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We look into the robustness of deep learning based MRI reconstruction whentested on unseen contrasts and organs. We then propose to generalize thenetwork by training with large publicly-available natural image datasets withsynthesized phase information to achieve high cross-domain reconstructionperformance which is competitive with domain-specific training. To explain itsgeneralization mechanism, we have also analyzed patch sets for differenttraining datasets.

Generalizing Deep Learning MRI Reconstruction across Different Domains