Department of Radiology, University of Miami, Miami, FL, USA.
Department of Biomedical Engineering, University of Miami, Miami, FL, USA.
Magn Reson Imaging. 2020 Jul;70:108-114. doi: 10.1016/j.mri.2020.04.013. Epub 2020 Apr 22.
Visual review of individual spectra in magnetic resonance spectroscopic imaging (MRSI) data benefits from the application of spectral smoothing; however, if this processing step is applied prior to spectral analysis this can impact the accuracy of the quantitation. This study aims to analyze the effect of spectral denoising and apodization smoothing on the quantitation of whole-brain MRSI data obtained at short TE.
Short-TE MRSI data obtained at 3 T were analyzed with no spectral smoothing, following (i) Gaussian apodization with values of 1, 2, 4, 6, and 8 Hz, and (ii) denoising using principal component analysis (dnPCA) with 3 different values for the number of retained principal components. The mean lobar white matter estimates for four metabolites, signal-to-noise ratio (SNR), spectral linewidth, and confidence intervals were compared to data reconstructed using no smoothing. Additionally, a voxel-wise comparison for N-acetylaspartate quantitation with different smoothing schemes was performed.
Significant pairwise differences were seen for all Gaussian smoothing methods as compared to no smoothing (p<0.001) in linewidth and metabolite estimates, whereas dnPCA methods showing no statistically significant differences in these measures. Confidence intervals decreased, and SNR increased with increasing levels of apodization smoothing or dnPCA denoising.
Mild Gaussian apodization (≤2 Hz at 3 T) can be applied with minimal (1%) errors in quantitation; however, smoothing values greater than that can significantly affect metabolite quantification. In contrast, mild to moderate dnPCA based denoising provides quantitative results that are consistent with the analysis of unsmoothed data and this method is recommended for spectral denoising.
磁共振波谱成像（MRSI）数据中个体谱的视觉检查得益于谱平滑的应用；然而，如果在谱分析之前应用此处理步骤，这可能会影响定量的准确性。本研究旨在分析谱去噪和谱平滑对短 TE 获得的全脑 MRSI 数据定量的影响。
在 3T 处获得的短 TE MRSI 数据在未进行谱平滑的情况下进行分析，采用(i)高斯加窗，窗值分别为 1、2、4、6 和 8Hz，和(ii)主成分分析（dnPCA）去噪，保留主成分的数量分别为 3 个。将四个代谢物的平均脑白质估计值、信噪比（SNR）、谱线宽和置信区间与未平滑重建的数据进行比较。此外，还对不同平滑方案的 N-乙酰天冬氨酸定量进行了体素水平比较。
与无平滑相比，所有高斯平滑方法在谱线宽和代谢物估计方面均存在显著的两两差异（p<0.001），而 dnPCA 方法在这些测量值方面没有统计学上的显著差异。随着加窗平滑或 dnPCA 去噪程度的增加，置信区间减小，SNR 增加。
在 3T 处，轻度高斯加窗（≤2Hz）可应用于定量，误差最小（1%）；然而，大于此值的平滑值会显著影响代谢物的定量。相比之下，轻度到中度基于 dnPCA 的去噪提供了与未平滑数据分析一致的定量结果，因此建议用于谱去噪。
