Wiest-Daesslé Nicolas, Prima Sylvain, Coupé Pierrick, Morrissey Sean Patrick, Barillot Christian
Unit/Project VisAGeS U746, INSERM - INRIA - CNRS - Univ-Rennes 1, IRISA campus Beaulieu 35042 Rennes, France.
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):344-51. 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.
扩散张量成像（DT - MRI）对噪声干扰非常敏感，这是因为扩散加权图像强度（DW - MRI）与所得扩散张量之间存在非线性关系。去噪是提高估计张量场质量的关键步骤。这种提高后的质量有助于更好地进行量化和图像解释。本文提出的方法基于非局部（NL）均值算法。该方法利用图像中信息的自然冗余来去除噪声。我们引入了三种适用于DW - MRI和DT - MRI的NL均值算法变体。对同一受试者的一组12幅扩散加权图像（DW - MRI）进行了实验。结果表明，在DT - MRI背景下，基于强度的NL均值方法比其他经典去噪方法（如高斯平滑、各向异性扩散和总变分法）能给出更好的结果。
