论文标题
MP-PCA扩散MRS数据的DeNoing：承诺和陷阱
MP-PCA denoising for diffusion MRS data: promises and pitfalls
论文作者
论文摘要
与常规MRS相比，由于增加扩散衰减，扩散加权（DW）磁共振光谱（MRS）遭受较低的信号与噪声比（SNR）。因此，该技术可以从降低降噪策略中受益匪浅。在目前的工作中，Marchenko-Pastur主成分分析（MP-PCA）在Monte Carlo模拟和大鼠大脑中9.4T的体内DW-MRS数据上进行了测试。我们提供了针对不同MP-PCA降解策略（使用滑动窗口的整个矩阵与整个矩阵与使用滑动窗口）观察到的效果的描述性研究，就明显的SNR，等级选择，B值内外的噪声相关性以及代谢物浓度和拟合扩散系数的噪声相关性。与原始数据相比，MP-PCA降解产生了明显的SNR，镜头之间更准确的B0漂移校正以及代谢物浓度和扩散率的相似估计值。没有观察到单个镜头上的光谱残差，但是引入了跨壳的噪声水平的相关性，这种效果是使用滑动窗口减轻的，但应仔细考虑。
Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction strategies. In the present work, the Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4T in the rat brain. We provide a descriptive study of the effects observed following different MP-PCA denoising strategies (denoising the entire matrix versus using a sliding window), in terms of apparent SNR, rank selection, noise correlation within and across b-values and quantification of metabolite concentrations and fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent SNR, a more accurate B0 drift correction between shots, and similar estimates of metabolite concentrations and diffusivities compared to the raw data. No spectral residuals on individual shots were observed but correlations in the noise level across shells were introduced, an effect which was mitigated using a sliding window, but which should be carefully considered.
