Comparison of the quantification precision of human short echo time1H spectroscopy at 1.5 and 4.0 Tesla
Bibliographic record
Abstract
Precise quantification of human in vivo short echo time (1)H spectra remains problematic at clinical field strengths due to broad peak linewidths and low signal-to-noise ratio (SNR). In this study, multiple STEAM spectra (TE = 20 ms, volume = 8 cm(3)) were acquired in a single individual at 1.5 T and 4 T to compare quantification precision. Test-retest STEAM spectra (volume = 1.5 cm(3)) were also acquired from the anterior cingulate and thalamus of 10 individuals at 4.0 T. Metabolite levels were quantified using automated software that incorporated field strength-specific prior knowledge. With the distinct methods of data acquisition, processing, and fitting used in this study, peak height SNR increased approximately 80% while peak linewidth increased by approximately 50% in the 8 cm(3) volumes at 4.0 T compared to 1.5 T, resulting in an average increase in quantification precision of 39%. Metabolite levels from test-retest data (1.5 cm(3) voxels at 4.0 T) were quantified with similar inter- and intraindividual variability. Magn Reson Med 44:185-192, 2000. Published 2000 Wiley-Liss, 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".