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Abstract #0777

Automatic quantification of ultra-high resolution quantitative first-pass perfusion imaging using deep-learning based segmentation and MOCO

Matthew Van Houten1, Xue Feng1, Yang Yang2, Austin Robinson3, Craig Meyer1, and Michael Salerno1,3
1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 3Department of Medicine, University of Virginia, Charlottesville, VA, United States

While quantitative first-pass quantitative perfusion imaging is an excellent non-invasive tool for the evaluation of coronary artery disease, current processing shortcomings have kept it from widespread clinical use. In this study, we developed a pipeline which robustly and automatically segments, registers, and quantifies flow with our ultra-high resolution quantitative perfusion sequence.

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