A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI.
A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI.
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DOI：
10.1371/journal.pone.0195952
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Trouard TP
中科院分区：
文献类型：
--
作者：
Chen NK;Chang HC;Bilgin A;Bernstein A;Trouard TP
在过去的几年里，人们做出了巨大的努力来提高扩散加权成像（DWI）的空间分辨率，旨在更好地检测细微病变并更可靠地解析白质纤维束。高分辨率DWI的一个主要问题是有限的信噪比（SNR），这可能会大大抵消高空间分辨率的优势。虽然DWI数据的SNR可以通过后处理中的去噪来提高，但是现有的去噪程序可能会潜在地降低高分辨率成像数据的解剖可分辨性。此外，低SNR DWI数据中的非高斯噪声引起的信号偏差可能并不总是用现有的去噪方法来校正。在这里，我们报告了一种改进的去噪过程，称为扩散匹配主成分分析（DM-PCA），它包括1）识别一组（不一定是相邻的）体素，所述体素沿着扩散维度沿着表现出非常相似的幅度信号变化模式，2）校正复值DWI数据中的低频相位变化，3）对真实的分量和非真实分量沿扩散维进行沿着PCA（在两个单独的通道中）具有匹配的扩散特性的相位校正的DWI体素，4）分别抑制真实的分量和伪分量中的噪声PCA分量，相位校正的DWI数据，以及5）组合去噪的DWI数据的真实的分量和伪分量。我们的数据显示，新的双通道（即，对于真实的分量和伪分量），DM-PCA去噪过程可靠地执行，而不会显著地损害解剖学可分辨性。非高斯噪声引起的信号偏差也可以减少与新的去噪方法。基于DM-PCA的去噪程序应该证明在研究和临床应用中对于高分辨率DWI研究是非常有价值的。
Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses.
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