RESTORE: Robust estimation of tensors by outlier rejection

Chang, Lin-Ching, Jones, Derek K. ORCID: https://orcid.org/0000-0003-4409-8049 and Pierpaoli, Carlo 2005. RESTORE: Robust estimation of tensors by outlier rejection. Magnetic Resonance in Medicine 53 (5) , pp. 1088-1095. 10.1002/mrm.20426

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Abstract

Signal variability in diffusion weighted imaging (DWI) is influenced by both thermal noise and spatially and temporally varying artifacts such as subject motion and cardiac pulsation. In this paper, the effects of DWI artifacts on estimated tensor values, such as trace and fractional anisotropy, are analyzed using Monte Carlo simulations. A novel approach for robust diffusion tensor estimation, called RESTORE (for robust estimation of tensors by outlier rejection), is proposed. This method uses iteratively reweighted least-squares regression to identify potential outliers and subsequently exclude them. Results from both simulated and clinical diffusion data sets indicate that the RESTORE method improves tensor estimation compared to the commonly used linear and nonlinear least-squares tensor fitting methods and a recently proposed method based on the Geman–McClure M-estimator. The RESTORE method could potentially remove the need for cardiac gating in DWI acquisitions and should be applicable to other MR imaging techniques that use univariate or multivariate regression to fit MRI data to a model.

Item Type:	Article
Status:	Published
Schools:	Psychology
Uncontrolled Keywords:	robust estimation; outliers; trace; anisotropy; diffusion; tensor
Publisher:	Wiley
ISSN:	0740-3194
Last Modified:	20 Oct 2022 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/33019

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