Variability of metabolite yield using STEAM or PRESS sequences in vivo at 3.0 T, illustrated with myo‐inositol
Bibliographic record
Abstract
Using as an example the myo-inositol (mI) band at approximately 3.6 ppm in the proton spectrum from brain, an evaluation is presented that highlights the difficulties of quantifying metabolites with strongly coupled spins with either STEAM or PRESS and demonstrates some advantages of prospective sequence analysis when measuring their concentrations. The analysis emphasizes the variation in coupled-spin signal yield and lineshape, compared with that of uncoupled singlets such as N-acetylaspartate, a variation that differs from one metabolite spin system to another. This difference in variation between a target metabolite (e.g., mI) and its contaminating background metabolites (e.g., glutamate and taurine, etc.) is shown to provide in certain circumstances a substantial reduction in background contamination (both metabolite and macromolecule) while maintaining sufficient signal-to-noise ratio for precise quantification. For example, sequence times are demonstrated, both for STEAM and for PRESS, that, relative to the short echo-time sequences typical in the literature, enhance the signal to metabolite background of the 3.6-ppm band of mI by factors of 1.7 and 1.3, respectively, essentially eliminate the macromolecular baseline, and yet in vivo retain an S/N approximately 10 in both cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 teacher head, 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".