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.
@article{arxiv.1902.10815,
title = {Generalizing Deep Learning MRI Reconstruction across Different Domains},
author = {Cheng Ouyang and Jo Schlemper and Carlo Biffi and Gavin Seegoolam and Jose Caballero and Anthony N. Price and Joseph V. Hajnal and Daniel Rueckert},
journal= {arXiv preprint arXiv:1902.10815},
year = {2023}
}