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Abstract #1164

VORTEX-SS: Encoding Physics-Driven Data Priors for Robust Self-Supervised MRI Reconstruction

Arjun Desai1,2, Beliz Gunel1, Batu Ozturkler1, Brian A Hargreaves1,2, Garry E Gold2, Shreyas Vasanawala2, John Pauly1, Christopher Ré3, and Akshay S Chaudhari2,4
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3Computer Science, Stanford University, Stanford, CA, United States, 4Biomedical Data Science, Stanford University, Stanford, CA, United States

Synopsis

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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