Fat-water separation based on Transition REgion Extraction (TREE).

Abstract

To develop a method based on fat-water transition region extraction (TREE) for robust fat-water separation and quantification in challenging scenarios, including low signal-to-noise ratio (SNR), fast changing B0 field, and disjointed anatomies. In TREE method, the phasor solutions of each pixel were categorized into fat-dominant and water-dominant groups. The fat-water transition region was then extracted by detecting sudden changes in the phasor maps. The phasor solutions of the pixels in the transition region were solved by choosing the smoothest phasor combinations. For the remaining subregions, the phasor solution was then determined by all the surrounding transition region pixels. The proposed method was validated using various datasets, including some from the International Society for Magnetic Resonance in Medicine (ISMRM) 2012 Challenge. Quantitative score of proposed method (9936.8 of 10,000) is comparable to the winner (9951.9) of ISMRM 2012 Challenge. The total processing time was 179.3s for 15 datasets. Sagittal spine data with ~400mm field of view in head-foot direction were used to compare TREE with several representative region-growing methods. Results showed that the proposed method was robust under fast changing B0 field, disjointed anatomies and low SNR area. No apparent fat-water swap was observed in the low SNR (SNR ~ 10) dataset. Accurate proton density fat fraction results were also produced from the proposed method. A method based on fat-water transition region extraction was proposed for robust water-fat separation and fat fraction quantification. The method worked well in spatially disjointed objects, fast changing B0 field, and low SNR application.