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

Abstract. Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusionweighted 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.

• Type: Conference
• Year: 2007
• Where: MICCAI
• Authors: Christian Barillot, Nicolas Wiest-Daesslé, Pierrick Coupé, Sean Patrick Morrissey, Sylvain Prima
• Keywords: Better Image Interpretation, Classical Denoising Methods, Diffusion Tensor, Medical Imaging, MICCAI 2007