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Abstract #0989

Physics-Based Self-Supervised Deep Learning for Accelerated MRI Without Fully Sampled Reference Data

Burhaneddin Yaman1,2, Seyed Amir Hossein Hosseini1,2, Steen Moeller2, Jutta Ellermann2, Kamil Ugurbil2, and Mehmet Akcakaya1,2
1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

Recently, deep learning (DL) has emerged as a means for improving accelerated MRI reconstruction. However, most current DL-MRI approaches depend on the availability of ground truth data, which is generally infeasible or impractical to acquire due to various constraints such as organ motion. In this work, we tackle this issue by proposing a physics-based self-supervised DL approach, where we split acquired measurements into two sets. The first one is used for data consistency while training the network, while the second is used to define the loss. The proposed technique enables training of high-quality DL-MRI reconstruction without fully-sampled data.

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