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Histological validation of the diffusion tensor: feasibility in human brain tissue
Author(s) -
Ortiz Jake,
Hageman Nathan,
Salin Ashley,
Salin Megan,
Dong Hongwei,
Stark M. Elena,
Vinters Harry V.,
Toga Arthur W.,
Wisco Jonathan J.
Publication year - 2010
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.24.1_supplement.642.2
Subject(s) - diffusion mri , fractional anisotropy , white matter , corpus callosum , tensor (intrinsic definition) , artificial intelligence , voxel , tractography , computer science , anisotropy , nerve fiber , neuroscience , physics , pattern recognition (psychology) , mathematics , biology , magnetic resonance imaging , medicine , radiology , optics , geometry
Taking advantage of anisotropic water movement caused by cytoarchitectural properties of brain tissue, Diffusion Tensor Imaging (DTI) is capable of representing approximate true nerve fiber orientations as a diffusion tensor at each voxel. The interpretation of DT images remains hampered in part by the error inherent in tensor calculations. This pilot study demonstrates the feasibility of histological validation of DTI in post‐mortem human brain tissue, with the eventual goal of constructing a probabilistic, ground‐truth atlas of fiber orientations specifically designed to reduce the uncertainty of tensor calculations. As a first step, we mapped nerve fibers of the corpus callosum, cingulum bundle and the intersecting region from images of histological sections nonlinearly registered to tensor maps. We confirmed that tensors accurately represented homogeneous but not necessarily heterogeneous fiber populations. Histological validation can provide a priori constraints for tensor calculations and will have a direct impact on the reconstruction of fiber tracts and help clinicians to identify and manage diseases involving white matter pathologies with greater sensitivity and specificity. This work was supported in part by NIH grants U54 RR021813, P41 RR013642, P50 AG16570, R21 MH083180, R90 DA023419‐01, T90 DA023419‐01 and GM08042. Grant Funding Source : U54 RR021813 , P41 RR013642 , P50 AG16570, R21 MH083180 , R90 DA023419‐01 , T90 DA023419‐01 and GM08042