Quantitative Evaluation of Ischemic Core Volume in GE’s CT Perfusion Imaging Analysis Software and Its Relationship to Alberta Stroke Program Early CT Score
Bibliographic record
Abstract
Objective: Computed tomography (CT) and magnetic resonance imaging of cerebral perfusion are useful in determining the indication of mechanical thrombectomy (MT) for acute ischemic stroke. RAPID (iSchemaView, Menlo Park, CA, USA) is the most common software for analyzing brain perfusion images worldwide, but various other software are also available. The optimal threshold value for each software is different, and each has its characteristics. This study investigated the relationship between the quantitative evaluation of ischemic core volume (ICV) and the Alberta Stroke Program Early CT Score (ASPECTS) using CT Perfusion 4D (GE Healthcare Inc., Milwaukee, WI, USA), a software used in our hospital. Methods: Among patients who underwent MT between April 2015 and February 2023, those with modified Rankin Scale: 0-2, obstruction by embolic mechanism, and thrombolysis in cerebral infarction: 2b or higher were selected retrospectively. Patients with middle cerebral artery M1 segment (M1) and internal carotid artery (ICA) occlusions (90 and 46 patients) were included. We quantitatively analyzed ICV at relative cerebral blood flow (rCBF) <20% and cerebral blood volume (CBV) <1 mL/100 g and evaluated the relationship with ASPECTS scores in 3 groups: M1 + ICA, M1, and ICA occlusion groups. Results: , and there was no statistically significant difference between the 2 groups (p = 0.23). There was a negative correlation between ICV and ASPECTS scores in each occlusion group in all groups. Conclusion: The quantitative evaluation of ICV at rCBF <20% and CBV <1 mL/100 g was negatively correlated with the ASPECTS score in GE's CT Perfusion imaging analysis software.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".