A statistical framework for the classification of tensor morphologies in diffusion tensor images

Zhu, Hongtu; Xu, Dongrong; Raz, Amir; Hao, Xuejun; Zhang, Heping; Kangarlu, Alayar; Bansal, Ravi; Peterson, Bradley S.

doi:10.1016/j.mri.2006.01.004

« Previous Next »Magnetic Resonance Imaging
Volume 24, Issue 5 , Pages 569-582, June 2006

A statistical framework for the classification of tensor morphologies in diffusion tensor images

Hongtu Zhu
- MRI Unit, Department of Psychiatry, Columbia University Medical Center, USA
- Department of Child Psychiatry, The New York State Psychiatric Institute, New York, NY 10032, USA
- Corresponding author. Department of Child Psychiatry, The New York State Psychiatric Institute, New York, NY 10032, USA. Tel.: +1 212 543 5207; fax: +1 212 543 6660.
Dongrong Xu
- MRI Unit, Department of Psychiatry, Columbia University Medical Center, USA
- Department of Child Psychiatry, The New York State Psychiatric Institute, New York, NY 10032, USA
Amir Raz
- MRI Unit, Department of Psychiatry, Columbia University Medical Center, USA
- Department of Child Psychiatry, The New York State Psychiatric Institute, New York, NY 10032, USA
Xuejun Hao
- MRI Unit, Department of Psychiatry, Columbia University Medical Center, USA
- Department of Child Psychiatry, The New York State Psychiatric Institute, New York, NY 10032, USA
Heping Zhang
- Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA
Alayar Kangarlu
- MRI Unit, Department of Psychiatry, Columbia University Medical Center, USA
- Department of Child Psychiatry, The New York State Psychiatric Institute, New York, NY 10032, USA
Ravi Bansal
- MRI Unit, Department of Psychiatry, Columbia University Medical Center, USA
- Department of Child Psychiatry, The New York State Psychiatric Institute, New York, NY 10032, USA
Bradley S. Peterson
- MRI Unit, Department of Psychiatry, Columbia University Medical Center, USA
- Department of Child Psychiatry, The New York State Psychiatric Institute, New York, NY 10032, USA

Received 22 October 2005; accepted 16 January 2006. published online 17 March 2006.

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.

Keywords: Diffusion tensor, Invariant measure, Degenerate tensor, Tractography