Ex vivo imaging of chronic total occlusions using forward‐looking optical coherence tomography
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Percutaneous coronary interventions (PCI) of chronic total occlusions (CTOs) of arteries are more challenging lesions to treat with angioplasty and stenting than stenotic vessels due primarily to the difficulty in guiding the wire across the lesion. Angiography alone is unable to differentiate between the occluded lumen and the vessel wall and to characterize the content of the occlusion. New technologies to aid in interventional guidance are therefore highly desirable. We sought to evaluate tissue characterization in arterial (CTOs) by imaging ex vivo peripheral arterial samples with optical coherence tomography (OCT). STUDY DESIGN/MATERIALS AND METHODS: Ex vivo arterial samples were obtained from patients undergoing peripheral limb amputation. Samples were imaged in an enface orientation using an OCT system, enabling sequential acquisition of longitudinal images and volumetric reconstruction of cross-sectional views of the occluded arteries. Histology was performed for comparison. RESULTS: OCT imaging reliably differentiated between the occluded lumen and the underlying arterial wall in peripheral CTOs. OCT correctly identified tissue composition within the CTO, such as the presence of collagen and calcium and was also able to identify intraluminal microchannels. CONCLUSIONS: OCT imaging of CTO anatomy and tissue characteristics may potentially lead to substantial improvements in PCI interventions by providing novel guiding capabilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 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".