Mikkelsen Mark, Tapper Sofie, Near Jamie, Mostofsky Stewart H, Puts Nicolaas A J, Edden Richard A E
Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland.
F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland.
NMR Biomed. 2020 Oct;33(10):e4368. doi: 10.1002/nbm.4368. Epub 2020 Jul 12.
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.
本文提出了一种用于磁共振波谱（MRS）数据频率和相位偏移的回顾性校正算法。该算法称为稳健谱配准（rSR），包含一组子程序，旨在即使在存在显著频率和相位偏移、不稳定脂质污染或残留水信号的情况下，也能稳健地对齐给定数据集中的各个瞬态信号。通过具有不同子谱轮廓的复杂多路复用编辑方法采集的数据也能被准确对齐。如有需要，首先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，以统计最优顺序将每个瞬态信号与加权平均参考信号对齐，在时域中校正频率和相位偏移。编辑数据集中子谱的对齐使用一种专门针对频域减法伪影的方法来执行。然后使用加权平均进行信号平均，以降低质量较差的瞬态信号的权重。在一个模拟数据集和67个体内儿科GABA-/GSH编辑的HERMES数据集中评估了算法性能，并与先前开发的用于对齐HERMES数据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减法伪影的存在，对新方法的性能进行了定量评估。经过稳健对齐后，谱质量得到了改善，尤其是在存在显著谱失真的情况下。在64%的GABA差异谱和75%的GSH差异谱中，rSR比多步方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。
