Feasibility and precision of cerebral blood flow and cerebrovascular reactivity MRI measurements using a computer‐controlled gas delivery system in an anesthetised juvenile animal model
Bibliographic record
Abstract
PURPOSE: To demonstrate the feasibility and repeatability of cerebrovascular reactivity (CVR) imaging using a controlled CO(2) challenge in mechanically ventilated juvenile pigs. MATERIALS AND METHODS: Precise end-tidal partial pressure CO(2) (PETCO(2)) control was achieved via a computer-controlled model-driven prospective end-tidal targeting (MPET) system integrated with mechanical ventilation using a custom-built secondary breathing circuit. Test-retest blood-oxygen level dependent (BOLD) CVR images were collected in nine juvenile pigs by quantifying the BOLD response to iso-oxic square-wave PETCO(2) changes. For comparison, test-retest baseline arterial spin labeling (ASL) cerebral blood flow (CBF) images were collected. Repeatability was quantified using the intra-class correlation coefficient (ICC) and coefficient of variation (CV). RESULTS: The repeatability of the PETCO(2) (CV < 2%) step changes resulted in BOLD CVR ICC > 0.94 and CV < 6% for cortical brain regions, which was similar to ASL CBF repeatability (ICC > 0.96 and CV < 4%). CONCLUSION: This study demonstrates the feasibility and precision of CVR imaging with an MPET CO(2) challenge in mechanically ventilated subjects using an animal model. Translation of this method into clinical imaging protocols may enable CVR imaging in young children with cerebrovascular disease who require general anesthesia.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".