Magnitude and Complex Single- and Multi-echo Water Fat Separation via End-to-End Deep Learning

The feasibility of water-fat separation using an end-to-end ConvNet approach was demonstrated for complex, magnitude and single echo acquisitions. The ConvNet approach showed images visually comparable to the GraphCut method with slightly higher signal to noise in typical cardiac image planes. Quantitative PDFF, R2* and off-resonance values had excellent correlation with a conventional analytical model based method. ConvNet based water-fat separation is a promising method capable of learning the water-fat separation problem with corrections for bipolar gradients, a multi-peak model, R2* and off-resonance.