  There has been considerable interest in the MR physics-based simulation of a
database of virtual cardiac MR images for the development of deep-learning
analysis networks. However, the employment of such a database is limited or
shows suboptimal performance due to the realism gap, missing textures, and the
simplified appearance of simulated images. In this work we 1) provide image
simulation on virtual XCAT subjects with varying anatomies, and 2) propose
sim2real translation network to improve image realism. Our usability
experiments suggest that sim2real data exhibits a good potential to augment
training data and boost the performance of a segmentation algorithm.
