title: Statistical artifacts in diffusion tensor MRI (DT-MRI) caused by background noise.
abstract: This work helps elucidate how background noise introduces statistical artifacts in the distribution of the sorted eigenvalues and eigenvectors in diffusion tensor MRI (DT-MRI) data. Although it was known that sorting eigenvalues (principal diffusivities) by magnitude introduces a bias in their sample mean within a homogeneous region of interest (ROI), here it is shown that magnitude sorting also introduces a significant bias in the variance of the sample mean eigenvalues. New methods are presented to calculate the mean and variance of the eigenvectors of the diffusion tensor, based on a dyadic tensor representation of eigenvalue-eigenvector pairs. Based on their use it is shown that sorting eigenvalues by magnitude also introduces a bias in the mean and the variance of the sample eigenvectors (principal directions). This required the development of new methods to calculate the mean and variance of the eigenvectors of the diffusion tensor, based on a dyadic tensor representation of eigenvalue-eigenvector pairs. Moreover, a new approach is proposed to order these pairs within an ROI. To do this, a correspondence between each principal axis of the diffusion ellipsoid, an eigenvalue-eigenvector pair, and a dyadic tensor constructed from it is exploited. A measure of overlap between principal axes of diffusion ellipsoids in different voxels is defined that employs projections between these dyadic tensors. The optimal eigenvalue assignment within an ROI maximizes this overlap. Bias in the estimate of the mean and of the variance of the eigenvalues and of their corresponding eigenvectors is reduced in DT-MRI experiments and in Monte Carlo simulations of such experiments. Improvement is most significant in isotropic regions, but some is also observed in anisotropic regions. This statistical framework should enhance our ability to characterize microstructure and architecture of healthy tissue, and help to assess its changes in development, disease, and degeneration. Mitigating these artifacts should also improve the characterization of diffusion anisotropy and the elucidation of fiber-tract trajectories in the brain and in other fibrous tissues. Magn Reson Med 44:41-50, 2000. Published 2000 Wiley-Liss, Inc.
authors.affiliation: Section on Tissue Biophysics and Biomimetics, Laboratory of Integrative and Medical Biophysics, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, 20892-5772, USA. pjbasser@helix.nih.gov
authors.forename: P J
authors.initials: PJ
authors.lastname: Basser
authors.forename: S
authors.initials: S
authors.lastname: Pajevic
mesh_terms: D016477:Artifacts; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009010:Monte Carlo Method; D013223:Statistics as Topic