2026-09-19T19:07:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/416522022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Polina Golland and Carl-Fredrik Westin.
Ngo, Tri M. (Tri Minh)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-05-19T16:05:35Z
2008-05-19T16:05:35Z
2007
2007
http://hdl.handle.net/1721.1/41652
219720649
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 75-77).
Neuroscientists hypothesize that the pathologies of some neurological diseases are associated with neuroanatomical abnormalities. Diffusion Tensor Imaging (DTI) and stochastic tractography allow us to investigate white matter architecture non-invasively through measurements of water self diffusion throughout the brain. Many comparative studies of white matter architecture utilize spatially localized comparisons of diffusion characteristics. White matter tractography enables studies of fiber bundle characteristics. Stochastic tractography facilitates these investigations by providing a measure of confidence regarding the inferred fiber bundles. This thesis presents an implementation of an easy to use, open-source stochastic tractography system that will enable novel studies of fiber tract abnormalities. We demonstrate an application of the system on real DTI images and discuss possible studies of frontal lobe fiber differences in Schizophrenia.
by Tri M. Ngo.
M.Eng.
77 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A stochastic tractography system and applications
Thesis
application/pdf
Publication
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