The butterfly effect: improving brain cone-beam CT image artifacts for stroke assessment using a novel dual-axis trajectory
Bibliographic record
Abstract
BACKGROUND: Cone-beam computed tomography (CBCT) imaging of the brain can be performed in the angiography suite to support various neurovascular procedures. Relying on CBCT brain imaging solely, however, still lacks full diagnostic confidence due to the inferior image quality compared with CT and various imaging artifacts that persist even with modern CBCT. OBJECTIVE: To perform a detailed evaluation of image artifact improvement using a new CBCT protocol which implements a novel dual-axis 'butterfly' trajectory. METHODS: Our study included 94 scans from 47 patients who received CBCT imaging for assessment of either ischemia or hemorrhage during a neurovascular procedure. Both a traditional uni-axis 'circular' and novel dual-axis 'butterfly' protocol were performed on each patient (same-patient control). Each brain scan was divided into six regions and scored out of 3 based on six artifacts originating from various physics-based and patient-based sources. RESULTS: The dual-axis trajectory produces CBCT images with significantly fewer image artifacts than the traditional circular scan (whole brain average artifact score, AS: 0.20 vs 0.33), with the greatest improvement in bone beam hardening (AS: 0.13 vs 0.78) and cone-beam artifacts (AS: 0.04 vs 0.55). CONCLUSIONS: Recent developments in CBCT imaging protocols have significantly improved image artifacts, which has improved diagnostic confidence for stroke and supports a direct-to-angiography suite transfer approach for patients with acute ischemic stroke.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".