XQ-NLM: Denoising Diffusion MRI Data via x-q Space Non-Local Patch Matching.
XQ-NLM: Denoising Diffusion MRI Data via x-q Space Non-Local Patch Matching.
复制标题
XQ-NLM：通过X-Q空间非本地贴片匹配的扩散MRI数据。
DOI：
10.1007/978-3-319-46726-9_68
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Yap PT
中科院分区：
文献类型：
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作者：
Chen G;Wu Y;Shen D;Yap PT
噪声是影响弥散MRI定量分析的主要问题。噪声的影响可以通过重复采集来减少，但这会导致较长的采集时间，这在临床环境中是不现实的。因此，采集后去噪方法被广泛用于提高信噪比。在现有的方法中，非局部均值（NLM）具有较好的图像质量和边缘保存效果。然而，目前NLM在扩散MRI中的应用主要集中在空间空间（即x空间）上，而扩散数据存在于x空间和q空间（即波向量空间）的组合空间中。在本文中，我们提出将NLM推广到x空间和q空间。我们展示了如何在方位等距投影和旋转不变特征的帮助下，在x-q空间中同时执行NLM所需的补丁匹配。在合成数据和实际数据上进行的大量实验证实，所提出的x-q空间NLM （XQ-NLM）优于经典的NLM。
Noise is a major issue influencing quantitative analysis in diffusion MRI. The effects of noise can be reduced by repeated acquisitions, but this leads to long acquisition times that can be unrealistic in clinical settings. For this reason, post-acquisition denoising methods have been widely used to improve SNR. Among existing methods, non-local means (NLM) has been shown to produce good image quality with edge preservation. However, currently the application of NLM to diffusion MRI has been mostly focused on the spatial space (i.e., the x-space), despite the fact that diffusion data live in a combined space consisting of the x-space and the q-space (i.e., the space of wavevectors). In this paper, we propose to extend NLM to both x-space and q-space. We show how patch-matching, as required in NLM, can be performed concurrently in x-q space with the help of azimuthal equidistant projection and rotation invariant features. Extensive experiments on both synthetic and real data confirm that the proposed x-q space NLM (XQ-NLM) outperforms the classic NLM.