  We look into the robustness of deep learning based MRI reconstruction when
tested on unseen contrasts and organs. We then propose to generalize the
network by training with large publicly-available natural image datasets with
synthesized phase information to achieve high cross-domain reconstruction
performance which is competitive with domain-specific training. To explain its
generalization mechanism, we have also analyzed patch sets for different
training datasets.
