title: A statistical framework for the classification of tensor morphologies in diffusion tensor images.
abstract: Tractography algorithms for diffusion tensor (DT) images consecutively connect directions of maximal diffusion across neighboring DTs in order to reconstruct the 3-dimensional trajectories of white matter tracts in vivo in the human brain. The performance of these algorithms, however, is strongly influenced by the amount of noise in the images and by the presence of degenerate tensors-- i.e., tensors in which the direction of maximal diffusion is poorly defined. We propose a simple procedure for the classification of tensor morphologies that uses test statistics based on invariant measures of DTs, such as fractional anisotropy, while accounting for the effects of noise on tensor estimates. Examining DT images from seven human subjects, we demonstrate that this procedure validly classifies DTs at each voxel into standard types (nondegenerate DTs, as well as degenerate oblate, prolate or isotropic DTs), and we provide preliminary estimates for the prevalence and spatial distribution of degenerate tensors in these brains. We also show that the P values for test statistics are more sensitive tools for classifying tensor morphologies than are invariant measures of anisotropy alone.
authors.affiliation: MRI Unit, Department of Psychiatry, Columbia University Medical Center, USA. hz2114@columbia.edu
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
authors.forename: Dongrong
authors.initials: D
authors.lastname: Xu
authors.forename: Amir
authors.initials: A
authors.lastname: Raz
authors.forename: Xuejun
authors.initials: X
authors.lastname: Hao
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
authors.forename: Alayar
authors.initials: A
authors.lastname: Kangarlu
authors.forename: Ravi
authors.initials: R
authors.lastname: Bansal
authors.forename: Bradley S
authors.initials: BS
authors.lastname: Peterson
mesh_terms: D000465:Algorithms; D001921:Brain / Q000166:cytology*; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity