Non-local means variants for denoising of diffusion-weighted and diffusion tensor MRI.

Nicolas Wiest-Daesslé 1 Sylvain Prima 1 Pierrick Coupé 1 Sean Patrick Morrissey 1 Christian Barillot 1
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
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.
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Submitted on : Tuesday, December 4, 2007 - 3:52:17 PM
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Nicolas Wiest-Daesslé, Sylvain Prima, Pierrick Coupé, Sean Patrick Morrissey, Christian Barillot. Non-local means variants for denoising of diffusion-weighted and diffusion tensor MRI.. 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct 2007, Brisbane, Australia. pp.344-51, ⟨10.1007/978-3-540-75759-7_42⟩. ⟨inserm-00193788⟩

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