Semi‐LASER <sup>1</sup>H MR spectroscopy at 7 Tesla in human brain: Metabolite quantification incorporating subject‐specific macromolecule removal
Bibliographic record
Abstract
Purpose To develop an in vivo 1H short‐echo‐time semi‐LASER spectroscopy protocol at 7 Tesla (T) incorporating subject‐specific macromolecule removal. Methods T1 constants of the major metabolites were measured with little macromolecule contribution in seven healthy volunteers and used to optimize double inversion metabolite nulling. Spectra were acquired from parietal–occipital cortex of five healthy volunteers. Metabolite‐nulled macromolecule spectra were subtracted from the metabolite spectra before fitting in the time domain with prior‐knowledge templates. Absolute metabolite concentrations were determined by referencing to the water signal, following partial volume and relaxation corrections. Results The average signal to noise ratio, N‐acetylaspartate peak height divided by the baseline noise standard deviation, was 48 ± 6. T1 constants for N‐acetylaspartate, glutamate, creatine, and choline were 1.71 ± 0.15 s, 1.68 ± 0.19 s, 1.63 ± 0.10 s, and 1.41 ± 0.09 s, respectively. The optimal double inversion times for metabolite suppression were TI1 = 2.09 s and TI2 = 0.52 s. The coefficient of variation was less than 10% for N‐acetylaspartate, creatine, choline, and myo‐inositol, and less than 20% for glutamate and glutamine. Conclusion Short echo‐time 1H semi‐LASER spectroscopy at 7T incorporating subject‐specific macromolecule removal yielded reproducible brain metabolite concentrations ideal for applications in disease conditions where macromolecule contributions may deviate from the norm. Magn Reson Med 74:4–12, 2015. © 2014 Wiley Periodicals, Inc.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".