Abstract P334: Multiphase Computed Tomography Angiography-Perfusion for Quantitative Measurement of Ischemic Volumes
Bibliographic record
Abstract
Introduction: Proficiency required to execute CT perfusion (CTP) protocols is a limiting factor in its use in acute stroke. We propose to calculate perfusion parametric maps and measure ischemic volumes using readily available non-contrast CT (NCCT) and multiphase CT angiography (mCTA) images. Materials and Methods: Twenty-five patients presenting with acute ischemic stroke were included in this study. Our proposed dynamic sequence (multiphase CT angiography-perfusion, mCTA-P) consisted of the NCCT as the pre-contrast baseline and three phases of mCTA, which corresponded to the peak arterial, peak venous, and late venous phases at 8 s intervals. CTP was acquired after mCTA and consisted of 22 dynamic images acquired over 60 s at 2.8 s intervals. A prototype model-based deconvolution algorithm (CT Perfusion 4D, GE Healthcare) was used to calculate cerebral blood flow (CBF) and Tmax maps for each series. Infarct was classified as voxels that satisfied both a time-dependent relative CBF threshold and Tmax > 8 s while penumbral voxels satisfied either threshold but not both. Results: Median (interquartile range) 24-hour follow-up infarct volume was 18.6 (4.7 to 34.3) ml and median stroke onset-to-CTP time was 124.0 (70.5 to 201.5) min. Bland-Altman analysis revealed good agreement between CTP and mCTA-P volume measurements as mean differences (limits of agreement) were -1.0 (-14.9 to 12.9) ml for infarct and 8.4 (-42.4 to 59.1) ml for penumbra. Intraclass correlation (95% confidence interval, p < 0.05) between CTP and mCTA-P volumes were 0.72 (0.46 to 0.87) for infarct and 0.68 (0.41 to 0.85) for penumbra, indicating good to moderate reliability. Conclusion: Quantitative perfusion can be estimated from NCCT and mCTA without introducing additional scan time, radiation dose, and contrast injections associated with CTP. Our technique allows assessments of early ischemic changes and collaterals to be augmented with quantitative perfusion measurements of ischemic volumes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".