Endovascular Cerebral Venous Sinus Imaging with Optical Coherence Tomography
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Imaging of the cerebral venous sinuses has evolved Substantially during the past 2 decades, and most recently intravascular sinus imaging with sonography has shed light on the pathophysiology of sinus thrombosis and intracranial hypertension. Optical coherence tomography is the highest resolution intravascular imaging technique available but has not been previously used in cerebral sinus imaging. The purpose of this study was to develop a preclinical animal model of endovascular optical coherence tomography cerebral venous sinus imaging and compare optical coherence tomography findings with histology. MATERIALS AND METHODS: Four consecutive Yorkshire swine were selected. The superior sagittal sinus was first catheterized with a microwire, and the optical coherence tomography catheter was delivered via a monorail technique into the sinus. Luminal blood was cleared with a single arterial injection. After structural and Doppler optical coherence tomography imaging, a craniotomy was performed and the sinus and adjacent dura/veins were resected. Bland-Altman analysis was performed to compare optical coherence tomography and histology. RESULTS: Technically successful optical coherence tomography images were obtained in 3 of 4 swine. The luminal environment and visualization of dural arteries and draining cortical veins were characterized. The average maximum diameters of the sinus, dural arteries, and cortical veins were 3.14 mm, 135 µm, and 260 µm, respectively. Bland-Altman analysis demonstrated good agreement between histology and optical coherence tomography images. CONCLUSIONS: Endovascular optical coherence tomography imaging was feasible in this preclinical animal study. Adoption of this imaging technique in the human cerebral venous sinus could aid in the diagnosis, treatment, and understanding of the pathophysiology of various diseases of the sinus. Human safety and feasibility studies are needed.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".