A Noninvasive Method for Quantifying Cerebral Blood Flow by Hybrid PET/MRI
Bibliographic record
Abstract
Although PET with 15O-water is the gold standard for imaging cerebral blood flow (CBF), quantification requires measuring the arterial input function (AIF), which is an invasive and noisy procedure. To circumvent this problem, we propose a noninvasive PET/MRI approach that eliminates the need to measure AIF by using global CBF determined by phase-contrast (PC) MRI as a reference region. This approach not only is noninvasive but also involves no additional imaging time, because PC MRI and 15O-water PET are acquired simultaneously. The purpose of this study was to test the accuracy of this hybrid method in an animal model in which AIF was measured directly and CBF was varied by changing the arterial CO2 tension. Methods: PET and MRI data were simultaneously acquired in juvenile pigs at hypocapnia (n = 5), normocapnia (n = 5), and hypercapnia (n = 4). CBF was measured by the MRI reference method and by PET alone using an MRI-compatible blood sampling system to measure AIF. Results: Global CBF estimates from PC MRI and 15O-water PET agreed well, with a correlation coefficient of 0.9 and a slope of 0.88. Strong positive correlations (R2 > 0.96) were also found between regional CBF generated by the PET-only and the MRI-reference methods. Conclusion: These findings demonstrate the accuracy of this hybrid PET/MRI approach, which might prove useful in patients for whom obtaining accurate CBF measurements is challenging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".