Intra‐ and inter‐site reproducibility of human brain single‐voxel proton MRS at 3 T
Bibliographic record
Abstract
Introduction Clinical trials that involve participants from multiple sites necessitate standardized and reliable quantitative MRI outcomes to detect significant group differences over time. Metabolite concentrations measured by proton MRS (1H‐MRS) provide valuable information about in vivo metabolism of the central nervous system, but can vary based on the acquisition and quantitation methods used by different MR sites. Therefore, we investigated the intra‐ and inter‐site reproducibility of metabolite concentrations measured by 1H‐MRS on MRI scanners from a single manufacturer across six sites. Methods Five healthy controls were scanned twice within 24 h at six participating 3 T MR sites with large single‐voxel PRESS (TE/TR/NSA = 36 ms/4000 ms/56) and anatomical images for voxel positioning and correction of partial volume relaxation. Absolute metabolite concentrations were calculated relative to the T1 and T2 relaxation corrected signal from water. Intra‐ and inter‐site reproducibility was assessed using Bland–Altman plots and intra‐ and inter‐site coefficient of variation (CoV) as well as intra‐ and inter‐site intra‐class correlation coefficient. Results The median intra‐site CoVs for the five major metabolite concentrations ([NAA], [tCr], [Glu], [tCho] and [Ins]) were between 2.5 and 5.3%. Inter‐site CoVs were also low, with the median CoVs for all metabolites between 3.7 and 6.4%. Metabolite concentrations were robust to small inconsistencies in voxel placement and site was not the driving factor in the variance of the measurement of any metabolite concentration. Between‐subject differences accounted for the majority of the concentration variability for creatine, choline and myo‐inositol (42–65% of the variance). Conclusion A large single‐voxel 1H‐MRS acquisition from a single manufacturer's MRI scanner is highly reproducible and reliable for multi‐site clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".