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Abstract #0771

Exploiting the Spectral Complexity of Fat for Robust Multi-Point Water-Fat Separation

Huanzhou Yu1, Ann Shimakawa1, Jean H. Brittain2, Charles A. McKenzie3, Scott B. Reeder4

1Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States; 2Applied Science Laboratory, GE Healthcare, Madison, WI, United States; 3Department of Medical Biophysics, University of Western Ontario, London, ON, Canada; 4Departments of Radiology, Medical Physics, Biomedical Engineering and Medicine, University of Wisconsin, Madison, Madison, WI, United States

Multi-point water-fat separation methods must address the challenge of water-fat ambiguity that arises from the signal behavior of water and fat which, when both modeled with a single spectral peak, may appear identical in the presence of Bo off-resonance. Water-fat ambiguity is typically removed by enforcing field- or phase-map smoothness using region growing based algorithms. However, the fat spectrum actually has multiple spectral peaks. In this work, a novel algorithm to identify water and fat for multi-point acquisitions is introduced by exploiting the spectral differences between water and fat. New opportunities arise to design algorithms for highly robust water-fat separation.