Abstract #0301
Motion2Recon: A Motion-Robust Semi-Supervised Framework for MR Reconstruction
Harris Beg1,2, Beliz Gunel2,3, Batu M Ozturkler2,3, Christopher M Sandino3, John M Pauly3, Shreyas Vasanawala4, Akshay S Chaudhari4,5, and Arjun D Desai3
1Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, United States, 2Equal Contribution, Stanford University, Stanford, CA, United States, 3Electrical Engineering, Stanford University, Stanford, CA, United States, 4Radiology, Stanford University, Stanford, CA, United States, 5Biomedical Data Science, Stanford University, Stanford, CA, United States
Synopsis
In this study, we propose Motion2Recon, a semi-supervised consistency-based approach for robust accelerated MR reconstruction of motion-corrupted images. Motion2Recon reduced dependence on fully-sampled (supervised) training data and improves reconstruction performance among motion-corrupted scans. It also maintained superior performance among non-motion, in-distribution scans, which may help eliminate the need for manual motion detection. All code and experimental configurations are openly available in Meddlr (https://github.com/ad12/meddlr).
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