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Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is acardiac magnetic resonance (CMR) technique that allows assessment of cardiacmotion in three orthogonal directions. Accurate and reproducible delineation ofthe myocardium is crucial for accurate analysis of peak systolic and diastolicmyocardial velocities. In addition to the conventionally available magnitudeCMR data, 4D MVM also acquires three velocity-encoded phase datasets which areused to generate velocity maps. These can be used to facilitate and improvemyocardial delineation. Based on the success of deep learning in medical imageprocessing, we propose a novel automated framework that improves the standardU-Net based methods on these CMR multi-channel data (magnitude and phase) bycross-channel fusion with attention module and shape information basedpost-processing to achieve accurate delineation of both epicardium andendocardium contours. To evaluate the results, we employ the widely used Dicescores and the quantification of myocardial longitudinal peak velocities. Ourproposed network trained with multi-channel data shows enhanced performancecompared to standard U-Net based networks trained with single-channel data.Based on the results, our method provides compelling evidence for the designand application for the multi-channel image analysis of the 4D MVM CMR data.

Automated Multi-Channel Segmentation for the 4D Myocardial Velocity Mapping Cardiac MR