Non-Local Means Variants for Denoising of Diffusion-Weighted and Diffusion Tensor MRI
Non-Local Means Variants for Denoising of Diffusion-Weighted and Diffusion Tensor MRI
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DOI：
10.1007/978-3-540-75759-7_42
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发表时间：
2007-10
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影响因子：
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通讯作者：
Nicolas Wiest-Daesslé;S. Prima;P. Coupé;S. Morrissey;C. Barillot
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文献类型：
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作者：
Nicolas Wiest-Daesslé;S. Prima;P. Coupé;S. Morrissey;C. Barillot
由于扩散加权图像强度（DW-MRI）和所得到的扩散张量之间的非线性关系，扩散张量成像（DT-MRI）对破坏噪声非常敏感。去噪是提高估计张量场质量的关键步骤。这种增强的质量允许更好的量化和更好的图像解释。本文提出的方法是基于非局部（NL）的平均算法。该方法利用图像中信息的自然冗余性来去除噪声。我们介绍了三种变化的NL-均值算法适应DW-MRI和DT-MRI。实验进行了一组12个扩散加权图像（DW-MRI）的同一主题。结果表明，基于强度的NL-means方法在DT-MRI的背景下比其他经典的去噪方法，如高斯平滑，各向异性扩散和总变分，得到更好的结果。
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.