Efficient sampling of early signal arrival for estimation of perfusion and transit time in whole‐brain arterial spin labeling
Bibliographic record
Abstract
Arterial spin labeling can be used to measure both cerebral perfusion and arterial transit time. However, accurate estimation of these parameters requires adequate temporal sampling of the arterial spin labeling difference signal. In whole-brain multislice acquisitions, two factors reduce the accuracy of the parameter estimates: saturation of labeled blood in transit and inadequate sampling of early difference signal in superior slices. Label saturation arises when slices are acquired inferior-to-superior such that slice selection in proximal slices spoils the label for a distal slice. Inadequate sampling arises when the time spent acquiring inferior slices is too long to allow early sampling of the difference signal in superior slices. A novel approach to multislice imaging is proposed to address these two issues. In round-robin arterial spin labeling, slices are acquired in a different order after every pair of control-label acquisitions. Round-robin arterial spin labeling enables the acquisitions of all slices across the same range of postlabel delays in a descending superior-to-inferior order. This eliminates the temporal sampling problem and greatly reduces label saturation. Arterial transit time estimates obtained for the whole brain with round-robin arterial spin labeling show better agreement with a single-slice acquisition than do conventional multislice acquisitions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".