Motion artifact correction for cone beam CT stroke imaging: a prospective series
Bibliographic record
Abstract
BACKGROUND: Imaging assessment for acute ischemic stroke (AIS) patients in the angiosuite using cone beam CT (CBCT) has created increased interest since endovascular treatment became the first line therapy for proximal vessel occlusions. One of the main challenges of CBCT imaging in AIS patients is degraded image quality due to motion artifacts. This study aims to evaluate the prevalence of motion artifacts in CBCT stroke imaging and the effectiveness of a novel motion artifact correction algorithm for image quality improvement. METHODS: Patients presenting with acute stroke symptoms and considered for endovascular treatment were included in the study. CBCT scans were performed using the angiosuite X-ray system. All CBCT scans were post-processed using a motion artifact correction algorithm. Motion artifacts were scored before and after processing using a 4-point scale. RESULTS: We prospectively included 310 CBCT scans from acute stroke patients. 51% (n=159/310) of scans had motion artifacts, with 24% being moderate to severe. The post-processing algorithm improved motion artifacts in 91% of scans with motion (n=144/159), restoring clinical diagnostic capability in 34%. Overall, 76% of the scans were sufficient for clinical decision-making before correction, which improved to 93% (n=289/310) after post-processing with our algorithm. CONCLUSIONS: Our results demonstrate that CBCT motion artifacts are significantly reduced using a novel post-processing algorithm, which improved brain CBCT image quality and diagnostic assessment for stroke. This is an important step on the road towards a direct-to-angio approach for endovascular thrombectomy (EVT) treatment.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".