  Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a
cardiac magnetic resonance (CMR) technique that allows assessment of cardiac
motion in three orthogonal directions. Accurate and reproducible delineation of
the myocardium is crucial for accurate analysis of peak systolic and diastolic
myocardial velocities. In addition to the conventionally available magnitude
CMR data, 4D MVM also acquires three velocity-encoded phase datasets which are
used to generate velocity maps. These can be used to facilitate and improve
myocardial delineation. Based on the success of deep learning in medical image
processing, we propose a novel automated framework that improves the standard
U-Net based methods on these CMR multi-channel data (magnitude and phase) by
cross-channel fusion with attention module and shape information based
post-processing to achieve accurate delineation of both epicardium and
endocardium contours. To evaluate the results, we employ the widely used Dice
scores and the quantification of myocardial longitudinal peak velocities. Our
proposed network trained with multi-channel data shows enhanced performance
compared to standard U-Net based networks trained with single-channel data.
Based on the results, our method provides compelling evidence for the design
and application for the multi-channel image analysis of the 4D MVM CMR data.
