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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