An algorithm for retrospective correction of frequency and phase offsets in MRS data is presented. The algorithm, termed robust spectral registration (rSR), contains a set of subroutines designed to robustly align individual transients in a given dataset even in cases of significant frequency and phase offsets or unstable lipid contamination and residual water signals. Data acquired by complex multiplexed editing approaches with distinct subspectral profiles are also accurately aligned. Automated removal of unstable lipid contamination and residual water signals is applied first, when needed. Frequency and phase offsets are corrected in the time domain by aligning each transient to a weighted average reference in a statistically optimal order using nonlinear least‐squares optimization. The alignment of subspectra in edited datasets is performed using an approach that specifically targets subtraction artifacts in the frequency domain. Weighted averaging is then used for signal averaging to down‐weight poorer‐quality transients. Algorithm performance was assessed on one simulated and 67 in vivo pediatric GABA‐/GSH‐edited HERMES datasets and compared with the performance of a multistep correction method previously developed for aligning HERMES data. The performance of the novel approach was quantitatively assessed by comparing the estimated frequency/phase offsets against the known values for the simulated dataset or by examining the presence of subtraction artifacts in the in vivo data. Spectral quality was improved following robust alignment, especially in cases of significant spectral distortion. rSR reduced more subtraction artifacts than the multistep method in 64% of the GABA difference spectra and 75% of the GSH difference spectra. rSR overcomes the major challenges of frequency and phase correction.

中文翻译：

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使用稳健的频谱配准校正 MRS 数据中的频率和相位偏移。

提出了一种用于对 MRS 数据中的频率和相位偏移进行追溯校正的算法。该算法称为稳健光谱配准 (rSR)，包含一组子程序，旨在稳健地对齐给定数据集中的各个瞬态，即使在存在显着频率和相位偏移或不稳定的脂质污染和残留水信号的情况下也是如此。通过复杂的多路复用编辑方法获取的具有不同子光谱配置文件的数据也可以准确对齐。需要时，首先应用自动去除不稳定的脂质污染和残留水信号。通过使用非线性最小二乘优化以统计最佳顺序将每个瞬态与加权平均参考对齐，在时域中校正频率和相位偏移。编辑数据集中的子光谱对齐是使用一种专门针对频域中的减法伪影的方法来执行的。然后将加权平均用于信号平均以降低质量较差的瞬变。算法性能在一个模拟和 67 个体内儿科 GABA/GSH 编辑的 HERMES 数据集上进行了评估，并与之前为对齐 HERMES 数据而开发的多步校正方法的性能进行了比较。通过将估计的频率/相位偏移与模拟数据集的已知值进行比较，或通过检查体内数据中是否存在减法伪影，对新方法的性能进行了定量评估。稳健对齐后，光谱质量得到改善，尤其是在光谱失真严重的情况下。在 64% 的 GABA 差异光谱和 75% 的 GSH 差异光谱中，rSR 比多步法减少了更多的减法伪影。rSR 克服了频率和相位校正的主要挑战。