title: Variance of estimated DTI-derived parameters via first-order perturbation methods.
abstract: In typical applications of diffusion tensor imaging (DTI), DT-derived quantities are used to make a diagnostic, therapeutic, or scientific determination. In such cases it is essential to characterize the variability of these tensor-derived quantities. Parametric and empirical methods have been proposed to estimate the variance of the estimated DT, and quantities derived from it. However, the former method cannot be generalized since a parametric distribution cannot be found for all DT-derived quantities. Although powerful empirical methods, such as the bootstrap, are available, they require oversampling of the diffusion-weighted imaging (DWI) data. Statistical perturbation methods represent a hybrid between parametric and empirical approaches, and can overcome the primary limitations of both methods. In this study we used a first-order perturbation method to obtain analytic expressions for the variance of DT-derived quantities, such as the trace, fractional anisotropy (FA), eigenvalues, and eigenvectors, for a given experimental design. We performed Monte Carlo (MC) simulations of DTI experiments to test and validate these formulae, and to determine their range of applicability for different experimental design parameters, including the signal-to-noise ratio (SNR), diffusion gradient sampling scheme, and number of DWI acquisitions. This information should be useful for designing DTI studies and assessing the quality of inferences drawn from them.
authors.affiliation: Section on Tissue Biophysics and Biomimetics, Laboratory of Integrative Medicine and Biophysics, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 20892-5772, USA. changlin@nih.gov
authors.forename: Lin-Ching
authors.initials: LC
authors.lastname: Chang
authors.forename: Cheng Guan
authors.initials: CG
authors.lastname: Koay
authors.forename: Carlo
authors.initials: C
authors.lastname: Pierpaoli
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Basser
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D008297:Male; D009010:Monte Carlo Method; D015203:Reproducibility of Results