title: Iterative reweighted linear least squares for accurate, fast, and robust estimation of diffusion magnetic resonance parameters.
abstract: OBJECTIVE
  Diffusion-weighted magnetic resonance imaging suffers from physiological noise, such as artifacts caused by motion or system instabilities. Therefore, there is a need for robust diffusion parameter estimation techniques. In the past, several techniques have been proposed, including RESTORE and iRESTORE (Chang et al. Magn Reson Med 2005; 53:1088-1095; Chang et al. Magn Reson Med 2012; 68:1654-1663). However, these techniques are based on nonlinear estimators and are consequently computationally intensive.
  
  
  METHODS
  In this work, we present a new, robust, iteratively reweighted linear least squares (IRLLS) estimator. IRLLS performs a voxel-wise identification of outliers in diffusion-weighted magnetic resonance images, where it exploits the natural skewness of the data distribution to become more sensitive to both signal hyperintensities and signal dropouts.
  
  
  RESULTS
  Both simulations and real data experiments were conducted to compare IRLLS with other state-of-the-art techniques. While IRLLS showed no significant loss in accuracy or precision, it proved to be substantially faster than both RESTORE and iRESTORE. In addition, IRLLS proved to be even more robust when considering the overestimation of the noise level or when the signal-to-noise ratio is low.
  
  
  CONCLUSIONS
  The substantially shortened calculation time in combination with the increased robustness and accuracy, make IRLLS a practical and reliable alternative to current state-of-the-art techniques for the robust estimation of diffusion-weighted magnetic resonance parameters.
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
authors.forename: Quinten
authors.initials: Q
authors.lastname: Collier
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
authors.forename: Jelle
authors.initials: J
authors.lastname: Veraart
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
authors.forename: Ben
authors.initials: B
authors.lastname: Jeurissen
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.|Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands.
authors.forename: Arnold J
authors.initials: AJ
authors.lastname: den Dekker
authors.affiliation: iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Sijbers
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001931:Brain Mapping / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007231:Infant, Newborn; D016018:Least-Squares Analysis; D059629:Signal-To-Noise Ratio
keywords: MRI; diffusion tensor imaging; outlier detection; robust; weighted linear least squares