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.
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