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

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.