We proposed a novel deep learning network for water-fat separation from undersampled mGRE data. The network contains three components: The first is the reconstruction module, which can effectively take advantage of the similarity between different echoes to recover the fully sampled image from the undersampled data; the second is the feature extraction module, which learns the correlations between consecutive echoes; and the third is the water-fat separation module that processes the feature information extracted from the feature extraction module. The results show that the proposed network can effectively obtain high-quality water and fat images at 6 times acceleration.
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