Rapid acquisition diffusion MR spectroscopy of metabolites in human brain
Bibliographic record
Abstract
Few studies have focused on metabolite diffusion in the human brain using 1 H‐MRS due to significant technical challenges. Moreover, such studies have required lengthy acquisition times and are therefore impractical to implement clinically. By first characterizing and then minimizing the effects of linear and oscillating eddy currents, which arise from the diffusion gradients, and by implementing phase‐cycle and slice‐order strategies, as well as introducing a new phase‐alignment methodology, we report a method that allows data acquisition requiring 20 seconds per spectrum. This remained feasible, even for b ‐values >8000 s/mm 2 , with a rapid acquisition diffusion MRS methodology. It has allowed the nonlinear characterization of signal intensity with multiple b ‐values, and has improved the measurement of rotationally invariant diffusion parameters via six‐direction, six b ‐value diffusion tensor spectroscopy (DTS) in 12 minutes at 4.7 T. The shorter DTS acquisition will enable its application to white matter regions not aligned with the gradients and permit clinical studies in a feasible time.
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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) | 0.000 | 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".