title: Just how much data need to be collected for reliable bootstrap DT-MRI?
abstract: Diffusion tensor MRI (DT-MRI) can provide estimates of fiber orientation derived from the orientational dependence of the diffusivity of water molecules, enabling the reconstruction of white matter fiber pathways using tractography methods. However, noise arising from various sources can introduce uncertainty into the estimates of the elements of the diffusion tensor, resulting in errors in fiber orientation estimates such that tractography reconstructions of fiber pathways potentially can be imprecise and inaccurate. Recently, attempts have been made to characterize the uncertainty in DT-MRI-derived parameters using the bootstrap method; however, several questions remain open regarding the number of repeat measurements and bootstraps required to accurately and precisely reconstruct the probability distributions of the DT-MRI parameters. This study investigates the accuracy and precision of the bootstrap method for characterizing distributions of DT-MRI parameters. A number of experimental bootstrap designs and sampling schemes containing different numbers of isotropically distributed gradient vectors are considered, using an idealized system where the true variability in each parameter is known. This study demonstrates that for most DT-MRI experiments, robust results will be obtained if the minimum number of bootstraps is approximately 500, and that at least five repeat samples of each diffusion-weighted intensity should be used for bootstrapping.
authors.affiliation: Department of Neuroimaging, King's College Hospital, London, UK. spimrog@iop.kcl.ac.uk
authors.forename: Ruth L
authors.initials: RL
authors.lastname: O'Gorman
authors.forename: Derek K
authors.initials: DK
authors.lastname: Jones
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D003198:Computer Simulation; D003625:Data Collection / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D009010:Monte Carlo Method; D009412:Nerve Fibers; D018709:Statistics, Nonparametric