Liquid lens-based endoscopic OCT probe with adjustable focus for 3D imaging in cross-sectionally irregular lumens
Bibliographic record
Abstract
For imaging and inspecting lumens in industrial components, endoscopic optical coherence tomography (OCT), with its high resolution and compact probe, is a promising method. The majority of OCT probes possess a fixed depth of field (DOF) and suffer from missing point clouds when measuring cross-sectionally irregular lumens, limiting their application scenarios. To address this problem, we developed an endoscopic OCT probe that incorporates dual electrically tunable liquid lenses for dynamic focal length adjustment, making the system adaptable for imaging irregularly shaped lumens. Furthermore, an automated focus tracking method was developed to adjust (1) the focal position of the probe, thereby ensuring continuous alignment with target surfaces, and (2) the optical path length of the reference arm to sustain optimal interference conditions, guided by feedback derived from the depth information of interference signals. Experimental results demonstrated that the probe with adjustable focus achieved a lateral resolution of better than 20 μ m within a DOF range of 10-40 mm. Tests on diverse cross-sectionally irregular lumens confirmed the effectiveness of the proposed method.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".