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 Salerno^1,3

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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