Volumetric microscopy of cerebral arteries with a miniaturized optical coherence tomography imaging probe
Bibliographic record
Abstract
Endovascular interventions are increasingly becoming the preferred approach for treating strokes and cerebral artery diseases. These procedures rely on sophisticated angiographical imaging guidance, which encounters challenges because of limited contrast and spatial resolution. Achieving a more precise visualization of the underlying arterial pathology and neurovascular implants is crucial for accurate procedural decision-making. In a human study involving 32 patients, we introduced the clinical application of a miniaturized endovascular neuro optical coherence tomography ( n OCT) imaging probe. This technology was designed to navigate the tortuous paths of the cerebrovascular circulation and to offer high-resolution imaging in situ. The n OCT probe is compatible with standard neurovascular microcatheters, integrating with the procedural workflow used in clinical routine. Equipped with a miniaturized optical fiber and a distal lens, the probe illuminates the tissue and collects the backscattered, near-infrared light. While rotating the fiber and the lens at high speed, the probe is rapidly retracted, creating a spiral-shaped light pattern to comprehensively capture the arterial wall and implanted devices. Using n OCT, we demonstrated volumetric microscopy of cerebral arteries in patients undergoing endovascular procedures. We imaged the anterior and posterior circulation of the brain, including distal segments of the internal carotid and middle-cerebral arteries, as well as the vertebral, basilar, and posterior cerebral arteries. We captured a broad spectrum of neurovascular pathologies, such as brain aneurysms, ischemic stroke, arterial stenoses, dissections, and intracranial atherosclerotic disease. n OCT offered artifact-free, high-resolution visualizations of intracranial artery pathology and neurovascular devices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| 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".