Short‐TE proton MRS is used to study metabolism in the human brain. Common analysis methods model the data as a linear combination of metabolite basis spectra. This large‐scale multi‐site study compares the levels of the four major metabolite complexes in short‐TE spectra estimated by three linear‐combination modeling (LCM) algorithms. 277 medial parietal lobe short‐TE PRESS spectra (TE = 35 ms) from a recent 3 T multi‐site study were preprocessed with the Osprey software. The resulting spectra were modeled with Osprey, Tarquin and LCModel, using the same three vendor‐specific basis sets (GE, Philips and Siemens) for each algorithm. Levels of total N‐acetylaspartate (tNAA), total choline (tCho), myo‐inositol (mI) and glutamate + glutamine (Glx) were quantified with respect to total creatine (tCr). Group means and coefficient of variations of metabolite estimates agreed well for tNAA and tCho across vendors and algorithms, but substantially less so for Glx and mI, with mI systematically estimated as lower by Tarquin. The cohort mean coefficient of determination for all pairs of LCM algorithms across all datasets and metabolites was trueR2¯= 0.39, indicating generally only moderate agreement of individual metabolite estimates between algorithms. There was a significant correlation between local baseline amplitude and metabolite estimates (cohort mean trueR2¯= 0.10). While mean estimates of major metabolite complexes broadly agree between linear‐combination modeling algorithms at group level, correlations between algorithms are only weak‐to‐moderate, despite standardized preprocessing, a large sample of young, healthy and cooperative subjects, and high spectral quality. These findings raise concerns about the comparability of MRS studies, which typically use one LCM software and much smaller sample sizes.
Purpose To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex. Methods 102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm3, pre‐inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite‐nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a “clean” MM spectrum using the Osprey software. Pearson’s correlation coefficient was calculated between integrals and age for the 14 MM signals. One‐way ANOVA was performed to determine differences between age groups. An independent t‐test was carried out to determine differences between sexes. Results MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One‐way ANOVA also suggested no age‐group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM‐predominant CSO (R = –0.29) and GM‐predominant PCC regions (R = –0.57) respectively while CSF increased significantly with age in both regions. Conclusion Our findings suggest that a pre‐defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.
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.
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