Deep learning has been successfully applied to Dixon reconstruction, but requires good training data, which limits clinical applicability. We propose a deep learning-based Dixon method with chemical-shift correction that is trained with only simulated data. Results on three anatomies show that the method produces equivalent or better results than conventional methods for Dixon water-fat separation with chemical-shift correction. This approach is fundamentally different from conventional linear and non-linear solvers and shows promise for extension to more complex problems.
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