A Note on the Validity of Statistical Bootstrapping for Estimating the Uncertainty of Tensor Parameters in Diffusion Tensor Imag

Abstract--Diffusion tensors are estimated from magnetic resonance images (MRIs) that are diffusion-weighted, and those images inherently contain noise. Therefore, noise in the diffusion-weighted images produces uncertainty in estimation of the tensors and their derived parameters, which include eigenvalues, eigenvectors, and the trajectories of fiber pathways that are reconstructed from those eigenvalues and eigenvectors. Although repetition and wild bootstrap methods have been widely used to quantify the uncertainty of diffusion tensors and their derived parameters, we currently lack theoretical derivationsthat would validate the use of thesetwo bootstrap methods for the estimation of statistical parameters of tensors in the presence of noise. The aim of this paper is to examine theoretically and numerically the repetition and wild bootstrap methods for approximating uncertainty in estimation of diffusion tensor parameters under two different schemes for acquiring diffusion weighted i...

• Type: Journal
• Year: 2008
• Where: TMI
• Authors: Ying Yuan, Hongtu Zhu, Joseph G. Ibrahim, Weili Lin, Bradley S. Peterson
• Keywords: Bootstrap Methods, Diffusion Tensor, Tensors, TMI 2008