title: Parametric and non-parametric statistical analysis of DT-MRI data.
abstract: In this work parametric and non-parametric statistical methods are proposed to analyze Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data. A Multivariate Normal Distribution is proposed as a parametric statistical model of diffusion tensor data when magnitude MR images contain no artifacts other than Johnson noise. We test this model using Monte Carlo (MC) simulations of DT-MRI experiments. The non-parametric approach proposed here is an implementation of bootstrap methodology that we call the DT-MRI bootstrap. It is used to estimate an empirical probability distribution of experimental DT-MRI data, and to perform hypothesis tests on them. The DT-MRI bootstrap is also used to obtain various statistics of DT-MRI parameters within a single voxel, and within a region of interest (ROI); we also use the bootstrap to study the intrinsic variability of these parameters in the ROI, independent of background noise. We evaluate the DT-MRI bootstrap using MC simulations and apply it to DT-MRI data acquired on human brain in vivo, and on a phantom with uniform diffusion properties.
authors.affiliation: Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5772, USA.
authors.forename: Sinisa
authors.initials: S
authors.lastname: Pajevic
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Basser
mesh_terms: D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D019047:Phantoms, Imaging