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

• Published in: Lecture notes in computer science Vol. 10; no. Pt 2; p. 344
• Main Authors: Wiest-Daesslé, Nicolas, Prima, Sylvain, Coupé, Pierrick, Morrissey, Sean Patrick, Barillot, Christian
• Format: Journal Article Book Chapter
• Language: English
• Published: Germany 2007
• Subjects: Algorithms; Data Interpretation, Statistical; Diffusion Magnetic Resonance Imaging - methods; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Sensitivity and Specificity
• ISSN: 1611-3349; 0302-9743
• DOI: 10.1007/978-3-540-75759-7_42

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.