Using multiple tensor deflection to reconstruct white matter fiber traces with branching

• Published in: 2006 3rd IEEE International Symposium on Biomedical Imaging--Macro to Nano : Arlington, WA, 6-9 April 2006 Vol. 3; pp. 69 - 72
• Main Authors: Guo, Weihong, Zeng, Qingguo, Chen, Yunmei, Liu, Yijun
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 2006
• Subjects: Brain modeling; Data mining; Image reconstruction; Joining processes; Large-scale systems; Magnetic resonance imaging; Noninvasive treatment; Smoothing methods; Tensile stress; Visualization
• DOI: 10.1109/ISBI.2006.1624854
• ISBN: 078039576X; 9780780395763
• ISSN: 1945-7928

The relationship between brain structure and complex behavior is governed by large-scale neurocognitive networks. Diffusion weighted imaging (DWI) is a noninvasive technique that can visualize the neuronal projections connecting the functional centers and thus provides new keys to the understanding of brain function. In this paper, we assume there are up to two diffusion channels at each voxel. A variational framework for 3D simultaneous smoothing and reconstruction of a multi-diffusion tensor field as well as a novel multi-tensor deflection (MTEND) algorithm for extracting white matter fiber traces based on the multi-diffusion tensor field are provided. By applying the proposed model to both synthetic data and human brain high angular resolution diffusion (HARD) magnetic resonance imaging (MRI) data of several subjects, we show the effectiveness of the model in recovering branching fiber traces. Superiority of the proposed model over existing models are also demonstrated