Abstract #1309

Evaluation of Automatic Processing of Quantitative Perfusion Images in Patients with Suspected Coronary Artery Disease

Matthew Van Houten¹, Xue Feng¹, Yang Yang², Patricia Rodriguez Lozano¹, Christopher Kramer¹, and Michael Salerno³

¹University of Virginia, Charlottesville, VA, United States, ²Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³Stanford University, Stanford, CA, United States

Synopsis

We developed high resolution quantitative perfusion sequence, along with DCNN network to automatically segment the image sets for motion correction. We deployed the acquisition and post-processing on patients with suspected coronary artery disease and compared the results to their coronary angiography findings. Our sequence and processing successfully match the angiography results.

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