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dc.contributor.authorPark, Hae-Jeong
dc.contributor.authorKubicki, Marek R.
dc.contributor.authorShenton, Martha Elizabeth
dc.contributor.authorGuimond, Alexandre
dc.contributor.authorMcCarley, Robert William
dc.contributor.authorMaier, Stephan Ernst
dc.contributor.authorKikinis, Ron
dc.contributor.authorJolesz, Ferenc A.
dc.contributor.authorWestin, Carl-Fredrik
dc.date.accessioned2016-09-15T20:54:09Z
dc.date.issued2003
dc.identifier.citationPark, Hae-Jeong, Marek Kubicki, Martha E. Shenton, Alexandre Guimond, Robert W. McCarley, Stephan E. Maier, Ron Kikinis, Ferenc A. Jolesz, and Carl-Fredrik Westin. 2003. Spatial Normalization of Diffusion Tensor MRI Using Multiple Channels. NeuroImage 20, no. 4: 1995–2009. doi:10.1016/j.neuroimage.2003.08.008.en_US
dc.identifier.issn1053-8119en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:28520547
dc.description.abstractDiffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.en_US
dc.language.isoen_USen_US
dc.publisherElsevier BVen_US
dc.relation.isversionofdoi:10.1016/j.neuroimage.2003.08.008en_US
dc.relation.hasversionhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2811885/en_US
dash.licenseLAA
dc.subjectdiffusion tensoren_US
dc.subjectspatial normalizationen_US
dc.subjecttractographyen_US
dc.titleSpatial normalization of diffusion tensor MRI using multiple channelsen_US
dc.typeJournal Articleen_US
dc.description.versionAccepted Manuscripten_US
dc.relation.journalNeuroImageen_US
dash.depositing.authorShenton, Martha Elizabeth
dc.date.available2016-09-15T20:54:09Z
dc.identifier.doi10.1016/j.neuroimage.2003.08.008*
dash.identifier.orcid0000-0003-4235-7879en_US
dash.contributor.affiliatedWestin, Carl-Fredrik
dash.contributor.affiliatedMaier, Stephan
dash.contributor.affiliatedJolesz, Ferenc
dash.contributor.affiliatedKubicki, Marek
dash.contributor.affiliatedShenton, Martha
dash.contributor.affiliatedMcCarley, Robert William
dash.contributor.affiliatedKikinis, Ron
dc.identifier.orcid0000-0003-4235-7879
dc.identifier.orcid0000-0001-5705-7495
dc.identifier.orcid0000-0001-7227-7058


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