Noninvasive Measurement of Cerebral Blood Flow Under Anesthesia Using Arterial Spin Labeling MRI: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Cerebral ischemia plays a major role in pathophysiology of the injured brain. Most of the currently available methods of cerebral blood flow (CBF) monitors are either indirect measure of CBF or needing radioactive agents for data acquisition. Arterial spin labeling magnetic resonance imaging (ASL-MRI) is a noninvasive method of measuring CBF. The aim of our study was to determine the differences in the CBF values between propofol and sevoflurane anesthesia using ASL-MRI technique in mechanically ventilated patients with cerebrovascular disease. METHODS: After ethics board approval and informed consent, we measured CBF in 4 patients with moyamoya disease, using a pseudo-continuous 3D ASL sequence available on a 3.0 T MRI scanner. Patients were anesthetized first with sevoflurane (1 MAC) and then anesthesia was converted to total intravenous anesthesia with propofol (100 to 125 μg/kg/min). When the patient was in a steady state with respect to anesthesia and normocapnia (baseline PETCO2), CBF was measured under both sevoflurane and propofol anesthesia. RESULTS: Quantitative estimation of both global and regional CBF was successfully performed in all patients. The mean global CBF in gray and white matter of all patients under propofol anesthesia were 38.4 and 31.6 mL/100 g/min, respectively. Similar values under sevoflurane anesthesia were 56.6 mL/100 g/min for gray matter and 42.5 mL/100 g/min for white matter. CONCLUSIONS: ASL-MRI is a feasible, noninvasive method of quantitative estimation of global and regional CBF in mechanically ventilated patients under anesthesia. In this pilot study CBF was consistently greater with sevoflurane than with propofol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".