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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Non-Local Means Variants for Denoising of Diffusion-Weighted and Diffusion Tensor MRI
verfasst von : Nicolas Wiest-Daesslé, Sylvain Prima, Pierrick Coupé, Sean Patrick Morrissey, Christian Barillot
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Verlag: Springer Berlin Heidelberg
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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.