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Abstract #0459

Young Investigator Award Finalist: Robust Water/Fat Separation in the Presence of Large Field Inhomogeneities Using a Graph Cut Algorithm

Diego Hernando1, Peter Kellman2, Justin Haldar1, Zhi-Pei Liang1

1Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA; 2NHLBI, National Institutes of Health, Bethesda, MD, USA

Water/fat separation is a classical problem for in vivo MRI. Although many methods have been proposed, robust water/fat separation is still challenging, especially in the presence of large field inhomogeneities. This work tackles the problem using a statistically-motivated formulation which jointly estimates the complete field map and water/fat images. This formulation results in a difficult (high-dimensional and non-convex) minimization problem, which is solved using a novel graph cut algorithm. The proposed method has good theoretical properties and an efficient implementation. It has proven effective for characterizing intramyocardial fat, producing robust water/fat separation in cases containing large field inhomogeneities due to susceptibility effects and magnet imperfections.