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Impact of Rician Adapted Non-Local Means Filtering on HARDI

  • Maxime Descoteaux
  • Nicolas Wiest-Daesslé
  • Sylvain Prima
  • Christian Barillot
  • Rachid Deriche
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

In this paper we study the impact of denoising the raw high angular resolution diffusion imaging (HARDI) data with the Non-Local Means filter adapted to Rician noise (NLMr). We first show that NLMr filtering improves robustness of apparent diffusion coefficient (ADC) and orientation distribution function (ODF) reconstructions from synthetic HARDI datasets. Our results suggest that the NLMr filtering improve the quality of anisotropy maps computed from ADC and ODF and improve the coherence of q-ball ODFs with the underlying anatomy while not degrading angular resolution. These results are shown on a biological phantom with known ground truth and on a real human brain dataset. Most importantly, we show that multiple measurements of diffusion-weighted (DW) images and averaging these images along each direction can be avoided because NLMr filtering of the individual DW images produces better quality generalized fractional anisotropy maps and more accurate ODF fields than when computed from the averaged DW datasets.

Keywords

Angular Resolution Orientation Distribution Function Angular Error High Angular Resolution Rician Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Maxime Descoteaux
    • 1
  • Nicolas Wiest-Daesslé
    • 3
    • 4
    • 5
  • Sylvain Prima
    • 3
    • 4
    • 5
  • Christian Barillot
    • 3
    • 4
    • 5
  • Rachid Deriche
    • 2
  1. 1.NMR LabNeuroSpin, CEA SaclayFrance
  2. 2.Project Team Odyssée, INRIA Sophia AntipolisMéditerranéeFrance
  3. 3.INRIA, VisAGeS Project-TeamRennesFrance
  4. 4.INSERMRennesFrance
  5. 5.University of Rennes I, CNRS, UMR 6074, IRISARennesFrance

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