Self-supervised deep learning for MR reconstruction has shown high potential in accelerating MR imaging as it doesn’t need fully sampled dataset for model training. However, the performances of the current self-supervised methods are limited as they don’t take full utilization of the available under-sampled data. We propose a physics-based data-augmented deep learning method to enable faster and more accurate parallel MR imaging. Novel augmenting losses are calculated, which can effectively constrain the model optimization with better utilization of the collected dataset. Extensive experiments are conducted, and better reconstruction results are generated by our method compared to the current state-of-the-art methods.
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