Robust phase unwrapping for Dixon magnetic resonance imaging multiresolution pixel clustering and local surface fitting.

Background: Water-fat separation plays a crucial role in clinical magnetic resonance imaging (MRI) examinations. However, the Dixon technique is encumbered by the challenge of water-fat swapping due to factors such as magnetic field inhomogeneity and low image signal-to-noise ratio (SNR). This study aimed to develop a multiresolution pixel clustering and local surface fitting (MrCLOSE) phase-unwrapping method to improve the robustness of water-fat separation in two-point Dixon MRI. Methods: MrCLOSE first constructs a multiresolution pyramid via acquired in-phase and out-of-phase complex MRI. It then performs hierarchical phase unwrapping with pixel clustering and local surface fitting (CLOSE) within a coarse-to-fine multiresolution framework. The performance of MrCLOSE was systematically evaluated and compared with those of B0 mapping with magnitude-based correction for bipolar two-point Dixon (B0-NICEbd), rapid opensource minimum spanning tree algorithm (ROMEO), Graph Cut, and the original CLOSE algorithm with both simulation and Results: In the simulation data, MrCLOSE achieved robust phase unwrapping under different thresholds, varying SNRs, disconnected regions, and phase changes from 4π to 10π. In the threshold-dependence simulation, CLOSE showed high error ratios of 43.71% and 94.74% at thresholds of π/10 and π/5, whereas MrCLOSE reduced these values to 0.67% and 0.11%, respectively. In additional simulations involving varying SNRs, disconnected regions, and increasing phase changes, MrCLOSE consistently maintained low error ratios and outperformed ROMEO and Graph Cut. In the 355 Conclusions: By innovatively integrating multiresolution strategies into CLOSE, MrCLOSE achieves high robustness and accuracy in phase unwrapping and provides a promising solution for phase-related MRI applications, particularly for water-fat separation in clinical scenarios.