The Development of High Angular Resolution Diffusion Tractography and its Applications
| Autor: | Yi-Ping Chao, 趙一平 |
|---|---|
| Rok vydání: | 2009 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 97 Since last few years, magnetic resonance imaging (MRI) has become an important approach for non-invasive map of functional organizations for neuroscience research and clinical applications. With the ability of mapping tissue anisotropy, diffusion MRI provides a novel aspect of depicting the structural connectivity between functional brain regions. Compared with conventional diffusion MRI techniques and diffusion tensor imaging (DTI), high angular resolution diffusion imaging (HARDI) provides more information for complex neural fiber architectures. Despite fruitful results have been acquired from the HARDI derived complex neural connectivity, some intrinsic problems still exist in the current multiple fiber tracking (MFT) algorithms. Therefore, the objective of this dissertation is twofold. First, we has developed a modified MFT method, namely multiple fiber assignment by continuous tracking (MFACT), to trace complex neural trajectories within brain. The MFACT method was applied to map the reticular neural connections of human brain from HARDI derived fiber orientations. With appropriate tracking criteria, this method not only has the potential to map the sophisticated trajectories which are passing through the regions with fiber heterogeneous, but also can be adopted to extract the known fiber tract dispersions in a shorter time. Second, an extension of MFACT approach was proposed to estimate the probabilistic fiber trajectories. It offers a high sensitivity in mapping multi-fiber connections and has the advance to assess neural connections between subtle functional regions. Using these new developed methods, we have developed a probabilistic topography of the corpus callosum (CC) by connecting Brodmann’s cytoarchitectonic template and the CC. A topological relationship between 28 cerebral regions and the mid-sagittal CC was accordingly revealed. In addition, the probabilistic topography allows an assignment of quantitative distribution in the subdivision of the CC. Fractional anisotropy derived from DTI data of 20 healthy subjects was evaluated to study the correlation with neural composition in distinct CC regions which has explored by Aboitiz in 20 postmortem human brains In conclusion, we have successfully developed a novel HARDI-based tractography algorithm and applied it to map probabilistic neural connectivity. From various cytoarchitectonic regions, sophisticated probabilistic population topography of the CC from 12 healthy subjects was accordingly identified. We also demonstrated that this topography may serve as a brain landmark to reveal the neural integrity among regional microstructure of the CC. These methods and applications may be potentially useful to facilitate the further neuroscience researches and clinical applications. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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