Abstract #0301

Motion2Recon: A Motion-Robust Semi-Supervised Framework for MR Reconstruction

Harris Beg^1,2, Beliz Gunel^2,3, Batu M Ozturkler^2,3, Christopher M Sandino³, John M Pauly³, Shreyas Vasanawala⁴, Akshay S Chaudhari^4,5, 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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