Cardiac MRI feature tracking by deep learning from DENSE data

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.