Pulsed arterial spin labeling parameter optimization for an elderly population
Bibliographic record
Abstract
PURPOSE: To optimize pulsed arterial spin labeling (PASL) parameters for the elderly to take into account possible perfusion changes that occur in the brain with age. MATERIALS AND METHODS: Healthy young (N = 14, age range = 21-27 years) and elderly (N = 12, age range = 61-67 years) subjects were scanned using Q2TIPS (QUIPSS II with thin-slice TI, periodic saturation) with varying inversion times (TI(2)) at 1.5T. The difference signal (DeltaM), transit time (deltat), and cerebral blood flow (CBF) were calculated in segmented gray matter (GM). RESULTS: The young displayed more perfusion-weighted signal difference than the elderly at all TI(2)'s. The peak DeltaM occurred at TI(2) approximately 1300 msec and 1500 msec in the young and elderly groups, respectively. Qualitatively, intravascular signal was minimal in the younger group by TI(2) = 1500 msec, whereas a longer TI(2) of 1800 msec was needed to minimize this signal in the elderly. The transit time was approximately 100 msec longer in the elderly, and CBF was in the range of literature values. CONCLUSION: For acquiring perfusion-weighted images with minimal intravascular signal and adequate tissue signal for PASL studies of cerebral perfusion in the elderly, a longer inversion time is advantageous.
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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) | 0.000 | 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".