Whole-brain quantitative mapping of metabolites using short echo three-dimensional proton MRSI
Bibliographic record
Abstract
BACKGROUND: To improve the extent over which whole brain quantitative three-dimensional (3D) magnetic resonance spectroscopic imaging (MRSI) maps can be obtained and be used to explore brain metabolism in a population of healthy volunteers. METHODS: Two short echo time (20 ms) acquisitions of 3D echo planar spectroscopic imaging at two orientations, one in the anterior commissure-posterior commissure (AC-PC) plane and the second tilted in the AC-PC +15° plane were obtained at 3 Tesla in a group of 10 healthy volunteers. B1 (+) , B1 (-) , and B0 correction procedures and normalization of metabolite signals with quantitative water proton density measurements were performed. A combination of the two spatially normalized 3D-MRSI, using a weighted mean based on the pixel wise standard deviation metabolic maps of each orientation obtained from the whole group, provided metabolite maps for each subject allowing regional metabolic profiles of all parcels of the automated anatomical labeling (AAL) atlas to be obtained. RESULTS: The combined metabolite maps derived from the two acquisitions reduced the regional intersubject variance. The numbers of AAL regions showing N-acetyl aspartate (NAA) SD/Mean ratios lower than 30% increased from 17 in the AC-PC orientation and 41 in the AC-PC+15° orientation, to a value of 76 regions of 116 for the combined NAA maps. Quantitatively, regional differences in absolute metabolite concentrations (mM) over the whole brain were depicted such as in the GM of frontal lobes (cNAA = 10.03 + 1.71; cCho = 1.78 ± 0.55; cCr = 7.29 ± 1.69; cmIns = 5.30 ± 2.67) and in cerebellum (cNAA = 5.28 ± 1.77; cCho = 1.60 ± 0.41; cCr = 6.95 ± 2.15; cmIns = 3.60 ± 0.74). CONCLUSION: A double-angulation acquisition enables improved metabolic characterization over a wide volume of the brain.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".