Abstract TP69: Three-Phase Helical Computed Tomographic Angiography Perfusion Maps Predict Final Infarction in the Acute Ischemic Stroke Setting
Bibliographic record
Abstract
Background: Three-phase helical CTA can provide information on parenchymal hemodynamics distal to the occlusion, similar to CT perfusion (CTP). Helical CTA is a less expensive and a more widely available modality. We propose two perfusion map algorithms applied to three-phase helical CTA, providing optimal thresholds for prediction of final infarct volume Methods: 44 stroke patients with occlusion visible on CTA were acutely imaged with three-phase CTA (temporal sampling was 8 seconds). MR diffusion weighted imaging (DWI) between 24-48 hours were used to measure final infarct volume. The three-phase helical CTA perfusion maps were denoted “Delay” and “Flow Average” (see Figure 1). The maps were filtered using a 3D Gaussian blurring technique. The maps were generated for all patients, then a receiver operating characteristic (ROC) curve was generated for the merged patient data, comparing infarct vs. normal tissue. Thresholds were determined using ROC curves by optimizing sensitivity and specificity. Results: The “Delay” map generated an ROC curve with an Area-Under-Curve (AUC) of statistical significance. The “Flow Average” map generated an ROC curve with an Area-Under-Curve (AUC) of statistical significance. Conclusion: The proposed “Delay” and “Flow Average” perfusion maps applied to three-phase CTA predicted final infarct volume to a high degree of accuracy, close to CTP accuracies from the literature. These results show the capability of three-phase helical CTA to generate quantitative perfusion maps which will be useful for non-tertiary centers that do not have access to expensive post-processing software.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".