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Abstract #5180

A novel method for Rapid 3D fat and water decomposition using a GlObally Optimal multi-surface Estimation (R-GOOSE)

Chen Cui1, Abhay Shah1, Xiaodong Wu1, Dan Thedens 2, and Mathews Jacob1

1Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States, 2Radiology, University of Iowa, Iowa City, IA, United States

A 3D Rapid, GlObally Optimal Surface Estimation (R-GOOSE) algorithm for fat-water decomposition in MRI is proposed. The fat-water separation is formulated as an optimization problem with data consistency and field-map smoothness penalty. The data consistency only contains exact minimizers from the fully discretized field-map value volume. The proposed method employs a connectivity-reduced graph construction that enables the new formulation to be solved efficiently. The method is validated by the 17 datasets from the 2012 ISMRM Challenge with thirty-fold computational gain compared to our previous method GOOSE while the high quantitative accuracy is maintained. Fat fraction maps obtained from the proposed method also provides a good marker for degenerative muscle diseases in newly collected lower limb datasets.

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