Water-fat separation is a powerful tool in diagnosing many diseases and many efforts have been made to reduce the scan time. Spatiotemporally encoded (SPEN) single-shot MRI, as an emerging ultrafast MRI method, can accomplish the fastest water-fat separation since only one shot is required. However, the SPEN water/fat images obtained by the state-of-the art methods still have some shortcomings. Here, a deep learning approach based on U-Net was proposed to obtain SPEN water/fat images simultaneously with improved spatial resolution, better fidelity and reduced reconstruction time. The efficiency of our method is demonstrated by numerical simulations, and in vivo rat experiments.
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