Name
Abstract | ||
|---|---|---|
Magnetic resonance diffusion tensor imaging (DTI) provides a noninvasive approach to characterize the fiber pathways in the human brain. Among the fiber tractography algorithms in DTI analysis, the fast marching (FM) method has been widely used in quantitatively analyzing the structural connectivity of the fibers and their changes. However, standard FM only considers the similarity and the principal direction information conveyed by two neighboring voxels. It may have poor tracking performance when image noise and fiber crossing are present. To solve this problem, we introduced an improved FM method employing a memory factor (MFFM) to better characterize the directionality of fiber propagation. Simulation showed that MFFM yields higher tracking accuracy, lower computational load, and better antinoise/crossing performance compared with standard FM. Finally, we applied MFFM to Alzheimer's disease (AD) DTI data to explore the impaired regional connectivity of fiber structure. The results augment the knowledge of the pathological alteration of white matter in AD. (c) 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 346-352, 2013 |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1002/ima.22070 | INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY |
Keywords | Field | DocType |
fast marching method, memory factor, fiber tractography, Alzheimer's disease, structural connectivity | Voxel,Diffusion MRI,White matter,Computer science,Artificial intelligence,Computer vision,Pattern recognition,Fiber,Fast marching method,Speech recognition,Image noise,Tractography,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
23 | 4 | 0899-9457 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Jie Zhao | 1 | 37 | 14.54 |
| Xiao-Tong Wen | 2 | 22 | 5.20 |
| Jiahui Shen | 3 | 0 | 0.68 |
| Hao Hong | 4 | 1 | 2.05 |
| Li Yao | 5 | 53 | 20.09 |