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Abstract #3474

Dual-domain Self-supervised Learning for Accelerated MRI Reconstruction

Bo Zhou1,2, Jo Schlemper2, Seyed Sadegh Mohseni Salehi 2, Neel Dey2,3, Kevin Sheth1, Chi Liu1, James Duncan1, and Michal Sofka2
1Yale University, New Haven, CT, United States, 2Hyperfine Research, Guilford, CT, United States, 3New York University, New York, NY, United States


We present a self-supervised approach for accelerated non-uniform MRI reconstruction, which leverages self-supervision in k-space and image domains. We evaluated the performance on both simulation and real data, where fully sampled data is unavailable. The experimental results on a non-uniform MRI dataset demonstrate that the proposed method can generate reconstruction that approaches the accuracy of the fully supervised reconstruction. Furthermore, we show that the approach can be applied to highly challenging real-world clinical MRI reconstruction acquired on a low-field (64 mT) MRI scanner with no data available for supervised training while demonstrating improved perceptual quality as compared to traditional reconstruction.

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