Abstract

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Title:: An improved white matter fiber assignment by continuous tracking algorithm

Author(s):: YAN Shumin1; XU Longchun2; ZHANG Minfeng2; ZHANG Gang3; ZOU Yue3; HE Lemin1; CHENG Yunfu1; YAN Cuiping1; YANG Xinyi1; XU Ranran1; WANG Xiaoyan1; WANG Pengcheng1; ZHAO Wenbo3; ZHANG Guangyu1; 1. Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271016, China 2. Department of Medical Imaging, the Second Affiliated Hospital of Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271000, China 3. Department of Otorhinolaryngology, the Second Affiliated Hospital of Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271000, China

Keywords:: Keywords: fiber assignment by continuous tracking algorithrn brain science diffusion entropy brain structural network diffusion tensor imaging

Abstract:: Abstract: Objective To improve the white matter fiber assignment by continuous tracking (FACT) algorithm for increasing the continuity and accuracy of fiber tracking. Methods The diffusion tensor magnetic resonance imaging data from Department of Medical Imaging of the Second Affiliated Hospital of Shandong First Medical University and the Human Connectome Project (http://www.neuroscienceblueprint.nih.gov/connectome/) were used to verify the validation of the improved algorithm. Firstly, the diffusion tensor magnetic resonance images were processed by median filters and Gaussian smoothing filters to eliminate the effects of noises on FACT algorithm, and a brain template was used to remove the effects of skull on follow-up tracking. Then the diffusion tensor and anisotropy index of each voxel were obtained by the least-mean-square error algorithm. Finally, starting with the initial voxel with an anisotropy index greater than the threshold, both tracking-editing technique and linear tracking based on diffusion entropy are used to accomplish fiber tracking. Results Compared with traditional FACT method, the improved algorithm realized a more continuous and accurate fiber tacking. In addition, the improved algorithm has a good anti-noise ability and a strong robustness. Conclusion The improved method can be used to construct the brain structural network and investigate brain diseases.