Dual echo Dixon imaging with a constrained phase signal model and graph cuts reconstruction

Purpose: The purpose of this work is to derive and demonstrate constrained-phase dual-echo Dixon imaging within a maximum likelihood framework solved with a regularized graph-cuts-guided optimization. Theory and Methods: Dual-echo Dixon reconstruction is fundamentally underdetermined; however, adopting a constrained-phase signal model reduces the number of unknowns and the nonlinear problem can be solved under a maximum likelihood framework. Period shifts in the field map (manifesting as fat/water signal swaps) must also be corrected. Here, a regularized cost function promotes a smooth field map and is solved with a graph-cuts-guided greedy binary optimization. The reconstruction shown here is compared to two other prevalent Dixon reconstructions in experimental phantom and human studies. Results: Reconstructed images of the water and fat signal are shown for a phantom study, and in vivo studies of foot/ankle, pelvis, and CE-MRA of the thighs. The method shown here compared favorably with the other two methods. Large field inhomogeneities on the order of 20 ppm were resolved, thereby avoiding the fat and water signal swaps present in images reconstructed with the other methods. Conclusion: Constrained-phase dual-echo Dixon imaging solved with a regularized graph-cuts-guided optimization has been derived and demonstrated to successfully separate water and fat images in the presence of large magnetic field inhomogeneities. Magn Reson Med 78:2203–2215, 2017.