Non-local means variants for denoising of diffusion-weighted and diffusion tensor MRI.
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.
Unit/Project VisAGeS U746, INSERM - INRIA - CNRS - Univ-Rennes 1, IRISA campus Beaulieu 35042 Rennes, France. nwiestda@irisa.fr
Nicolas
N
Wiest-Daesslé
Sylvain
S
Prima
Pierrick
P
Coupé
Sean Patrick
SP
Morrissey
Christian
C
Barillot
D000465:Algorithms; D016477:Artifacts; D003627:Data Interpretation, Statistical; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity