Meeting Banner
Abstract #2661

Generalizability and Robustness of an Automated Deep Learning System for Cardiac MRI Plane Prescription

Kevin Blansit1, Tara Retson1, Naeim Bahrami2, Phillip Young3,4, Christopher Francois3, Lewis Hahn1, Michael Horowitz1, Seth Kligerman1, and Albert Hsiao1
1UC San Diego, La Jolla, CA, United States, 2GE Healthcare, Menlo Park, CA, United States, 3Mayo Clinic, Rochester, MN, United States, 4Mayo, Rochester, MN, United States

We show that an automated system of deep convolutional neural networks can effectively prescribe double-oblique imaging planes necessary for acquisition of cardiac MRI. To examine its clinical potential, we assess its component-wise performance by comparing simulated imaging planes predicted by DCNNs against ground truth imaging planes defined by a cardiac radiologist. Performance was assessed on 280 exams including 1252 image series obtained on ten 1.5T and 3T MRI scanners from three academic institutions. We further compare imaging planes acquired by technologists at the time of original acquisition against the same ground truth as an additional external reference.

This abstract and the presentation materials are available to members only; a login is required.

Join Here