Poster - 04: Resolving the Glutamine Resonance with an Optimized Magnetic Resonance Spectroscopy PRESS Sequence at 9.4T
Bibliographic record
Abstract
Purpose: To optimize the timings of the magnetic resonance spectroscopy technique, Point RESolved Spectroscopy (PRESS), to resolve glutamine (Gln; ∼2.45ppm) in vivo at 9.4T. The Gln resonance is contaminated by N-acetylaspartate (NAA; ∼2.49ppm) and to a lesser extent by glutathione (GSH; ∼2.51ppm). Methods: Signals of glutamate (Glu; ∼2.35ppm), Gln, and NAA at 9.4T in response to TE (echo time) values of a PRESS sequence were investigated numerically, and the {TE1, TE2} combination that best resolved Gln while retaining Glu was considered optimal. The timings were verified on phantom solutions and in vivo on four Sprague Dawley rat brains. In-vivo spectra were analyzed with LCmodel. Results: An optimal {TE1, TE2} combination was determined to be {106ms, 16ms}, which resulted in simulated peak areas for Glu, Gln, and NAA, of 54%, 42%, and −2%, respectively, of the corresponding shortest {2ms, 2ms} values. Experimentally, the Gln yield with the optimal TE values was found to be ∼54% of that obtained with a short-TE of {TE1, TE2} = {12ms, 9ms}. The yield changed by <10% with the addition of NAA and GSH. The rat brain spectra showed well resolved peaks for Glu and Gln at ∼2.35ppm and ∼2.45ppm, respectively. LCModel Cramér-Rao Lower Bound values for all rats were <8% and <20% for Glu and Gln, respectively. Conclusions: A PRESS sequence with {TE1, TE2} = {106ms, 16ms} is suitable for resolving Gln in vivo at 9.4T.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".