Abstract WP93: Cross-Institution Adaptive CT Perfusion Protocol for Stroke Workup Can Save Time, Radiation Dose and Improve Diagnostic Quality
Bibliographic record
Abstract
Introduction: CT perfusion (CTP) is an integral part of the diagnosis and treatment pathway for acute ischemic stroke patients, however CTP protocols vary in scan duration and radiation dose across institutions. An ideal protocol would customize scan acquisition for each individual patient in real-time to avoid truncation-related postprocessing errors and minimize time and radiation dose. We explore the potential time and radiation dosage savings if such an ideal adaptive CTP protocol were implemented across institutions. Methods: We retrospectively assessed 148 CTP scans across two institutions, postprocessed by an expert reader with the CT perfusion 4D Neuro software package (GE Healthcare). Bolus arrival, peak and exit time for the arterial input (AIF) and venous output (VOF) functions were determined via inflection point analysis of time-attenuation curves. An empirically determined customized adaptive scan protocol (Figure) was generated based on each patient’s AIF/VOF curves. Resultant reduction in radiation dose, scan acquisition time and total protocol time were calculated. Results: With the adaptive protocol, all scans would have optimal diagnostic image quality, which addresses the 3% and 18% exams of sub diagnostic quality at the two institutions respectively. Average total protocol time decreased by 153 s and 12 s (59% and 9%) and average radiation dose decreased by 61% and 12% respectively, in comparison to current protocols at the two institutions. Scan acquisition time increased for 7% (11 of 148) of exams. Conclusions: An adaptive CTP protocol would realize significant time and radiation savings across institutions. Methods for real-time identification of VOF end times are currently under development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".