Triple‐quantum‐filtered sodium imaging of the human brain at 4.7 T
Bibliographic record
Abstract
The limited signal-to-noise ratio of triple-quantum-filtered MRI of sodium is a major hurdle for its application clinically. Although it has been shown that short 90° radiofrequency pulses in combination with sufficiently long repetition time for full T(1) recovery (labelled "standard" parameters) produce the maximum signal through the triple-quantum-filter, and in this work, simulation and images of agar phantoms and human brain demonstrate that the use of longer radiofrequency pulses and reduced repetition time (optimized parameters to accommodate more averages for a constant specific absorption rate, reducing noise variance for a given scan length) results in signal-to-noise ratio improvement (22 ± 5% in brain tissue of five healthy volunteers--images created in 11 min with nominal resolution of 8.4 mm isotropic). However, residual intensity was observed in the ventricular space on triple-quantum-filtered images acquired with either optimized or standard parameters, contrary to the expectation of complete single-quantum signal suppression. Further simulation and experimentation suggest that this is likely due to the combination of triple-quantum-passed signal from surrounding brain tissue being spatially smeared into the ventricular space and single-quantum-signal breakthrough from sodium nuclei in the fluid space. It is shown that the latter can be eliminated with judicious first flip angle selection.
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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) | 0.002 | 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".