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Abstract #5614

Dual Echo Water-Fat Separation Using Deep Learning

Tao Zhang1, Yuxin Chen2, Shreyas Vasanawala3, and Ersin Bayram1

1Global MR Applications and Workflow, GE Healthcare, Houston, TX, United States, 2Electrical Engineering, Princeton University, Princeton, NJ, United States, 3Radiology, Stanford University, Stanford, CA, United States

Water-fat separation is widely used in many MR applications and is known to be challenging in various situations. Traditionally, region growing, spatial smoothing, and global optimization have been applied in dual echo water-fat separation. These methods require complex-valued images acquired at two echo times and occasionally suffer from global or local swaps due to inaccurate field map estimation. In this work, a deep learning approach for dual echo water-fat separation is investigated.

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