Comparing the reproducibility of commonly used magnetic resonance spectroscopy techniques to quantify cerebral glutathione
Bibliographic record
Abstract
BACKGROUND: Cerebral glutathione (GSH), a marker of oxidative stress, has been quantified in neurodegenerative diseases and psychiatric disorders using proton magnetic resonance spectroscopy (MRS). Using a reproducible MRS technique is important, as it minimizes the impact of measurement technique variability on the study results and ensures that other studies can replicate the results. HYPOTHESIS: We hypothesized that very short echo time (TE) acquisitions would have comparable reproducibility to a long TE MEGA-PRESS acquisition, and that the short TE PRESS acquisition would have the poorest reproducibility. STUDY TYPE: Prospective. SUBJECTS/PHANTOMS: Ten healthy adults were scanned during two visits, and six metabolite phantoms containing varying concentrations of GSH and metabolites with resonances that overlap with GSH were scanned once. FIELD STRENGTH/SEQUENCE: At 3T we acquired MRS data using four different sequences: PRESS, SPECIAL, PR-STEAM, and MEGA-PRESS. ASSESSMENT: Reproducibility of each MRS sequence across two visits was assessed. STATISTICAL TESTS: Mean coefficients of variation (CV) and mean absolute difference (AD) were used to assess reproducibility. Linear regressions were performed on data collected from phantoms to examine the agreement between known and quantified levels of GSH. RESULTS: ≥ 0.98 or higher) using all methods. DATA CONCLUSION: Our data suggest that GSH can be quantified reproducibly without the use of spectral editing. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:176-183.
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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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".