Assessment of metabolite quantitation reproducibility in serial 3D‐<sup>1</sup>H‐MR spectroscopic imaging of human brain using stereotactic repositioning
Bibliographic record
Abstract
Intrasubject reproducibility of metabolite quantitation in three-dimensional proton magnetic resonance spectroscopic imaging (3D-MRSI) was investigated in 10 healthy volunteers over five separate sessions using two echo times (TEs): 144 and 30 ms. The use of a Gill-Thomas-Cosman (GTC) stereotactic head frame enabled precise subject repositioning and immobilization. Metabolite levels from each voxel in the volume of interest (VOI) were quantified using the Linear Combination of Model spectra (LCModel) analysis algorithm, and coefficients of variation (CVs) were calculated. Standard error estimates (%SD or Cramer-Rao lower bounds) generated by LCModel were used as a confidence filter. The 95% confidence interval (CI) was found for each metabolite, providing an indication of the normal fluctuation expected for 3D-MRSI. In vivo, median CVs at the %SD < or = 20 level were found to be (%CV for TE = 144 and 30 ms, respectively): N-acetyl-aspartate plus N-acetyl-aspartyl-glutamate (NAA): 10.2% and 13.5%; creatine plus phosphocreatine (Cr), 14.4% and 21.7%; and choline-containing compounds (Cho), 15.2% and 18.4%. Relaxing the statistical filtering criteria to %SD < or = 30 increased median CVs by less than 5% and permitted in vivo quantitation reproducibility to be evaluated for glutamine plus glutamate (Glx) and myoinositol (Ins) for TE = 30 ms, yielding CVs of 24.0% and 21.0%, respectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".