Huazhong University of Science and Technology, Wuhan, China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Magn Reson Med. 2019 Jul;82(1):436-448. doi: 10.1002/mrm.27710. Epub 2019 Mar 12.
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 B 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 179.3 s for 15 datasets. Sagittal spine data with ~400 mm 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 B 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 B field, and low SNR application.
开发一种基于水脂分离区域提取（TREE）的方法，用于在具有挑战性的场景中进行稳健的水脂分离和定量分析，包括低信噪比（SNR）、快速变化的 B 场和不连续的解剖结构。
在 TREE 方法中，每个像素的相量解被分为以脂肪为主和以水为主的两组。然后通过检测相图中的突然变化来提取水脂转换区域。通过选择最平滑的相量组合来解决转换区域中像素的相量解。对于剩余的子区域，然后通过选择所有周围转换区域像素来确定相量解。该方法使用各种数据集进行了验证，包括一些来自国际磁共振医学学会（ISMRM）2012 挑战赛的数据。
所提出方法的定量评分（10000 分中的 9936.8 分）与 ISMRM 2012 挑战赛的获胜者（9951.9 分）相当。对于 15 个数据集，总处理时间为 179.3 秒。使用矢状脊柱数据，头脚方向的视野约为 400mm，将 TREE 与几种有代表性的区域生长方法进行了比较。结果表明，该方法在快速变化的 B 场、不连续的解剖结构和低 SNR 区域具有稳健性。在低 SNR（SNR~10）数据集上未观察到明显的水脂交换。所提出的方法还产生了准确的质子密度脂肪分数结果。
提出了一种基于水脂分离区域提取的方法，用于稳健的水脂分离和脂肪分数定量。该方法在空间不连续的物体、快速变化的 B 场和低 SNR 应用中表现良好。
