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Abstract #0451

Fully automated aortic 4D flow MRI large-cohort analysis using deep learning

Michael B Scott1, Haben Berhane1, Justin Baraboo1, Cynthia K Rigsby2, Joshua D Robinson2, Patrick M McCarthy1, S Chris Malaisrie1, Ryan J Avery1, Bradley D Allen1, Alexander Barker3, and Michael Markl1
1Northwestern University, Chicago, IL, United States, 2Lurie Children's Hospital of Chicago, Chicago, IL, United States, 3University of Colorado, Anschutz Medical Campus, Aurora, CO, United States

A fully automated pipeline using four convolutional neural networks was designed to perform analysis of aortic 4D flow MRI, including preprocessing (eddy current correction, noise masking, and antialiasing), 3D segmentation of the aorta, quantification of mean flow-time curves and peak velocities. The analysis pipeline was run on a total of 2084 4D flow MRI studies and compared against manual analysis in a subset of 69 studies. Median segmentation Dice score for the ascending aorta was 0.93 [0.90 – 0.95]. Pipeline-based quantification of ascending aortic peak velocities demonstrated bias of -0.05 m/s versus manual analysis [LOA: -0.26 to 0.15 m/s].

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