Fiber Re-Tracking Based on Flow Field Distribution
Xie Fei1, Feng Yuanjing1,2*, He Jianzhong1, Li Mao1
1(Institute of Information Processing and Automation, School of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China) 2(Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China)
Abstract：White matter fiber tracking can reconstruct the direction of brain fibers. However, due to the limitation of resolution, the imaging voxels show zigzag boundaries, leading to the early termination of the reconstructed fiber bundles at the white matter region boundary. In addition, the lack of voxel direction information caused by signal noise also leads to premature termination of fiber tracking. In view of these problems, a fiber stop tracking algorithm was proposed to obtain more accurate result of the fiber tracking. Firstly, the flow field distribution model of the fiber local structure was established, and the fiber direction information of any point in the voxel was expressed by the flow field distribution. Next, based on the voxel flow field distribution model, the continuity of the fiber tracking stop point was calculated for the fiber tracking, and more accurate fiber results were obtained. Two experimental data sets (including ISMRM 2015 data set of 25 major fiber bundles and a set of clinical data sets from Stanford University database) were used to verify the proposed method. Firstly, the proposed algorithm was quantitatively analyzed on the simulation data set by using the tractometer quantitative index, the results showed that the effective number of valid bundles (VB), valid connections (VC) and no connections (NC) of the algorithm were 24, 51.3% and 12.9%, respectively. Compared with SD Stream, VB increased by 2 bundles, VC increased by 19.1%, and NC decreased by 22.8%. The effectiveness of this method was verified through the qualitative analysis of clinical data. The experimental results showed that the algorithm could effectively avoid the premature termination of fiber tracking, improving the effective fiber ratio of the fiber tracking, reducing the proportion of unconnected fibers, and improving the accuracy of fiber tracking results.
谢飞, 冯远静, 何建忠, 李茂. 基于流场分布的纤维续跟踪[J]. 中国生物医学工程学报, 2023, 42(2): 180-188.
Xie Fei, Feng Yuanjing, He Jianzhong, Li Mao. Fiber Re-Tracking Based on Flow Field Distribution. Chinese Journal of Biomedical Engineering, 2023, 42(2): 180-188.
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