Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging.

ABSTRACT: Self-supervised learning has shown great promise because of its ability to train deep learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully sampled data. Current self-supervised learning methods for physics-guided reconstruction networks split acquired undersampled data into two disjoint sets, where one is used for data consistency (DC) in the unrolled network, while the other is used to define the training loss. In this study, we propose an improved self-supervised learning strategy that more efficiently uses the acquired data to train a physics-guided reconstruction network without a database of fully sampled data. The proposed multi-mask self-supervised learning via data undersampling (SSDU) applies a holdout masking operation on the acquired measurements t

SUBMITTER: Yaman B 

PROVIDER: S-EPMC9669191 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature