Assessment of arterial arrival times derived from multiple inversion time pulsed arterial spin labeling MRI
Bibliographic record
Abstract
The purpose of this study was to establish a normal range for the arterial arrival time (AAT) in whole-brain pulsed arterial spin labeling (PASL) cerebral perfusion MRI. Healthy volunteers (N = 36, range: 20 to 35 years) provided informed consent to participate in this study. AAT was assessed in multiple brain regions, using three-dimensional gradient and spin echo (GRASE) pulsed arterial spin labeling at 3.0 T, and found to be 641 +/- 95, 804 +/- 91, 802 +/- 126, and 935 +/- 108 ms in the temporal, parietal, frontal, and occipital lobes, respectively. Mean gray matter AAT was found to be 694 +/- 89 ms for females (N = 15), which was significantly shorter than for men, 814 +/- 192 ms (N = 21; P < 0.0003), and significant after correcting for brain volume (P < 0.001). Significant AAT sex differences were also found using voxelwise permutation testing. An atlas of AAT values across the healthy brain is presented here and may be useful for future experiments that aim to quantify cerebral blood flow from ASL data, as well as for clinical comparisons where disease pathology may lead to altered AAT. Pulsed arterial spin labeling signals were simulated using an identical sampling scheme as the empiric study and revealed AAT can be estimated robustly when simulated arrival times are well beyond the normal range.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".