Water-fat separation incorporating spatial smoothing is robust to noise

作者：Andersson Jonathan^*; Ahlstrom Hakan; Kullberg Joel

来源：Magnetic Resonance Imaging, 2018, 50: 78-83.

DOI：10.1016/j.mri.2018.03.015

• Dixon
• Water-fat separation
• Graph cuts
• Multi-scale
• Chemical shift imaging
• Quadratic pseudo-Boolean optimization

Purpose: To develop and evaluate a noise-robust method for reconstruction of water and fat images for spoiled gradient multi-echo sequences.
Methods: The proposed method performs water-fat separation by using a graph cut to minimize an energy function consisting of unary and binary terms. Spatial smoothing is incorporated to increase robustness to noise. The graph cut can fail to find a solution covering the entire image, in which case the relative weighting of the unary term is iteratively increased until a complete solution is found.
The proposed method was compared to two previously published methods. Reconstructions were performed on 16 cases taken from the 2012 ISMRM water-fat reconstruction challenge dataset, for which reference reconstructions were provided. Robustness towards noise was evaluated by reconstructing images with different levels of noise added. The percentage of water-fat swaps were calculated to measure performance.
Results: At low noise levels the proposed method produced similar results to one of the previously published methods, while outperforming the other. The proposed method significantly outperformed both of the previously published methods at moderate and high noise levels.
Conclusion: By incorporating spatial smoothing, an increased robustness towards noise is achieved when performing water-fat reconstruction of spoiled gradient multi-echo sequences.

• 出版日期2018-7