Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, ArtifactsDeep learning (DL) has demonstrated promise for fast, high quality accelerated MRI reconstruction. However, current supervised methods require access to fully-sampled training data, and self-supervised methods are sensitive to out-of-distribution data (e.g. low-SNR, anatomy shifts, motion artifacts). In this work, we propose a self-supervised, consistency-based method for robust accelerated MRI reconstruction using physics-driven data priors (termed VORTEX-SS). We demonstrate that without any fully-sampled training data, VORTEX-SS 1) achieves high performance on in-distribution, artifact-free scans, 2) improves reconstructions for scans with physics-driven perturbations (e.g. noise, motion artifacts), and 3) generalizes to distribution shifts not modeled during training.
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