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Abstract #4810

Zero-Shot Physics-Guided Self-Supervised Learning for  Subject-Specific MRI Reconstruction

Burhaneddin Yaman1,2, Seyed Amir Hossein Hosseini1,2, and Mehmet Akcakaya1,2
1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, Minneapolis, MN, United States


While self-supervised learning enables training of deep learning reconstruction without fully-sampled data, it still requires a database. Moreover, performance of pretrained models may degrade when applied to out-of-distribution data. We propose a zero-shot subject-specific self-supervised learning via data undersampling (ZS-SSDU) method, where acquired data from a single scan is split into at least three disjoint sets, which are respectively used only in physics-guided neural network, to define training loss, and to establish an early stopping strategy to avoid overfitting. Results on knee and brain MRI show that ZS-SSDU achieves improved artifact-free reconstruction, while tackling generalization issues of trained database models.

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