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dc.contributor.advisorPolina Golland and Carl-Fredrik Westin.en_US
dc.contributor.authorNgo, Tri M. (Tri Minh)en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2008-05-19T16:05:35Z
dc.date.available2008-05-19T16:05:35Z
dc.date.copyright2007en_US
dc.date.issued2007en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/41652
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.en_US
dc.descriptionIncludes bibliographical references (p. 75-77).en_US
dc.description.abstractNeuroscientists 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.en_US
dc.description.statementofresponsibilityby Tri M. Ngo.en_US
dc.format.extent77 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleA stochastic tractography system and applicationsen_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc219720649en_US


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