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Abstract #1781

Enhancing the Reconstruction quality of Physics-Guided Deep Learning via Holdout Multi-Masking

Burhaneddin Yaman1,2, Seyed Amir Hossein Hosseini1,2, Steen Moeller2, and Mehmet Ak├žakaya1,2
1University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, Minneapolis, MN, United States

Physics-guided deep learning (PG-DL) approaches unroll conventional iterative algorithms consisting of data consistency (DC) and regularizers, and typically perform training on a fully-sampled database. Although supervised training has been incredibly successful, there is still room for further removing residual and banding artifacts. To improve reconstruction quality and robustness of supervised PG-DL, we propose to use multiple subsets of acquired measurements in the DC units during training by applying a multi-masking operation on available sub-sampled data, unlike existing supervised PG-DL approaches that use all the available measurements in DC units. Proposed method outperforms conventional supervised PG-DL method by further reducing theartifacts.

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