Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that provides information about the organization and structural integrity of tissue. It has become an increasingly popular tool for investigation of white matter tissue in the brain emph{in vivo}, with clinical applications ranging from myelin-related diseases, such as multiple sclerosis and Krabbe disease, to psychiatric disorders, such as schizophrenia and bipolar disorder. In studies comparing DTI data between groups, whole-brain, voxel-wise analyses are commonly performed. However, there is not enough information in the scalar images typically used for image co-registration to accurately align specific fiber pathways within large white matter structures. This misalignment potentially results in decreased sensitivity to detecting subtle changes within specific tracts and makes interpretation of results more difficult in studies of disorders where it is suspected that changes in white matter diffusion properties are pathway-specific. In an effort to overcome this limitation, several tract-based analysis methods that utilize fiber tractography to isolate specific tracts of interest have recently been proposed. However, the majority of these methods have been developed for particular white matter pathways and are not easily translated to other tracts, or compare the average of diffusion parameters over the entire tract, overlooking localized changes within the tracts. Proposed here is a new tract-based method that utilizes the image co-registration necessary for a voxel-wise analysis to automatically isolate white matter tracts-of-interest in each subject with fiber tractography and parameterize them so that spatially localized statistical comparisons between groups can be made. It was applied to studies of schizophrenia and Williams syndrome, and the results were compared to voxel-based analyses of these studies. Our results suggest that the new tract-based analysis method performs better than the voxel-based method in regions where image co-registration performs poorly and in regions where image co-registration appears to perform well but actually fails to align smaller tracts within larger white matter structures. This new tract-based analysis method, used in combination with a voxel-wise analysis, may be able to improve the interpretation of results from group comparisons of diffusion tensor imaging data.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-01152009-125138 |
| Date | 15 January 2009 |
| Creators | Arlinghaus, Lori R. |
| Contributors | John C. Gore, Benoit M. Dawant, Malcolm J. Avison, Sohee Park, Adam W. Anderson |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-01152009-125138/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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