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Abstract #3892

Spatiotemporal Filtering of Myocardial ASL Data: Implications in Detection and Diagnosis of Coronary Artery Disease

Terrence Jao1, Zungho Zun2, Padmini Varadarajan3, Ramdas Pai3, Krishna Nayak4

1Biomedical Engineering, University of Southern California, Los Angeles, CA, United States; 2Division of Radiology, Stanford School of Medicine, Stanford, CA, United States; 3Division of Cardiology, Loma Linda University Medical Center; 4Electrical Engineering, University of Southern California

ASL is a promising technique for the quantification of myocardial perfusion and perfusion reserve in humans, ultimately for the diagnosis of coronary artery disease (CAD). The primary challenges are image misregistration and low signal to physiological noise ratio. Image registration and spatio-temporal filtering techniques can offset these setbacks. In this work, we evaluate how the choice of spatial filter parameters ultimately impacts the sensitivity and specificity of ASL myocardial perfusion reserve as a clinical tool for the diagnosis of CAD.