As cine DENSE provides myocardial contours and intramyocardial displacement data, we investigated the use of DENSE to train deep networks to predict intramyocardial motion from contour motion. FlowNet2, an optical-flow convolutional neural network, was used as a comparator/reference, and as the starting point for a DENSE-trained network (DT-FlowNet2). Further, we added a correction network with convolution along time, resulting in a through-time-corrected DENSE-trained network (TC-DT-FlowNet2). TC-DT-FlowNet2 outperformed other methods, providing accurate intramyocardial displacements from myocardial contours. DENSE-based learning of intramyocardial displacements from contours holds promise as a new method for computing strain from the contours of standard cine MRI.
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