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Advisor: Chang, Lin-Ching
Cardiac magnetic resonance imaging (CMRI) has been proven to be a valuable source of diagnostic information concerning heart health. One application, myocardial blood flow (MBF) quantification using first-pass contrast-enhanced myocardial perfusion, has aided the detection of coronary artery disease and provides an accurate evaluation of myocardial ischemia, an identifier of coronary artery stenosis. However, the image and analysis requires tedious user interaction, increasing the time and effort required to utilize it. In addition, it can introduce subjectivity and variability into the data analysis, which further limits the potential use of the modality. This dissertation presents several automated image algorithms to increase the accuracy, consistency, and efficiency of CMR image , and validates them on large, clinical datasets.
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