Comparison of MRS acquisition methods for separation of overlapping signals at 3 T
Bibliographic record
Abstract
BACKGROUND: Proton magnetic resonance spectroscopy (MRS) can be used to quantify multiple neurometabolites. However, due to the difficulty of separating overlapping signals at the commonly used field strength of 3 T, the quantified values are often composites of metabolically related chemicals. This can complicate interpretation and mask effects of interest. Therefore, it is important to determine the ability to accurately separate these signals at 3 T. Data acquired at 7 T can provide a benchmark, as higher field strength facilitates spectral resolution and reduces the signal overlap. NEW METHODS: This study assessed the ability of multiple 3 T MRS sequences to separate the commonly acquired neurochemicals (Glutamate (Glu) and Glutamine (Gln); N-Acetyl aspartate (NAA) and N-acetylaspartylglutamate (NAAG); Creatine (Cr) and Phosphocreatine (PCr); Choline (Cho), Phosphocholine (PC) and Glycerophosphocholine (GPC)). We compared metabolites quantified at 3 T from 6 acquisitions (PRESS, TE= 20, 30, 40, 80 ms, semi-LASER, TE=28 ms and STEAM TE=6 ms) with those quantified at 7 T using STEAM (TE=8 ms). RESULTS: Sequences with short echo times (STEAM-6, PRESS-20) generally performed better at separating most metabolites when using correlative and difference analyses with 7 T reference data. The exceptions were NAAG, which was best quantified with PRESS-80, and Cr and PCr, which were not well separated by any sequence. COMPARISON WITH EXISTING METHODS AND CONCLUSION: When wanting to specifically separate composite metabolite signals using single voxel MRS, shorter echo times generally perform better. Researchers should be mindful of the effects of acquisition parameters on the metabolite measures.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".