Cardiovascular diseases remain the leading cause of death worldwide. Noninvasive imaging techniques for diagnosis of ischemic heart disease (IHD) are necessary tools in clinical care of patients with suspected IHD. Cardiovascular magnetic resonance imaging (CMRI) provides distinct advantages compared to other modalities for diagnosis of CMD including lack of ionizing radiation and higher in-plane resolution. Recent landmark studies have shown that up to 50% of the patients referred for diagnostic testing have IHD with no obstructive coronary artery disease. The vast majority these patients have coronary microvascular dysfunction (CMD), which carries a high risk of adverse events. In this dissertation, we aim to develop CMRI techniques that enable more accurate and reliable assessment of CMD. Stress/rest first-pass perfusion CMRI is the only validated and widely used CMRI technique for detection of CMD. However, its clinical performance is limited by the so-called subendocardial dark-rim artifact (DRA), which mimics the presence of CMD. By examining the interplay between CMRI acquisition and cardiac mechanics, in Chapter 3, we propose a deep learning-enabled method to automatically detect and suppress DRA. In Chapter 4, we propose a novel imaging marker for CMD on the basis of cyclic changes in myocardial blood volume (MBV) during the cardiac cycle. There are no CMRI techniques for reliable quantification of the relative change in MBV from systole to diastole. We developed a high-resolution hybrid 3D/2D pulse sequence to quantify cyclic MBV changes and showed its feasibility in large animal studies.
In Chapter 5, we focus on MBV reserve as a marker of IHD and propose a practical Ferumoxytol-enhanced CMRI approach to rapidly quantify MBV reserve and showed its advantages to myocardial stress/rest T1 reactivity for detection of ischemia in a swine model. In Chapter 6, we took on a key challenge in translation of CMRI methods for assessment of CMD, specifically the need for a cost-effective large animal model. We propose an interventional CMRI framework at 0.55T with a novel catheter device design to create a swine model of CMD as a key step for future translational research in improving CMRI techniques for diagnosis of CMD.
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