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Abstract #4381

Field Map Estimation from Magnitude-Based Water-Fat Separation

Alexandre Triay Bagur1, Chloe Hutton1, Benjamin Irving1, Michael L. Gyngell1, Matthew D. Robson1, and Michael Brady1

1Perspectum Diagnostics Ltd, Oxford, United Kingdom

Complex-based MRI chemical-shift encoded water-fat separation depends on accurate field map convergence, which is often mitigated with spatial regularization. This is prone to error propagation and over-smoothing of fat-fraction maps. Magnitude-based separation circumvents field mapping but is reportedly limited in fat-fraction range (0-50%). We have recently presented MAGO, a magnitude-based method that resolves this water-fat ambiguity. In this study, we compare MAGO to state-of-the-art fat-fraction quantification on N=150 volunteers, and we expand the method for field map calculation using previously estimated water and fat images. MAGO is comparable to regularized hybrid-based decomposition and shows promise in higher field inhomogeneity regimes.

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