Novel flat-panel cone-beam CT compared to multi-detector CT for assessment of acute ischemic stroke: A prospective study
Bibliographic record
Abstract
PURPOSE: Cone beam CT (CBCT) imaging assessment of acute ischemic stroke (AIS) patients with large-vessel occlusion (LVO) in the angiosuite may improve stroke workflow and decrease time to recanalization. In order for this workflow to gain widespread acceptance, current CBCT imaging needs further development to improve image quality. Our study aimed to compare the image quality of a new CBCT protocol performed directly in the angiosuite with imaging from multidetector CT as a gold standard. METHODS: AIS patients with an LVO who were candidates for endovascular treatment were prospectively included in this study. Following conventional multidetector CT (MDCT), patients underwent unenhanced cone beam CT (XperCT, Philips) imaging in the angiosuite, using two different protocols: a standard 20.8 s XperCT and/or an improved 10.4 s XperCT protocol. Images were evaluated using both qualitative and quantitative methods. RESULTS: We included 65 patients in the study. Patients received CBCT imaging prior to endovascular treatment; 18 patients were assessed with a standard 20.8 s protocol scans and 47 with a newer 10.4 s scan. The quantitative analysis showed that the mean contrast-to-noise ratio (CNR) was significantly higher for the newer 10.4 s protocol compared with the 20.8 s protocol (2.08 +/- 0.64 vs. 1.15 +/- 0.27, p < 0.004) and the mean image noise was significantly lower for the 10.4 s XperCTs when compared with the 20.8 s XperCTs (6.30 +/- 1.34 vs. 7.82 +/- 2.03, p=<0.003). Qualitative analysis, including 6 measures of image quality, demonstrated that 74.1 % of the 10.4 s XperCT scans were ranked as 'Acceptable' for assessing parenchymal imaging in AIS patients(scoring 3-5 points on a 5-point Likert-scale), compared with 32.4 % of the standard 20.8 s XperCT and 100 % of the MDCT scans. Compared to the MDCT studies, 83 % of the 10.4 s XperCT scans were deemed sufficient image quality for a direct-to-angiosuite selection, compared to only 11 % for the standard 20.8 s scans. The largest image quality improvements included grey/white matter differentiation (59 % improvement), and reduction of image noise and artefacts (63 % & 50 % improvement, respectively). CONCLUSIONS: Continued advances in cone-beam CT allow marked improvements in image quality for the assessment of brain parenchyma, which supports a direct-to-angiosuite approach for AIS patients eligible for thrombectomy treatment.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".