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Non-Local Means Variants for Denoising of Diffusion-Weighted and Diffusion Tensor MRI

  • Nicolas Wiest-Daesslé
  • Sylvain Prima
  • Pierrick Coupé
  • Sean Patrick Morrissey
  • Christian Barillot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4792)

Abstract

Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (DW-MRI) and the resulting diffusion tensor. Denoising is a crucial step to increase the quality of the estimated tensor field. This enhanced quality allows for a better quantification and a better image interpretation. The methods proposed in this paper are based on the Non-Local (NL) means algorithm. This approach uses the natural redundancy of information in images to remove the noise. We introduce three variations of the NL-means algorithms adapted to DW-MRI and to DT-MRI. Experiments were carried out on a set of 12 diffusion-weighted images (DW-MRI) of the same subject. The results show that the intensity based NL-means approaches give better results in the context of DT-MRI than other classical denoising methods, such as Gaussian Smoothing, Anisotropic Diffusion and Total Variation.

Keywords

Fractional Anisotropy Denoising Method Rician Noise Principal Geodesic Analysis Propose Denoising Method 
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 2007

Authors and Affiliations

  • Nicolas Wiest-Daesslé
    • 1
  • Sylvain Prima
    • 1
  • Pierrick Coupé
    • 1
  • Sean Patrick Morrissey
    • 1
  • Christian Barillot
    • 1
  1. 1.Unit/Project VisAGeS U746, INSERM - INRIA - CNRS - Univ-Rennes 1, IRISA campus Beaulieu 35042 Rennes CedexFrance

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