherosclerotic disease process. By providing physical measurements of T 1 instead of relative pixel intensities dependent on scanner- and session-specific parameters, spatial T 1 maps could readily allow longitudinal studies to examine changes in T 1 markers with progressive disease and/or therapy, as well as enable comparative studies between different patient groups and study centers. By correlating risk factors and outcomes with measured T 1 s, T 1 maps may also allow for improved risk stratification in the form of a quantitative risk score, as opposed to the binary

39 26 assignment of subjects into high- vs. low-risk groups based on the presence or absence of hyperintensity seen on T 1 -weighted images. In order to provide the proposed advantages over T 1 -weighted imaging, a suitable T 1 mapping approach requires several features that are central to the utility of the existing T 1 -weighted approaches. These include: high spatial resolution ( 1mm 2 in-plane) to resolve features of interest; volumetric coverage in order to localize disease markers along the length of the vessel; and flow suppression to avoid partial volume effects from flowing blood. The first two requirements represent significant challenges for traditional T 1 mapping approaches based on inversion recovery (IR) or saturation recovery (SR) methods, which are associated with prohibitively long acquisition times for in-vivo volumetric imaging [81]. A number of accelerated T 1 measurement approaches have been developed to allow significant reduction in acquisition times for clinical applications. In particular, the method of Variable Flip Angles (VFA) involves the acquisition of multiple SPGR volumes at different flip angles, allowing the derivation of a voxel-wise T 1 map by fitting the acquisitions to a linearized signal model [81, 84]. Different strategies have been proposed for the selection of flip angles in the VFA approach, each with different consequences on the accuracy and precision of T 1 estimation within the biological range (approximately 300ms 3000ms at 3T). Deoni and Rutt introduced a strategy known as DESPOT-1 based on the selection of two flip angles optimized for a given T 1 of interest [84]. While this strategy requires only two SPGR acquisitions, accurate and precise T 1 mapping has been shown to be limited to a given range of the T 1 of interest, thus limiting the applicability of this strategy to the vessel wall where a broad range of T 1 s may be anticipated. Alternative strategies requiring three or more SPGR acquisitions have been developed to allow T 1 mapping over the whole biological range, but the need for more acquisitions results in greater overall scan times, again potentially limiting the applicability to the vessel wall due to the long scan times required for each high-resolution 3D acquisition [81, 97]. Ideally, a flip angle selection strategy for the vessel wall would require a minimal number (i.e. two) of acquisitions while providing effective T 1 mapping across the anticipated broad range of T 1 s. In the present work, the development of a spatial T 1 mapping approach suitable for the vessel wall is described. To this end, the possibility of wide-range T 1 mapping using only two SPGR acquisitions is explored in order to develop a suitable flip angle selection strategy for a VFAbased T 1 mapping approach. A theoretical basis for this investigation is presented, followed by

40 27 results of simulations showing a systematic effect on the range of T 1 measurement accuracy and precision with dual-angle optimization for progressively longer T 1 s. From these results, a candidate flip angle set known as the Wide-Range Angle (WRA) set is identified. Experimental validation of the WRA set is performed in phantoms and in the spinal cords of healthy volunteers using a spatial B 1 mapping approach to compensate for B 1 inhomogeneity. Initial validation of this approach to vessel wall T 1 mapping is shown in ex-vivo thoracic aorta samples from four rabbits, with results showing good agreement with reference IR-measured T 1 values. Finally, the approach is applied using spatial saturation bands outside of the imaging volume for flow suppression in order to obtain T 1 measurements from the carotid arteries of three volunteers. 2.2 Theory Basic VFA Theory The steady-state signal intensity produced by SPGR imaging previously given in Section (Eq. 1.4) may be rewritten as follows: (2.1) S i = M 0 sinα i 1 e TR T 1 1 cosα i e TR T 1 where S i is the signal amplitude acquired at flip angle α i. Denoting E 1 =, this equation may be transformed into the linear form Y i = mx i + b: (2.2) S i S = E i sinα 1 + M i tanα 0 (1 E 1 ) i By acquiring multiple SPGR signals at different flip angles and fixed TR, linear regression may be used to estimate the slope m = E 1 and intercept b = M 0 (1 E 1 ). T 1 and M 0 may then be derived as follows:

41 28 (2.3) T 1 = TR lnm (2.4) M 0 = b 1 m The selection of flip angles α i has been examined by several authors with the aim of finding the smallest angle set that allows accurate and precise T 1 mapping. Wang et al. used a numerical approach to show that optimal precision for a estimating a specific T 1 given a fixed TR could be obtained using two optimized flip angles [97]. Deoni et al. demonstrated that these angles could be obtained through maximization of the product of the dynamic range and SNR for a given TR/T 1 combination [84]. The influence of the dynamic range may be understood by considering that the measured SPGR signal at each flip angle represents a single data point on the regression line. Given that all possible points along the line have the same measurement SNR, greater distance between the two measured points along the line reduces the effect of the noise on the regression, leading to better estimation of the slope (and hence T 1 ). However, all possible points do not have the same measurement SNR; as demonstrated in Fig 1.1, the SPGR signal vs. flip angle profile is non-monotonic with a maxima occurring at a specific flip angle known as the Ernst angle α E for each T 1. From the perspective of maximizing the SNR of each measured point, the angles should lie as close to the Ernst angle as possible; this would result in placement of the points as close to one another as possible and centered at the (x,y) coordinates corresponding to the Ernst angle on the regression line, given by: (2.5) S αe S αe, tanα E sinα E Thus, while increasing the separation between measured points along the regression line improves T 1 estimation by minimizing the effect of SNR, it generally results in a decrease of SNR as the two points move away from the midpoint given by the location of the Ernst angle along the regression line. Hence, finding the optimal flip angle pair for a given T 1 requires identification of the placement of points along the regression line that provides the optimal

42 29 tradeoff between these competing effects. Deoni et al. used plots of the dynamic range-snr product over a wide-search space to find that estimate precision for a given T 1 was maximized by choosing flip angles such that: (2.6) S α1 = S α 2 = 0.71S αe Relating the SPGR signal equation for either of the ideal angles with that of the Ernst angle given the above relation, we have: (2.7) sinα i sinα = 0.71 E 1 E 1 cosα i 1 E 1 cosα E Using a derivation provided in [84], α i may be solved to yield two solutions, representing optimal angles for the estimation of a given T 1 using this approach. When observed on a plot of SPGR signal vs. flip angle, these angles lie nearly symmetrically on opposite sides of the Ernst angle for T 1target, separated by some distance providing the optimal balance between relative signal and dynamic range along the linear regression line. While this strategy may be used to select ideal angles for estimating a known T 1, this does not represent an optimal solution for measuring a range of T 1 s as present in most tissues, and especially across different tissues and pathological states. In practice, this approach has been applied by choosing ideal angles optimized to a chosen value, referred to here as T 1target, which lies within the anticipated range of T 1 s. This heuristic method of T 1target selection is variable, as no clear strategy exists for how T 1target should be chosen with respect to the anticipated range. Previous studies have demonstrated that this approach can result in significant errors in the presence of a broad range of tissue T 1 s and/or insufficient SNR [81, 97]. However, no studies to date have systematically investigated the effect of the choice of T 1target on the range of effective T 1 estimation or the SNR requirements associated with this dual-angle approach Proposed Flip Angle Selection Strategy Reexamination of Fig 1.1 reveals a characteristic asymmetry present in the SPGR signal vs. flip angle curves. This asymmetry is a result of a signal bias against longer T 1 s under SPGR imaging that exists given that all other parameters (M 0, α, TR) are the same; this signal bias is evidenced

43 30 by a monotonically decreasing relationship between relative signal and T 1 at all flip angles. Based on this signal bias against long T 1 s, the selection of optimized angles based on a shorter T 1target within the range of interest may result in ineffective T 1 estimation at longer T 1 s within the range. If T 1target was chosen to be at the longest end of the range or beyond, however, effective T 1 estimation may still be possible at the shorter end of the range due to signal bias favoring the shorter T 1 s. To illustrate this point, Fig. 2.1 below contrasts the difference in relative signal between SPGR acquisitions at flip angles optimized for a) T 1target = 500ms and b) T 1target = 3000ms, representing shorter and longer T 1 s in the biological range, respectively. Figure 2.1. Flip angle optimization for different choices of T 1target changes the relative signal at each sample point (colored markers) for different T 1 s (represented by differently coloured curves). Since T 1 is estimated from the slope of a linearized form of these curves, the sample point with lower signal in each pair dominates the error in T 1 estimation. Considering only the lower signal sample points in each pair, optimization for a shorter T 1target (left) provides greater signal for shorter T 1 s but lower signal for longer T 1 s when compared to optimization for a longer T 1target (right). Optimization for a longer T 1target provides greater uniformity among the lower signal points across all T 1 s. Since T 1 is estimated from the slope given by regression of the two measured points to the linearized SPGR form (seen in Fig. 2.2), the measurement with the lower SNR of the two will dominate the error in T 1 estimation. In the case of T 1target = 500ms, both flip angles provide high signal at shorter T 1 s, but the minimum signal point for longer T 1 s is much lower than for the shorter T 1 s. For T 1target = 3000ms, the minimum signal point for shorter T 1 s has dropped in comparison to the T 1target = 500ms case, but is higher for longer T 1 s. Furthermore, the minimum

44 31 signal point at T 1target = 3000ms is seen to be significantly more uniform across all T 1 s in the range, thus mitigating the inherent signal bias against long T 1 s in SPGR imaging. Optimization for a longer T 1target may also help to mitigate the effect of signal bias on T 1 estimation by improving dynamic range for longer T 1 s. Since dynamic range is based upon separation along the regression line, Fig. 2.2 below illustrates this bias on plots of the measured points in the linear SPGR coordinate system, with flip angles optimized for a) T 1target = 500ms and b) T 1target = 3000ms. In these plots, the difference in dynamic range is reflected by the distance between the two measured points for each T 1 (marked by different colors in the figure legend). Figure 2.2. Optimization for different choices of T 1target changes the dynamic range between sample points for different T 1 s. Optimization for T 1target = 500ms (left) provides a greater dynamic range (i.e. separation between sample points) for shorter T 1 s than for longer T 1 s; this bias is reversed when optimizing for T 1target = 3000ms (right), providing greater dynamic range for longer T 1 s than for shorter T 1 s. Note that T 1 is estimated from subtle differences in slope between the plotted lines that are not visually apparent in this representation. In the case of T 1target = 500ms, greater separation is observed for shorter T 1 s (e.g. T 1 = 500ms) than for longer T 1 s (e.g. T 1 = 2500ms); this is reversed for the T 1target = 3000ms case, where the measured points for longer T 1 s show greater separation. Therefore, optimization for a longer T 1target acts to create a dynamic range bias in favor of longer T 1 s. The progressive change from signal and dynamic range bias favoring short T 1 s to mitigation of the signal bias and reversal of the dynamic range bias with progressively longer T 1target suggests

45 32 the possibility that there may a choice of T 1target which provides optimal uniformity across the biological range of T 1 s anticipated in the vessel wall. To observe the combined effect of changes in signal and dynamic range bias with increasing T 1target, Fig. 2.3 presents a plot of the product of the minimum signal and dynamic range (S*DR) across the biological T 1 range for six different choices of T 1target and typical imaging parameters (TR = 10ms, M 0 = 1000). Figure 2.3. Plot of the product of minimum signal and dynamic range (S*DR) across the biological range based on optimization for different choices of T 1target. Optimization for progressively longer choices of T 1target within the biological range ( ms) provides greater uniformity of the S*DR product across the entire range. As seen in Fig. 2.3, conventional optimization for T 1target = 1000ms yields an S*DR curve that is highly sensitive for shorter T 1 s, yielding a maximum at T 1 < 1000ms despite the target value, followed by rapid decay at T 1 s longer than 1000ms. Optimization for longer T 1 s in the biological range (T 1target = 2500ms, 3000ms) yields S*DR curves that are lower for shorter T 1 s than in the T 1target = 1000ms case, but higher in the longer half of the biological range, yielding a more uniform profile overall. In the case of T 1target = 4000ms, which represents a target beyond the T 1 range of interest, the S*DR product is lower across the whole biological range when compared with the T 1target = 2500ms, and significantly lower at shorter T 1 s in the range. In the cases of T 1 s within the range of interest, points of inflection are observed when T 1 = T 1target, after which the S*DR product rapidly decays.

46 33 Based upon these observations, I hypothesize that optimization for the longest T 1 in a range of interest lying within the biological range represents the most suitable strategy for flip angle selection. For vessel wall T 1 mapping, where the range of interest is equal to the biological range, I thus hypothesize that selection of T 1target = 3000ms will yield a Wide-Range Angle (WRA) set that provides optimal uniformity of T 1 accuracy and precision across this range given sufficient SNR. In the following section, numerical simulations and experimental work are presented to test this hypothesis. The results are applied towards a vessel wall T 1 mapping approach in subsequent experiments. 2.3 Materials and Methods Simulations Numerical simulations were used to evaluate different dual flip angle sets with S*DR products optimized to progressively increasing T 1target values from 1000ms to 3000ms at intervals of 500ms. Signals were generated using the SPGR equation (Section 2.2.1) using flip angles corresponding to each T 1target, TR = 10ms and M 0 = In order to evaluate the performance of each angle set at different SNRs, Gaussian-distributed complex-valued random noise with zero mean and σ = M 0 /SNR was added to the signals for five choices of SNR between 300 and 800. As defined here, SNR refers to the maximum signal-to-noise ratio per pixel for an image acquired with α = 90 o and TR >> T 1 [81], i.e. the SNR obtained if the transverse magnetization at the start of every readout was equal to the equilibrium magnetization, with full recovery of the longitudinal magnetization before each subsequent B 1 pulse. T 1 was determined from the noisy signals using simple linear regression on the linear SPGR equation. This process was repeated times to find a mean T 1 estimate µ T1 and standard deviation σ T1 for each angle set. The accuracy of each angle set was evaluated using the absolute error in T 1 estimation given by µ T1 T 1, while the precision was evaluated using the T 1 -to-noise ratio (T 1 NR), given by µ T1 /σ T. Candidate angle sets for vessel wall T 1 mapping were deemed to be those that achieved a mean error 10% and a T 1 NR 10 for all T 1 s in the biological range at the lowest SNR possible.

47 B 1 Inhomogeneity Correction Due to the relatively small size of the vessel wall, a suitable T 1 mapping approach would benefit from the use of higher field strengths in order to obtain greater SNR. Clinical 3T scanners provide increased SNR compared with 1.5T scanners, but are associated with significant B 1 inhomogeneity. While traditional methods for B 1 inhomogeneity correction are associated with long acquisition times, a number of rapid methods for spatial B 1 mapping have been developed to allow correction for B 1 inhomogeneity. In this work, a rapid B 1 mapping approach based on actual flip-angle imaging (AFI) is employed for B 1 correction [93]. Briefly, the AFI technique employs a pulse sequence with two identical RF pulses followed by two delays of different duration, where TR 1 <TR 2 ; under these conditions, a pulsed steady state is achieved and B 1 may be derived from the ratio of two gradient echo signals acquired at the beginning of the time intervals TR 1 and TR 2. The AFI technique has been shown to allow correction of T 1 maps obtained using VFA methods in the presence of B 1 inhomogeneity [93]. In the experimental work described in the following sections, the AFI technique was used for B 1 inhomogeneity correction in phantom and volunteer spinal cord VFA T 1 mapping experiments validating the performance of the WRA candidate flip angle set, and in subsequent ex-vivo and in-vivo application of this T 1 mapping approach to vessel wall. Except where otherwise noted, AFI sequence parameters for spatial B 1 mapping in the described experiments were: TR 1 = 30ms, TR 2 = 100ms, TE = 5ms, α = 60 o, actual voxel size = 3x3x6mm, overcontiguous slices, reconstructed voxel size = 3x3x3mm, volumetric coverage matching the associated WRA acquisitions Phantom Experiments Phantom experiments were performed on a 3T MRI system (Philips Achieva, Best, The Netherlands) using a transmit/receive birdcage head coil. Two phantom experiments were performed to validate VFA T 1 mapping using the proposed WRA set. In the first experiment, a 1000mL cylindrical water phantom (Philips) doped with 770mg CuSO4.5H2O was used to validate T1 estimation in the presence of significant B1 inhomogeneity. Data for WRA T 1 maps were acquired with a 3D T1W fast field echo (FFE) sequence with flip angle selection corresponding to the WRA set: TR/TE = 15ms/3.7ms, α = [3 o, 15 o ], FOV = 240x240x48mm, actual voxel size = 1x1x2mm, overcontiguous slices. Spatial B 1 mapping was performed using an AFI sequence as described. Pixel-wise T 1 maps were calculated from the WRA data using the AFI B 1 maps for flip angle correction. In order to assess the accuracy of the corrected WRA T 1

48 35 maps, reference single-slice inversion recovery (IR) data was obtained using the following parameters: TR/TE = 6000ms/9.5ms, TI = [50, 150ms, 200ms, 400ms, 800ms, 1600ms, 3200ms]. Regions of interest (ROIs) drawn within the phantom were used to find mean and standard deviations of the corrected WRA and the reference IR T1 maps, in order to assess the accuracy and precision of the WRA approach. For the second experiment, a phantom was created using 15ml distilled water samples with varying concentrations of MnCl2 (SigmaAldrich, St. Louis, USA) to yield 11 T1s over a range of ms, in order to validate the hypothesized effect of flip angle selection on the range of effective T1 estimation (Fig 2.4). All data for the second experiment (WRA T1 maps, AFI B1 maps, reference IR T1 data) were acquired and analyzed using the same parameters as in the first phantom experiment. Reference IR T1 data was used to determine the relaxation rate (1/T1) for each sample, which demonstrated a linear relationship with MnCl2 concentration consistent with previous studies (Fig 2.5) [99]. Figure 2.4. Phantom experiment designed to test wide-range T1 mapping capability of the WRA set. Left: The phantom apparatus containing 12 samples of varying MnCl2 concentrations was scanned using the birdcage head coil; 11 samples yielding T1s in the range of ms were used for analysis. Right: Corresponding slices from 3D T1W FFE phantom images taken at flip angles of 3o (top) and 15o (middle) and the WRA T1 map produced from them (bottom). Regions of interest used for T1 estimation in each sample are shown overlaid in red.

49 36 Figure 2.5. The inverse of T 1, also known as the longitudinal relaxation rate R 1, demonstrates a linear relationship with the MnCl 2 concentration across all phantom samples Validation in Ex-vivo Rabbit Thoracic Aortas Initial validation of VFA T 1 mapping using the WRA set in the vessel wall was performed in exvivo samples of thoracic aortas from cholesterol-fed New Zealand White rabbits (n = 4). Prior to sacrifice, the rabbits were housed in the Sunnybrook Comparative Research facility in accordance with the Sunnybrook Animal Care committee guidelines. Following sacrifice after 20 weeks of cholesterol feeding, a single sample (1-3cm) of the thoracic aorta was excised from each of the four rabbits and suspended in saline solution at 4 o C for 18 hours. The suspended samples were then left at room temperature for 2 hours before being removed from the saline solution and left to dry on a paper towel for 10 minutes. A custom phantom apparatus with four compartments (i.e. one for each sample) was used for imaging the samples (Fig. 2.6). Before transfer to the apparatus, three of the four samples (vessels 2-4) were each cut into two parts to allow easier placement within their respective compartments; one sample (vessel 1) was left uncut due to its shorter length. After the placement of the samples, the apparatus was filled with perfluorocarbon (3 mol/l Fluorinert, 3M) to reduce susceptibility artifacts induced by air/tissue interfaces [100]. The apparatus was rested upon a large rectangular water phantom placed within the birdcage head coil for scanning. 3D T1W FFE acquisitions corresponding to VFA T 1 maps using the WRA set were obtained using the following parameters: TR/TE = 100ms/13ms, α =

50 37 [6o, 34o], FOV = 32x32x52mm, actual voxel size = 0.25x0.25x5mm, overcontiguous slices, interpolated voxel size = 0.125x0.125x2.5mm. AFI sequence parameters for spatial B1 mapping over the same volumetric coverage were as follows: TR1 = 50ms, TR2 = 250ms, TE = 6.7ms, α = 60o, actual voxel size = 0.5x0.5x8mm, overcontiguous slices, reconstructed voxel size = 0.5x0.5x4mm. Reference IR data was obtained over the same volumetric coverage using the following parameters: TR/TE = 4000/15, TI = [ ], actual/reconstructed voxel size= 0.25x0.25x2.5mm actual, 0.25x0.25x2.5mm reconstructed, slice gap 2.5mm (every other slice). Two representative slices (one through vessels 1 and 2, and another through vessels 3 and 4) from the WRA T1 map were selected for analysis. Mean and standard deviation values for each sample were found from ROIs drawn within the walls of each vessel segment, and compared with corresponding values obtained from the reference IR T1 maps in order to assess the accuracy and precision of the technique. Figure 2.6. Experimental setup for ex-vivo vessel wall validation. Thoracic aorta samples from each of the four rabbits were placed in respective compartments of the phantom apparatus (left). The samples were then suspended in perfluorocarbon within a sealed container prior to scanning (right).

51 Volunteer Experiments 1) Validation in Spinal Cord Prior to application in the vessel wall, 3D T 1 mapping was performed in the spinal cords of three healthy volunteers (two males and one female, ages 24-27) in order to allow initial in-vivo validation of the approach. The spinal cord was chosen as a validation target due to its wellcharacterized T 1 distribution in healthy volunteers as well as its proximity to the carotid arteries, allowing the use of the same imaging setup and sequence parameters. Three volunteers underwent spinal cord scanning to validate the in-vivo accuracy and precision of the VFA T 1 maps obtained using the WRA set. Volumetric scans approximately centered at the C3 disc level (Fig. 2.7) were taken following a VFA protocol described in [101], but employing the WRA set for flip angle selection. Scanning was performed using body coil excitation and a 16-channel neurovascular coil for reception to obtain 3D T1W FFE acquisitions corresponding to the WRA set: TR/TE = 100ms/10ms, α = [6 o, 34 o ], FOV = 212x212x40mm, actual voxel size = 1x1x4mm, overcontiguous slices. Spatial B 1 mapping was performed using an AFI sequence as previously described. ROIs were drawn for gray matter and lateral column white matter on the low-angle (6 o ) FFE acquisition and propagated to each B 1 -corrected T 1 maps. Mean and standard deviation values were computed for each ROI, and the results were compared with reference IR values [101]. Figure 2.7. Prescription of spinal cord VFA acquisitions; volumes were approximately centered at the C3 disc level.

52 39 2) Initial Application to Volunteer Carotid Arteries A volunteer study was conducted to assess the feasibility of in-vivo vessel wall T 1 measurement after receiving approval from the Sunnybrook Health Sciences Centre research ethics board. Written informed consent was obtained from volunteers. Dual-angle VFA using the WRA set was used to perform T 1 mapping in the carotid arteries of three volunteers (n=6 arteries). Two of the three volunteers were healthy, and the third volunteer had a history of symptomatic cerebrovascular disease. Volunteers were scanned using a protocol that included pre-contrast acquisition of WRA T 1 maps, AFI B 1 maps for B 1 inhomogeneity correction over the same volumetric coverage, and a corresponding MRIPH scan to identify MR-depicted intraplaque hemorrhage. In healthy volunteers, WRA T 1 maps were also obtained following administration of 5ml Gadovist (Bayer Healthcare, Montville, NJ, USA). In order to suppress the signal from flowing blood, spatial saturation bands were placed above and below the imaging volume for all 3D T1W FFE acquisitions corresponding to WRA T 1 maps (Fig. 2.8); additional parameters were as follows: TR/TE = 100ms/3.7ms, α = [6 o, 34 o ], FOV = 160x160x100mm (axial orientation), actual voxel size = 1x1x5mm, overcontiguous slices, interpolated voxel size = 0.25x0.25x2.5mm, NSA = 2, acquisition time = 10:48 per flip angle. AFI B 1 maps were acquired using the following additional parameters: TR 1 = 30ms, TR 2 = 100ms, TE = 5ms, α = 60 o, actual voxel size = 2.5x2.5x10mm, overcontiguous slices, reconstructed voxel size = 2.5x2.5x5mm. The MRIPH scan was performed using ProSet fat suppression (pulse type = 1331) and an inversion pre-pulse (TI = 560 ms) for flow suppression; the remaining parameters were as follows: TR/TE = 100ms/4.1ms, TFE factor = 79, α = 15 o, FOV = 270x191x50mm (coronal orientation), actual voxel size = 1x1.2x0.5mm, overcontiguous slices, reconstructed voxel size = 0.5x0.5x0.5mm, NSA = 2, acquisition time = 8:40. Axial reformats were obtained for the MRIPH scans to allow visual comparison with the axial T 1 maps.

53 40 Figure 2.8. Prescription for volunteer carotid WRA acquisitions. 40 slices were acquired through a 10cm imaging slab (red), with spatial saturation bands (blue) placed above and below to suppress the signal from flowing blood. All data processing was performed in the Matlab environment (The Mathworks, Natick, MA, USA). To facilitate the processing workflow, a graphical user interface (Fig. 2.9) was created using GUIDE (The Mathworks). Each T1W FFE volume corresponding to the VFA T 1 maps acquired pre- and post- (when available) contrast was loaded into the interface. For registration of the datasets, the low-angle (6 o ) pre-contrast acquisition was used as a reference to which all other acquisitions were registered. In order to register the datasets along the slice direction (i.e. z-direction), the bifurcation was used as a landmark in each acquisition to allow registration using a simple z-shift. Following this, registration within the imaging plane (i.e. x-y plane) was performed using a normalized mutual information approach [102]. For each slice, contours were then drawn for the lumen and outer wall on the reference 6 o acquisition in order to manually segment the vessel wall; these contours were propagated to all other registered acquisitions and the resulting T 1 maps. B 1 maps were generated from the AFI acquisitions and resampled to match the resolution of the T1W FFE volumes. Vessel wall T 1 maps were then generated along with corresponding M 0 maps from the segmented T1W FFE volumes using simple linear regression with voxel-wise correction of flip angle using the resampled B 1 maps.

54 41 Figure 2.9. Screenshot of graphical user interface used for volunteer carotid data processing. Low- (left column) and high-angle (right column) acquisitions obtained pre- (top row) and post- contrast (bottom row) were all registered to the reference low-angle pre-contrast acquisition (top left). ROIs manually drawn on the reference acquisition were propagated to the other registered acquisitions, before application of the resampled B 1 map and generation of resulting pre- and post-contrast T 1 maps. In several instances, regions of low signal within the vessel wall were noticed on the T1W FFE acquisitions (as shown in Fig. 2.10) and found to correspond with regions of low M 0. These low M 0 regions may be the result of a number of physiological or technical factors, including susceptibility artifacts from calcifications/tissue interfaces, partial volume effects due to limited resolution, and/or spatial blurring due to vessel wall motion effects. Lower M 0 in these regions results in decreased SNR, resulting in uncertainty regarding accuracy and precision of T 1 measurements from these areas. In order to systematically collect vessel wall T 1 data given the presence of these low SNR regions, an M 0 threshold was defined such that M 0threshold = µ M0 σ M0, where µ M0 and σ M0 are the mean and standard deviations of M 0 over the vessel wall; T 1 maps were then filtered using the corresponding M 0 maps such that all vessel wall pixels with M 0 < M 0threshold were excluded from analysis. From each resulting 3D T 1 map, histograms were

55 42 generated to examine T 1 distributions over each slice and over the entire volume (i.e. summing over all slices). Histograms were compared pre- and post-contrast in healthy volunteers, and between left and right sides in all volunteers. Figure Example of the appearance of a low signal region on the low-angle WRA acquisition found to correspond to a region of low M 0. Lower SNR results in uncertainty regarding accuracy and precision of T 1 measurements from these regions. 2.4 Results Simulation Results Fig below shows profiles for mean error and T1NR (as defined in Section 2.3.1) over the biological T 1 range for different choices of T 1target at this minimum SNR threshold. From these profiles, a minimum SNR = 475 was found to yield candidate flip angle sets for vessel wall T 1 mapping that satisfied the requirements of mean error 10% and a T 1 NR 10 for all T 1 s in the biological range. Two candidate angle sets emerged at this SNR, corresponding to optimization for T 1target = 2500ms and T 1target = 3000ms (WRA set).

56 43 Figure Profiles for mean error (left) and T 1 NR (right) across the biological range based on different choices of T 1target at SNR = 475. Optimization for T 1target = 3000ms (the WRA set; green) provides the most uniform profiles across the biological range. Of the tested candidate angle sets, optimization using the WRA set yielded the highest uniformity of accuracy and precision across the biological range. Accuracy and precision profiles for different choices of T 1target matched theoretical predictions based on the S*DR product profiles across the biological range; shorter T 1target choices provided markedly greater accuracy and precision at the lower end of range compared with the higher end, while progressively longer T 1target choices provided improved uniformity of accuracy and precision profiles across the entire range. Simulations indicate that optimization for the longest T 1 in a range of interest within the biological range shows the greatest uniformity of accuracy and precision across the desired range. For vessel wall T 1 mapping where the range of interest is the entire biological range, simulations indicate that the WRA set provides the highest accuracy and precision at the minimum SNR threshold = Phantom Results Fig shows a representative profile through the reference IR T 1 map, the uncorrected VFA T 1 map obtained using the WRA set, and the B 1 -corrected WRA T 1 map produced by the first phantom experiment. The application of AFI B 1 correction to the WRA T 1 map yielded a profile of comparable uniformity to the reference IR T 1 map, and demonstrated high accuracy and precision across the profile (WRA: 385 +/- 13 ms vs. IR: 397 +/- 1 ms). These results indicate

57 44 effective T 1 measurement using the WRA set at a lower biological T 1, and the capability of B 1 correction in the presence of significant inhomogeneity across the water phantom using the AFI technique. Results of the second phantom experiment are shown in Fig B 1 -corrected WRA T 1 measurements showed high accuracy (mean error = 4.7%; maximum error = 8.2%) and precision (mean T1NR = 12.3; minimum T1NR = 7.1) across all tested T 1 samples. These results validate the T 1 mapping capability of the WRA set using B 1 correction across the biological T 1 range. Figure Representative T 1 profile through water phantom of first phantom experiment. B 1 correction is applied to the WRA profile (red) to yield a corrected WRA profile (green), which shows good agreement with the reference IR profile (blue) (Corrected WRA: 385 +/- 13 ms vs. IR: 397 +/- 1 ms).

58 45 Figure WRA-measured T 1 values plotted against reference IR-measured T 1 values for each T 1 sample in the second phantom experiment. Error bars show two standard deviations of the WRA-measured values. The WRA approach showed high accuracy (mean error < 8.2%) and precision (T 1 NR > 7.3) across all samples Ex-Vivo Rabbit Thoracic Aorta Results Representative slices from T 1 W-FFE images, reference IR and WRA T 1 maps for each aortic specimen are shown in Fig below. Inspection of the T 1 maps revealed large regions of T 1 corresponding to vessel wall, shorter T 1 regions corresponding to perivascular fat, and long T 1 regions corresponding to the presence of residual saline. Good overall agreement was observed between vessel wall T 1 s measured using each technique.

59 46 Figure Each excised thoracic aorta sample is shown alongside a representative T 1 W-FFE image slice, reference IR T1 map, and WRA T1 map. Note that Vessels 2-4 were each cut in half for easier placement in the phantom apparatus; both halves of each vessel were imaged side-by-side. Good overall agreement between the IR and WRA techniques is observed within the vessel walls of each sample. An example of perivascular fat (blue arrow, labeled PF) is shown on the IR T 1 map of Vessel 2; this region is spatially displaced due to chemical shift artifact in the WRA map. An example of artifactual saline (pink arrow, marked AS) is shown on the IR T1 map of Vessel 3; this is also visible on the corresponding region of the WRA T 1 map. Certain differences resulting from technical factors are observable in the T 1 maps. Careful examination reveals that pixels corresponding to perivascular fat are visible in the IR map, but are sometimes spatially mislocated due to chemical shift artifact in the WRA map (best observed in Vessel 2). Additionally, the vessel wall appears to be thicker in the WRA T 1 map relative to the IR T 1 map in some instances, which may be due to blurring along the slice direction of the 3D WRA acquisition (absent in the 2D IR acquisition).

60 47 Table 2.1 below shows the mean and standard deviation of T 1 s measured using each technique from each vessel specimen, with perivascular fat and artifactual saline excluded. Table 2.1. IR- and WRA-measured T 1 values from each thoracic aorta specimen, with perivascular fat and artifactual saline excluded. Vessel IR WRA /- 148 ms 950 +/- 229 ms /- 131 ms 910 +/- 190 ms /- 240 ms 992 +/- 152 ms /- 200ms 913 +/- 241 ms Histograms showing the T 1 distribution profiles for each technique are shown for each vessel and across all vessels in Fig below. Good agreement between major peak locations in each vessel wall demonstrates the accuracy of the WRA measurements relative to the IR measurements. However, major peaks appeared broader in the WRA T 1 profiles, which showed a higher noise floor across all T 1 s than the IR T 1 profile. The effect of this imprecision was somewhat reduced in histograms taken over all vessels, which showed better agreement between overall T 1 profiles between the WRA and IR techniques.

61 48 Figure T 1 histogram profiles for each vessel (top panel) and across all vessels (bottom panel) show good agreement for peak locations between techniques. The reduced precision of the WRA technique results in broadening of peaks and higher noise floor compared with the reference IR technique Results of Volunteer Experiments 1) Spinal Cord Results Fig shows representative spinal cord B 1 and WRA T 1 maps obtained from a volunteer, with ROIs overlayed for gray and white matter.

62 49 Figure Spinal cord B 1 map (left) and WRA T 1 map (right) from a volunteer. ROIs drawn for gray matter (red) and lateral column white matter (blue) on the low-angle WRA acquisition are propagated to the B 1 and T 1 maps (overlayed). The results of spinal cord T 1 measurements obtained using the WRA set are shown in Table 2.2 below. Across the three volunteers, mean errors of 8.7% in gray matter and 1% in white matter were found relative to reported IR-measured T 1 values [101], with a T1NR 11.3 in all cases. The WRA set demonstrated comparable accuracy and reduced precision when compared with reported VFA values approximately optimized to a T 1target = ms (mean errors of 2.2% in gray matter and 4.3% in white matter, T1NR 15.5) [101]. Table 2.2. WRA-measured T 1 values from volunteer spinal cords. Gray matter and white matter T 1 s both show good agreement with reported IR and conventionally-optimized VFA values [101]. Gray Matter T1 (ms) White Matter T1 (ms) Volunteer ± ± 51 Volunteer ± ± 31 Volunteer ± ± 74 Literature IR [101] 972 ± ± 23 Literature VFA [101] 994 ± ± 47 2) Volunteer Carotid Artery Results Fig 2.17 shows representative results of 3D T 1 map generation for a single volunteer carotid artery. T 1 histogram distributions are shown for each slice and for the whole artery following low-m 0 filtering. Several regions appearing as low T 1 on the unfiltered map correspond to low- M 0 regions that were excluded from analysis in the filtered T 1 map.

63 50 Unfiltered T 1 Map Unfiltered M 0 Map Filtered T 1 Map Filtered M 0 Map Overall T 1 Distribution Slice T 1 Distributions Figure Pre-contrast 3D T 1 map generation for a single volunteer artery. Several regions of apparently low T 1 in the unfiltered T 1 map (top left) correspond to regions of low M 0 (top right), resulting in lower SNR. Exclusion of low M 0 regions from analysis produces filtered T 1 and M 0 maps (center row), from which overall and slice-by-slice T 1 distributions are produced (bottom row).

64 51 Table 2.3 shows mean and standard deviation values for measurements taken over all slices for each volunteer carotid artery measured (n=6). Pre-contrast T 1 measurements showed a mean T 1 = 867 ± 278ms across all volunteer carotid arteries, with a higher mean T 1 across healthy volunteer arteries (T 1 = 900ms) compared with the arteries of the volunteer with CVD (T 1 = 800ms). Post-contrast T 1 measurements performed in healthy volunteers showed a mean T 1 = 363 ± 187ms across all measured arteries, representing T 1 shortening in the approximate range of ms for each artery. Overall pre- and post-contrast distributions for each measured artery are shown Fig below. Considerable overlap exists among both pre- and post-contrast distributions from all volunteers. Table 2.3. Pre- and post-contrast T 1 measurements taken over the whole vessel for each volunteer carotid artery. Pre- Contrast Post- Contrast Vessel Mean T1 Stdev T1 Mean T1 Stdev T1 Healthy Vol. 1 Left Healthy Vol. 1 Right Healthy Vol. 2 Left Healthy Vol. 2 Right CVD Vol. Left CVD Vol. Right

65 52 Figure Pre-contrast (top panel) and post-contrast (bottom panel) T 1 distributions taken over the whole vessel for each volunteer carotid artery. Considerable overlap is observed among pre- and post-contrast distributions across all vessels. Marked T 1 shortening is observed in post-contrast distributions from healthy volunteer arteries. Corresponding MRIPH scans performed for each patient revealed five of six volunteer arteries as negative for the presence of IPH; the left carotid artery of the volunteer with prior history of CVD (3D T 1 map shown in Fig. 2.17) was found to be positive for IPH. Low T 1 regions in the filtered T 1 maps were observed in slices 6-8 and found to correlate with IPH regions on the corresponding MRIPH scan. Fig 2.19 below shows representative matching slices from the MRIPH scan and unfiltered T 1 map.

66 53 Figure Matching slices from the MRIPH scan (left) and T 1 map (center) in the diseased artery. Hyperintensity corresponding to IPH on the MRIPH scan correlates with regions of low T 1 seen on the map. The T 1 distribution of the slice map (right) shows several low T 1 pixels (< 500ms; red box) that represent IPH region pixels. 2.5 Discussion Significance of T 1 variation in the vessel wall Intraplaque hemorrhage and neovascularization, both of which represent features of high-risk disease, have been associated with hyperintensity relative to the surrounding vessel wall in preand post-contrast T 1 W images respectively. Given that this hyperintensity results from local T 1 shortening, vessel wall T 1 estimation may lead to improved sensitivity to the presence of these high-risk features. However, T 1 differences in the vessel wall are not limited to these two features alone, and may vary considerably based upon composition and structure. For example, the presence of closely-bound protons in the structures of both lipid and collagen allow for magnetization transfer with the surrounding lattice, resulting in T 1 shortening. Accumulation of both lipids and collagen within the vessel wall are characteristic of atherosclerotic disease, but the risk associated with each of these features differs, as do the risks associated with intraplaque hemorrhage and neovascularization. Furthermore, there may be considerable variability in tissue composition across the vessel wall, with many voxels in quantitative T 1 maps representing different mixed tissue compositions with averaged T 1 estimates. Before the utility of quantitative T 1 mapping in detecting high-risk disease features can be realized, the effects of various tissue T 1 contributions and the degree of biological variability must be characterized through correlation of measured T 1 s with pathological features on excised plaque specimens and in animal models of atherosclerosis. At present, the T 1 mapping technique described in this thesis may have utility as

67 54 an investigational method for understanding changes in T 1 associated with pathology, an important first step towards determining the potential clinical utility of any T 1 mapping approach towards assessment of high-risk vascular disease. In the following subsection, the relevance of the overall study and the present limitations are described; the remaining subsections further elaborate on the significance and limitations of findings from each component of the study Study relevance and limitations The aim of this study was to test the hypothesis that a method for accurately and reproducibly measuring T 1 from the vessel wall could be created, with the following criteria defining suitability of the method: wide range of T 1 estimation ( ms), high spatial resolution ( 1mm cross-sectional dimension), volumetric coverage, and suppression of signal from flowing blood. This thesis described a new method that was designed to satisfy these technical criteria, using a dual-angle VFA approach for high-resolution, volumetric T 1 measurement and spatial saturation bands for flow suppression; however, previous authors reported only limited ranges of effective T 1 estimation associated with conventional dual-angle VFA approaches [81, 84]. To investigate the possibility of wide-range T 1 mapping using an alternative dual-angle VFA approach, numerical simulations were conducted to systematically characterize the effect of flip angle selection on the range of effective T 1 estimation. The results of these simulations led to the unreported finding that optimal accuracy and precision of T 1 estimation over a range of interest may be achieved by choosing flip angles to maximize the signal-dynamic range product for the longest expected T 1 in the range. As a special case of this general finding, the criteria of wide range of T 1 estimation ( ms) for the vessel wall was satisfied by choosing flip angles to maximize the signal-dynamic range product for T 1 = 3000ms, where I refer to this choice of flip angles as the Wide-Range Angle (WRA) set. The accuracy and precision of the described method was verified using in-vitro, ex-vivo and invivo experiments. Phantom experiments allowed assessment of the method under controlled conditions, in order to validate the wide-range capability of the WRA set and assess the effectiveness of the Actual Flip-Angle Imaging (AFI) technique to correct for errors due to B 1 inhomogeneity. T 1 estimation in the spinal cords of three healthy volunteers allowed in-vivo validation of the method using an established protocol in a site with well-characterized tissue T 1 values. Validation of the method in the vessel wall was performed as an ex-vivo experiment in

68 55 excised rabbit thoracic aortas, where the results demonstrated the accuracy and precision of T 1 estimation using the WRA set as compared to reference IR measurements. Taken together, these results suggest the appropriateness of the described method for in-vivo vessel wall T 1 mapping; however, the absence of a suitable reference technique for in-vivo vessel wall T 1 estimation prevented the assessment of accuracy in this setting. While IR measurements are generally taken as being the reference standard for comparison when evaluating new clinical T 1 mapping approaches, no established technique exists for IR measurement of vessel wall T 1 values, likely due to prohibitively long acquisition times that prevent adequate imaging of the relatively small vessel wall in the presence of gross patient motion. Therefore, while the preliminary results of T 1 estimation in volunteer carotid arteries demonstrated the satisfaction of the technical criteria for vessel wall T 1 mapping as outlined, assessment of the accuracy of in-vivo vessel wall T 1 estimation using this method may rely on future histological validation using vessel wall specimens excised after imaging, in order to obtain quantitative associations between the concentration of T 1 biomarkers (e.g. methemoglobin) and in-vivo T 1 estimates, and compare the results with those predicted using known biomarker relaxivities. The reproducibility of the described approach also remains to be assessed. The experimental format of this study is similar to that of several previously reported studies that described and investigated the effectiveness of new T 1 estimation approaches, where reproducibility measures were generally omitted from validation experiments if the evaluated technique showed good agreement with reference IR measurements across multiple independent experiments [81, 84]. In this study, the results of phantom, ex-vivo rabbit vessel wall, and in-vivo spinal cord T 1 estimation using the described approach were independently shown to agree with reference IR measurements, illustrating consistent agreement with the reference standard under different conditions. However, given that reproducibility of the vessel wall T 1 estimation using the described method represented one of the main aims of the study, conclusive testing of the study hypothesis requires repeated application of the method to the vessel wall and a comparison of the results for reproducibility. Since the limited volunteer carotid data reported in this thesis only represent findings from a small pilot group in whom the described approach was tested for satisfaction of technical criteria, these reproducibility measures have yet to be obtained. Once the reproducibility of the technique has been assessed in several volunteers, the results may help to characterize sources of variability between measures as being technical, physiological or

69 56 environmental in nature, which may ultimately help to contribute to our understanding of vessel wall T 1 variation under normal and adverse conditions. Overall, this study has introduced a new method for T 1 estimation that has been validated for accuracy and precision in several experimental settings, and satisfies the technical criteria outlined as necessary for vessel wall T 1 mapping. While preliminary results from volunteer carotid T 1 mapping using this method suggest that it may be useful in detecting T 1 changes associated with disease, validation of vessel wall T 1 findings against histological specimens and comparison of repeated measures in the same volunteer arteries are necessary to conclusively validate the hypothesis that the described method allows for accurate and reproducible T 1 measurement from the vessel wall Flip angle selection strategy The need for high-resolution volumetric T 1 mapping within clinically feasibly scan times motivated the use of a VFA based approach. Each 3D T1W FFE acquisition requires long scan times due to the need for sufficient SNR and the large number of phase encoding steps required for the desired resolution and coverage, thus making minimization of the number of acquisitions a design objective. While as few as two acquisitions at different flip angles are needed for VFAbased T 1 estimation using linear regression, the range of this dual-angle approach has been previously shown to be limited to a small subset of the biological range [81], making it inappropriate for an investigative vessel wall T 1 mapping technique where T 1 s may plausibly be expected to fall anywhere within the biological range. Given the combined goals of minimizing the number of acquisitions and biological-range T 1 mapping, the effect of flip angle choice on the range of dual-angle T 1 estimation was systematically investigated for the first time. A new theoretical model for flip angle selection was proposed based on observations that optimization for longer choices of T 1target yielded S*DR product profiles that were increasingly uniform across the biological T 1 range. It was hypothesized that optimization for the longest T 1 in the biological range, taken to be T 1target = 3000ms (the WRA set), would yield optimal uniformity of both accuracy and precision of T 1 estimates across the biological range. Numerical simulations were conducted to test this hypothesis and to find a minimum SNR threshold at which dual-angle based VFA T 1 mapping could yield T 1 estimates with mean error < 10% and T 1 NR > 10. Results of the simulations confirmed the hypothesis, yielding satisfactory angle sets for T 1target = 2500ms

70 57 and T 1target = 3000ms at minimum SNR = 475. These simulation results suggest that while a T 1target = 3000ms yields optimal uniformity and precision, the range of T 1target choices between ms may perform comparably to one another; this provides a certain margin of error with regard to B 1 inhomogeneity, as long as a corresponding B 1 map is obtained. For instance, consider that at a TR = 100ms was used for in-vivo carotid T 1 mapping in this study, optimization for T 1target = 2500ms yields flip angles of α = [7 o, 37 o ] while a T 1target = 3000ms yields α = [6 o, 34 o ]. For the current study where the latter angle set was used for prescription, regions where the true flip angle was within 10% greater than the prescribed flip angle (as commonly seen within the relatively homogeneous FOV of the vessel within the neurovascular coil) would roughly correspond to the angle set of T 1target = 2500ms, which still allows for accurate and precise wide-range T 1 mapping so long as the flip angle correction is known. The results of phantom experiments validated the wide-range T 1 mapping capability of the WRA set and the use of the AFI B 1 mapping approach to provide effective flip angle correction for the T 1 maps in the presence of significant B 1 inhomogeneity. For the in-vivo spinal cord validation experiment, the use of a previously published protocol allowed comparison of the T 1 measurements obtained using the WRA set to reported IR- and dual-angle VFA-measured T 1 s (using conventional optimization to spinal cord T 1 s). The WRA set showed comparable accuracy and reduced precision with respect to conventional optimization to spinal cord T 1 s, consistent with predictions based on the differences in their S*DR product profiles. However, the WRA set still showed high overall precision and good agreement with reported IR-measured T 1 s. This demonstrates an important consideration regarding the potential of the WRA set to replace conventional optimization in clinical T 1 mapping protocols: in practice, clinical T 1 mapping protocols are frequently implemented without explicit quantification of SNR; instead, flip angles are chosen using conventional optimization for a T 1 somewhere in the range of interest, voxel size is chosen based on desired resolution, and the remaining imaging parameters are selected to provide high signal in each of the flip angle acquisitions based on visual assessment. As is the case in the spinal cord protocol used, the resulting SNR is often above the minimum SNR threshold identified in this study, and is thus amenable to the use of the WRA set over conventional optimization while still maintaining high accuracy and precision. In such situations, the use of the WRA set results in no practical loss of T 1 estimation ability near the

71 58 physiological T 1 target of the conventional optimization, and provides the added benefit of expanding the range of T 1 estimation significantly. In addition to allowing investigation of T 1 s in previously uncharacterized areas such as the vessel wall in this study, the WRA set also avoids T 1 aliasing that may occur when a T 1 longer than the T 1target appears as a lower T 1 (within the expected range of effective T 1 estimation) when using conventional optimization. While other flip angle selection strategies exist for wide-range T 1 mapping, the requirement of three or more acquisitions limits their utility for high-resolution volumetric T 1 mapping when the minimum length of each acquisition is long. Furthermore, the simplicity of flip angle selection in the dual-angle approach may explain its continued popularity over existing wide-range methods in which the selection process is more complex; the WRA set offers the simplicity of the conventional dual-angle selection strategy while also providing widerange T 1 estimation. Finally, the results of the current study also provide new insight into how optimization using the conventional dual-angle strategy should be done for a limited range of T 1 s. While past authors have arbitrarily chosen a T 1target within the range of interest when selecting flip angles, simulation results suggest that optimization for the longest T 1 in the range of interest is the ideal strategy for providing high accuracy and precision over the range; the WRA set is a special case of this general strategy, where the range of interest is the whole biological range as needed for investigating T 1 s in the largely unstudied vessel wall Ex-vivo vessel wall T 1 measurement Because of the relative absence of previous data regarding vessel wall T 1 s in health and disease, the results from the vessel wall T 1 mapping experiments performed here must be considered from both biological and technical perspectives. From a biological perspective, the IR T 1 maps obtained from the ex-vivo rabbit thoracic aorta samples provide a sense of the physiological heterogeneity that may be anticipated in the vessel wall, particularly in the absence of fat suppression. Standard deviations of the T 1 maps taken over all pixels (after background thresholding) were on the order of several hundred milliseconds; however, ROIs drawn within the vessel wall and avoiding perivascular fat and artifactual saline showed much lower standard deviations, with T 1 NR as high as 8. Mean values from these ROIs were in the range of ms, which were generally consistent with predicted vessel wall T 1 values and likely still represent a mixture of tissues (fibrous tissue, intravascular lipids, etc.) from the cholesterol-fed

72 59 rabbit aortas. VFA T 1 mapping using the WRA set showed comparable results for mean vessel wall T 1 s, but showed reduced precision when compared to the IR-measured T 1 s. The consequences of this reduced precision are not entirely reflected by comparing the ROI standard deviations from each technique, which are only slightly higher overall for the WRA set with the given imaging parameters. Examination of the histograms for each vessel in the top panel of Fig shows the differences in T 1 distribution profiles using each technique across all pixels in the vessel. While there is good agreement between the major peaks in all cases, Vessels 2 and 4 in particular appear to show additional peaks at the low T 1 range of the IR-measured distributions, likely corresponding to perivascular and intravascular fat. These peaks are not readily identifiable on the WRA-measured distributions for these vessels, apparently due to the higher noise floor of the WRA distribution. While this is partly due to the displacement of fat pixels as a result of chemical shift artifact, this difference between the distributions illustrates a potential consequence of the reduced precision of VFA-based T 1 mapping and emphasizes the importance of considerations for SNR. While the minimum SNR threshold for this study was defined based on the general criteria of mean error < 10% and T 1 NR > 10, further investigation of the association of T 1 with vessel wall physiological in health and disease may reveal the need for greater accuracy and precision in order to distinguish relevant features from one another. Given the mean error in T 1 and T 1 NRs with the vessel wall ROIs, the parameters for the acquisitions as implemented appeared to satisfy the general criteria for accuracy and precision of the technique In-vivo carotid T 1 measurement Application of the vessel wall T 1 mapping approach to volunteer carotid arteries produced promising initial results while also highlighting several limitations of the current approach. Mean T 1 s taken over the entire artery were in the range of ~ ms across all volunteers; in healthy volunteers, the mean T 1 across all arteries was 900ms. While this value falls at the low end of the predicted range based upon the isointensity of vessel wall with skeletal muscle (T 1 ~ ms at 3T [87]) on T 1 W images, this difference may be accounted for by the presence of fat, which has a lower T 1 ~ ms at 3T and is typically suppressed in T 1 W vessel wall imaging. Differences between left and right sides of the same volunteer ranged from ~ ms; while this may represent variability within the limits of expected technical imprecision, the presence of mean differences between sides taken over all slices may reflect a true T 1 difference between them. This possibility is illustrated in the arteries of the volunteer with a

73 60 history of CVD, who showed a lower mean T 1 on the left side shown to have intraplaque hemorrhage on the MRIPH scan, compared with the right side where intraplaque hemorrhage was absent. However, mean T 1 differences were also seen between sides in healthy volunteers despite the absence of intraplaque hemorrhage on both sides. Further investigation is required to investigate the source of these differences between apparently healthy arteries, and whether they may represent physiological differences in lipid content and/or microhemorrhage in early subclinical disease; if so, vessel wall T 1 mapping may have the potential to detect early systemic vascular disease that is invisible under T 1 -weighted imaging due to its diffuse and unremarkable appearance. The utility of acquiring volumetric T 1 maps is demonstrated in the IPH-positive artery of the volunteer with CVD; slices containing focal disease with frank intraplaque hemorrhage showed markedly different distributions from adjacent slices, which showed distributions consistent with those obtained from healthy volunteer arteries. In the two healthy volunteers, the difference between pre- and post-contrast distributions were similar, showing T 1 shortening on the order of ~ ms, consistent with limited previous data regarding the degree of vessel wall T 1 shortening following contrast administration [103]. Having demonstrated that T 1 shortening following contrast administration may be quantified, the greater T 1 sensitivity afforded by the T 1 maps may allow improved measurement of transfer constants associated with plaque neovasculature and inflammation under DCE-MRI. Furthermore, the results suggest the possibility of associating post-contrast T 1 shortening measures with these disease markers in static studies (i.e. single post- contrast acquisitions), as differences in postcontrast distributions may be indicative of differences in the degree of neovasculature and inflammation present within the vessel wall. Comparison of pre- and post-contrast acquisitions may allow detection of additional disease features by examining differential T 1 shortening between different areas of the vessel wall, e.g. lipid core shows relatively little enhancement compared to fibrous tissue. As this study was intended to be serve as an initial investigation into the feasibility of vessel wall T 1 mapping, volunteer recruitment for in-vivo carotid T 1 mapping was low. Given the small size of the dataset obtained (n=6), the reported results among the healthy volunteers or the volunteer with CVD cannot be taken to be representative of their respective populations. The differences observed among the volunteer arteries suggest that considerable T 1 variability may exist between arteries as a whole, as well as between different regions of the same artery. While these

74 61 preliminary results suggest that T 1 shortening associated with both contrast perfusion and intraplaque hemorrhage may be detected in the vessel wall, these experiments must be performed within much larger populations to assess the validity of the observations. While hyperintensity on a corresponding MRIPH scan serves as a surrogate marker for the presence of intraplaque hemorrhage, histological correlation in animal models and/or excised human carotid plaque specimens is important for validating the detection of additional disease features (i.e. neovascularization, lipid core) using the T 1 mapping approach presented. There are also several technical limitations in the current in-vivo approach, including: susceptibility to motion artifacts, manual delineation of vessel contours from T 1 W FFE acquisitions and limited resolution. Motion artifacts in the images can arise from two main sources: pulsatile vessel wall motion and gross patient motion. Given the small dimensions of the vessel wall, particularly in healthy volunteers, even small amounts of motion can result in significant degradation of the FFE acquisitions and the resulting T 1 maps. The long scan times of ~20 minutes associated with obtaining volumetric vessel wall T 1 maps of sufficient SNR are related to both sources of motion artifacts: first, pulsatile vessel wall motion compensation may be performed using a windowed acquisition during a low-motion period in end-diastole, but the increased scan time required by this gating strategy may be prohibitive for clinical application; second, gross patient motion increases with long acquisition times, and can significantly degrade the image quality when gross motion occurs during an acquisition. The technique currently features no pulsatile motion correction due to scan time limitations; data from a small pilot study examining carotid vessel wall motion in healthy volunteers suggests that this may only be a minor source of error at the current resolution of the technique, but it may account for some imprecision in the technique due to partial volume effects across the pixels covering the moving vessel wall. The effect of gross patient motion is mitigated by acquiring each FFE acquisition separately (~10 minutes each, instead of a continuous 20 minute acquisition) and allowing the patient a chance to recover (i.e. swallow, clear throat, readjust position) between acquisitions; rigid registration is then effectively able to correct for shifts in patient position between acquisitions. However, each acquisition is still of considerable length, and is susceptible to intraacquisition motion artifacts that cannot be corrected for. The effect of this motion may be reflected in precision differences between the T 1 maps obtained for the two healthy volunteers. Healthy volunteer 1 showed considerably greater movement than healthy volunteer 2, requiring

75 62 that multiple acquisitions be repeated during the course of the protocol; this may partly account for the larger standard deviations observed in the T 1 maps of healthy volunteer 1. Another important limitation of the technique is the manual segmentation process used for the vessel wall. Accurate assessment of T 1 distributions within the vessel wall relies on accurate delineation of the wall in the T 1 maps. In this study, contours for the lumen and outer vessel wall were manually drawn on the low-angle pre-contrast FFE acquisition (which showed the darkest appearance of blood among all acquisitions) and then propagated to all other registered acquisitions before generation of pre- and post-contrast T 1 maps. Because this process relies on visual assessment of the FFE acquisitions alone, errors may arise in discriminating the vessel wall from surrounding structures and incompletely-suppressed/recovering blood signal. These errors are compounded by partial volume effects resulting from pulsatile vessel wall motion, which can cause a falsely thickened appearance of the wall; contours drawn for the thickened wall then result in T 1 estimation from pixels that are averaged over the true vessel wall and surrounding tissues. The presence of low signal regions on the FFE acquisitions corresponding to low-m 0 regions within the wall may also lead to errors in contour placement, since contours through these regions are typically drawn by extrapolating the arcs from the remainder of the higher signal vessel wall regions. While the filtering process applied excludes the lowest M 0 regions from analysis, areas exhibiting large M 0 dropout due to motion and/or susceptibility artifacts still result in the inclusion of some low-m 0 pixels into the T 1 distributions. Reliable, automated segmentation of the vessel wall may thus result in improvements in the both accuracy and precision of the technique, and may be best achieved through the incorporation of additional co-registered sequences such as time-of-flight or contrast-enhanced angiograms to derive lumen contours. The present resolution of the technique may serve as an additional source of imprecision in the technique. For carotid T 1 mapping, the in-plane resolution (1x1mm 2 ) appeared to be sufficient for resolving the vessel wall and lumen with a minimum of one pixel (before interpolation) across the thickness of the wall in all volunteers. In the left carotid artery of the volunteer with CVD, focal T 1 shortening was identifiable within the vessel wall and readily correlated to intraplaque hemorrhage on the higher in-plane resolution (0.5x0.5mm 2 ) MRIPH scan. However, in regions of healthy volunteer arteries where the vessel wall appeared to be one pixel thick, the appearance of that pixel may have resulted from a mixture of thin, moving vessel wall and

76 63 surrounding background tissues. This partial volume effect likely existed along the length of the vessel as well, given an actual slice thickness of 5mm along the axis of the vessel. While the common carotid was relatively straight in all volunteers, some degree of through-slice blurring was observed through the carotid bifurcation in most instances. Thus, the presence of partial volume effects due to the resolution limits of the technique at present may have resulted in imprecision that may be corrected if higher resolution scans may be achieved at the same SNR. 2.6 Conclusion A method for obtaining in-vivo spatial T 1 maps from the vessel wall was presented. A new strategy for dual-angle VFA flip angle selection capable of accurate and precise T 1 mapping over the biological range of T 1 s for the vessel wall was developed and validated in a series of experiments. The technique was applied to ex-vivo rabbit thoracic aortas, with resulting T 1 maps showing good agreement with reference IR T 1 maps. Preliminary results from in-vivo carotid T 1 mapping in volunteers indicate that the technique is capable of characterizing T 1 changes associated with intraplaque hemorrhage and contrast perfusion within the vessel wall. Future correlation with histology and volunteer experiments involving repeated measures are necessary to verify the accuracy and reproducibility of the technique.

77 64 Chapter 3 Summary and Future Directions 3 Spatial T 1 Mapping Technique for the Vessel Wall 3.1 Thesis Summary This study was performed to investigate the feasibility of T 1 mapping within the vessel wall. An approach was outlined which was designed to satisfy several needs for vessel wall T 1 mapping, including minimal scan time, high accuracy and precision over the wide range of T 1 s possible within the vessel wall, high resolution and volumetric coverage. The choice of a VFA-based implementation was made to best address these needs, but required consideration of additional implementation-specific needs, such as appropriate flip angle selection for allow wide-range T 1 mapping with a minimum number of acquisitions, B 1 inhomogeneity correction and flow suppression. To address these needs, the effect of flip angle selection on the range of T 1 estimation using dual-angle VFA was systematically investigated for the first time, and a new selection strategy based on the Wide-Range Angle (WRA) set was introduced that provides optimal uniformity of T 1 accuracy and precision across the biological range of T 1 s possible within the vessel wall. WRA-based T 1 mapping was validated in phantom and in-vivo spinal cord experiments using an actual flip-angle imaging (AFI) approach for B 1 inhomogeneity correction, showing effective T 1 measurement across the biological range. Initial application of T 1 mapping to the vessel wall was performed in ex-vivo thoracic aorta samples from rabbits, where the WRA-based approach showed good agreement with reference IR measurements. For in-vivo vessel wall T 1 mapping, the technique was augmented with spatial saturation bands placed outside of the imaging volume and applied to the carotid arteries of three volunteers, two of whom were healthy and one who had a history of CVD. Pre-contrast T 1 distributions were comparable across all volunteers, with a mean T 1 = 900ms in healthy volunteers arteries and mean T 1 = 800ms in the arteries of the volunteer with CVD. T 1 shortening of ~ ms was observed following contrast administration in healthy volunteers. Finally, low T 1 measurements (< 500ms) from one side of the volunteer with CVD were found to correlate with hyperintensity on a corresponding MRIPH scan. The preliminary results produced by this study are among the first to indicate that in-vivo vessel wall T 1 mapping is possible, and may be useful in detecting T 1 changes associated with disease; however, the small sample size and lack of histological validation in the current study require that further investigation be performed to support these

78 65 initial findings, along with repeated measures in future volunteer experiments to verify the reproducibility of T 1 estimates obtained using the described approach. 3.2 Future Directions Improvements in SNR The availability of greater SNR provides increased flexibility in choosing parameters to achieve desired resolution and scan time in MRI. In the current study, imaging parameters for the invivo T 1 mapping approach were chosen to satisfy the SNR requirements identified by numerical simulations (SNR = 475 in order to achieve mean error < 10% and T 1 NR > 10 across the biological T 1 range). This SNR was not achievable at the high-resolution and short TRs of clinical vessel wall imaging scans (0.5x0.5mm 2 in-plane, 1mm slice thickness; TR = 10ms). The voxel size of the T 1 mapping approach as implemented was 1x1mm 2 in-plane, slice thickness = 5mm; the decreased resolution likely resulted in increased partial volume effects that may have affected the precision of the T 1 measurements, particularly in the slice direction where some degree of blurring was frequently observed through the bifurcation. While the extent of this blurring appeared to be minor, it is likely that the use of thinner slices and higher in-plane resolution, given the same SNR, may improve the precision of the technique by reducing partial volume effects. However, SNR is proportional to each voxel dimension; therefore, given all other imaging parameters are fixed, an increase in resolution results in decreased SNR. Therefore, any increase in resolution would require a greater starting SNR, such that the resulting SNR after the increase in resolution would still satisfy the minimum SNR requirements identified in the study. A greater starting SNR would also allow for a reduction in scan time over the existing technique, which uses a TR = 100ms and has a scan time of ~10 minutes per flip angle acquisition (~20 minutes per T 1 map). Shorter scan times would result in less gross patient motion during the acquisitions, reducing the presence of artifacts that can degrade the image and result in errors in T 1 estimation from the vessel wall. Outside of scan prescription parameters, major determinants of SNR include the main magnetic field strength, B 0, and the choice of coil. The current study was performed at a field strength of 3T in order to provide nearly double the SNR than the alternative clinical field strength choice of 1.5T. In general, coils designed to maximize SNR for a given imaging target are ideally placed

79 66 as close as possible to the target (in order to maximize sensitivity to signal from the target) and small in size (to reduce sensitivity to background noise). The current study made use of a 16- channel neurovascular coil, which provided multiple coil elements allowing acquisition of the desired volumetric coverage of the carotid bifurcation with good SNR. However, this coil was not specifically optimized for carotid imaging. Greater SNR may be achievable through alternative coil designs, such as a flexible phased-array surface coil placed over and along the neck in close proximity to the carotids. The development and application of customized coils providing greater SNR in T 1 mapping studies may provide significant improvements in accuracy and precision by allowing higher resolution and/or shorter scans, reducing the effects of errors due to partial volume averaging and motion artifacts Pulsatile Vessel Wall Motion Compensation Given that blood flows as periodic pulses through the arteries, the elastic artery wall is subject to periodic expansion and relaxation due to the flowing blood. The resulting wall motion may present a source of error in the T 1 mapping technique described, which assumes that the vessel wall is static within the pixels of the segmented area. If the combined thickness and extent of motion of the vessel wall exceeds the pixel dimension, some degree of partial volume effect will be present, resulting in an averaged signal representing a combination of true vessel wall and background tissues in some pixels. Thus, the extent of vessel wall motion must be characterized in order to assess the potential for partial volume effects due to this motion in the T 1 mapping technique. A small pilot study was performed to characterize the effect of vessel wall motion on T1- weighted images of healthy volunteer carotid arteries at a clinical vessel wall imaging resolution (voxel size = 0.5x0.5x0.8mm). Five healthy volunteers aged were scanned in a 3T Philips Achieva system using a small surface receive coil positioned near the bifurcation of the carotid artery. A bright blood, 3D T1 TFE sequence was used to give a clear delineation between vessel wall and dark background (TR/TE/α = 9.8ms/2.5ms/15 ). The acquired data for each subject was retrospectively rebinned into 20 segments throughout the cardiac cycle with no data sharing between cycles. The number of slices was increased such that both carotid bifurcations were covered, with the resulting imaging times ranging from 4 minutes 58 seconds to 9 minutes 14 seconds. Each cycle was then Fourier transformed and raw k-space data was appropriately taken

80 67 from each cardiac cycle bin to simulate an increase in gating window from 5% in end diastole to 100% in increments of 5%. Vessel images were then segmented using a 2 cluster k-means algorithm that classified the pixel as either vessel or background based on the intensity. From these segmented images, a lumen size was computed, and a mean difference in the phase encoding direction was calculated. Figure 3.1. Lumen Boundary Motion Trace (bottom right) of a column of pixels over 20 acquired phases of the cardiac cycle at the lumen boundary near the carotid bifurcation of a healthy volunteer (grayscale top left, color scaled bottom left). Simulated ECG signal shown (top right) for reference. Relatively little boundary motion is observed in the latter half of the cycle. The result of this analysis of increasing apparent vessel wall dimension with increased gating window size is shown in Fig. 3.2 below. The increase in vessel dimension has a sigmoidal shape with an inflection point near 50%, below which the apparent increase in vessel dimension is less than 1 pixel. Among this volunteer population, this region also corresponds to low vasomotion variability which increases dramatically as the gating window is increased beyond 50%. Thus, these results indicate that the use of a gating window of approximately 50% of end-diastole yields a low motion period during which the carotid artery wall may be imaged at high resolution (0.5x0.5x0.8mm) in a healthy volunteer.