کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1808028 1025307 2006 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A statistical framework for the classification of tensor morphologies in diffusion tensor images
موضوعات مرتبط
مهندسی و علوم پایه فیزیک و نجوم فیزیک ماده چگال
پیش نمایش صفحه اول مقاله
A statistical framework for the classification of tensor morphologies in diffusion tensor images
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Magnetic Resonance Imaging - Volume 24, Issue 5, June 2006, Pages 569-582
نویسندگان
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