Dixon imaging generally requires multiple input images acquired at varying echo-times for robust phase-correction and water and fat separation. However, multi-echo Dixon imaging suffers from relatively long scan-time and is more susceptible to motion related artefacts and inflexible in choosing scan-parameters. Recently, it was reported that deep neural networks can help separate water and fat from two-point, or multi-point Dixon images. In this work, we present a deep learning based method that can achieve water and fat separation from a single image acquired at a flexible echo-time and therefore can help alleviate the limitations of multi-point Dixon imaging.
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