Parametric and non-parametric statistical analysis of DT-MRI data. Export

@article{citeulike:3198076, 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.}, address = {Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5772, USA.}, author = {Pajevic, S. and Basser, P. J.}, citeulike-article-id = {3198076}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/12660106}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=12660106}, issn = {1090-7807}, journal = {Journal of magnetic resonance (San Diego, Calif. : 1997)}, keywords = {dt-mri, pajevic\_s}, month = {March}, number = {1}, pages = {1--14}, posted-at = {2009-06-30 21:06:53}, priority = {2}, title = {Parametric and non-parametric statistical analysis of DT-MRI data.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12660106}, volume = {161}, year = {2003} }

TY - JOUR ID - citeulike:3198076 L3 - citeulike-article-id:3198076 N2 - 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. IS - 1 JF - Journal of magnetic resonance (San Diego, Calif. : 1997) SN - 1090-7807 AD - Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5772, USA. EP - 14 TI - Parametric and non-parametric statistical analysis of DT-MRI data. VL - 161 SP - 1 KW - dt-mri KW - pajevic_s AU - Pajevic, S AU - Basser, PJ PY - 2003/03// UR - http://view.ncbi.nlm.nih.gov/pubmed/12660106 ER -