Informed RESTORE: A method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts

Authors

  • Lin-Ching Chang,

    Corresponding author
    1. Department of Electronic Engineering and Computer Science, The Catholic University of America, Washington, District of Columbia, USA
    • D.Sc., Department of Electrical Engineering and Computer Science, The Catholic University of America, 620 Michigan Avenue, NE, Washington, DC 20064

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  • Lindsay Walker,

    1. Center for Neuroscience and Regenerative Medicine at the Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
    2. National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
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  • Carlo Pierpaoli

    1. National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
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Abstract

Physiological noise artifacts, especially those originating from cardiac pulsation and subject motion, are common in clinical Diffusion tensor-MRI acquisitions. Previous works show that signal perturbations produced by artifacts can be severe and neglecting to account for their contribution can result in erroneous diffusion tensor values. The Robust Estimation of Tensors by Outlier Rejection (RESTORE) method has been shown to be an effective strategy for improving tensor estimation on a voxel-by-voxel basis in the presence of artifactual data points in diffusion-weighted images. In this article, we address potential instabilities that may arise when using RESTORE and propose practical constraints to improve its usability. Moreover, we introduce a method, called informed RESTORE designed to remove physiological noise artifacts in datasets acquired with low redundancy (less than 30–40 diffusion-weighted image volumes)—a condition in which the original RESTORE algorithm may converge to an incorrect solution. This new method is based on the notion that physiological noise is more likely to result in signal dropouts than signal increases. Results from both Monte Carlo simulation and clinical diffusion data indicate that informed RESTORE performs very well in removing physiological noise artifacts for low redundancy diffusion-weighted image datasets. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.

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