Diffusion‐weighted J‐resolved spectroscopy
Bibliographic record
Abstract
PURPOSE: To develop a novel diffusion-weighted magnetic resonance spectroscopy (DW-MRS) technique in conjunction with J-resolved spatially localized spectroscopy (JPRESS) to measure the apparent diffusion coefficients (ADCs) of brain metabolites beyond N-acetylaspartic acid (NAA), creatine (Cr), and choline (Cho) at 3T. This technique will be useful to probe tissue microstructures in vivo, as the various metabolites have different physiological characteristics. METHODS: Two JPRESS spectra were collected (high b-value and low b-value), and the ADCs of 16 different metabolites were estimated. Two analysis pipelines were developed: 1) a 2D pipeline that uses ProFit software to extract ADCs from metabolites not typically accessible at 3T and 2) a 1D pipeline that uses TARQUIN software to extract the metabolite concentrations from each line in the 2D dataset, allowing for scaling as well as validation. RESULTS: The ADCs of 16 different metabolites were estimated from within six subjects in parietal white matter. There was excellent agreement between the results obtained from the 1D and 2D pipelines for NAA, Cr, and Cho. CONCLUSION: The proposed technique provided consistent estimates for the ADCs of NAA, Cr, Cho, glutamate + glutamine, and myo-inositol in all subjects and additionally glutathione and scyllo-inositol in all but one subject. Magn Reson Med 78:1235-1245, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".