We design a data-driven method to generate water/fat images from dual-echo complex Dixon images, aimed at near-instant water-fat separation with high robustness. A hierarchical convolutional neural network is employed, where ground truth images are obtained using a binary quadratic optimization approach. With IRB approval and informed consent, 9281 image sets are collected from 30 pediatric patients to train and test networks, with the application of six-fold cross validation. In addition to high fidelity and significantly reduced processing time, the predicted images are superior to the ground truth in mitigation of water/fat swaps and correction of artifacts introduced by metallic implants.
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