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RESTORE: Robust estimation of tensors by outlier rejection†

  1. Lin-Ching Chang1,
  2. Derek K. Jones1,2,
  3. Carlo Pierpaoli1,*

Article first published online: 20 APR 2005

DOI: 10.1002/mrm.20426

Issue

Magnetic Resonance in Medicine

Magnetic Resonance in Medicine

Volume 53, Issue 5, pages 1088–1095, May 2005

Additional Information

How to Cite

Chang, L.-C., Jones, D. K. and Pierpaoli, C. (2005), RESTORE: Robust estimation of tensors by outlier rejection. Magn. Reson. Med., 53: 1088–1095. doi: 10.1002/mrm.20426

Author Information

  1. 1

    Section on Tissue Biophysics and Biomimetics, Laboratory of Integrative Medicine and Biophysics, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA

  2. 2

    Centre for Neuroimaging Sciences, Institute of Psychiatry, London, United Kingdom

*National Institutes of Health, Building 13, Room 3W16, 13 South Drive, Bethesda, MD 20892-5772, USA

  1. †

    This article is a US Government work and, as such, is in the public domain in the United States of America.

Publication History

  1. Issue published online: 20 APR 2005
  2. Article first published online: 20 APR 2005
  3. Manuscript Revised: 29 NOV 2004
  4. Manuscript Accepted: 29 NOV 2004
  5. Manuscript Received: 9 SEP 2004

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Keywords:

  • robust estimation;
  • outliers;
  • trace;
  • anisotropy;
  • diffusion;
  • tensor

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. Magn Reson Med 53:1088–1095, 2005. Published 2005 Wiley-Liss, Inc.

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