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An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data
We conclude that the use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.
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