The Dixon method is a popular and widely used technique for fat-water separation in magnetic resonance imaging, and today, nearly all scanner manufacturers are offering a Dixon-type pulse sequence that produces scans with four types of images: in-phase, out-of-phase, fat-only, and water-only. A natural ambiguity due to phase wrapping and local minima in the optimization problem cause a frequent artifact of fat-water inversion where fat- and water-only voxel values are swapped. This artifact affects up to 10 % of routinely acquired Dixon images, and thus, has severe impact on subsequent analysis. We propose a simple yet very effective method, Dixon-Fix, for correcting fat-water swaps. Our method is based on regressing fat- and water-only images from in- and out-of-phase images by learning the conditional distribution of image appearance. The predicted images define the unary potentials in a globally optimal maximum-a-posteriori estimation of the swap labeling with spatial consistency. We demonstrate the effectiveness of our approach on whole-body MRI with various types of fat-water swaps.
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