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Record W4411823307 · doi:10.1364/oe.567621

Liquid lens-based endoscopic OCT probe with adjustable focus for 3D imaging in cross-sectionally irregular lumens

2025· article· en· W4411823307 on OpenAlexaff
Zhongxing Wang, Yurui Pu, W Xiong, Xiaolong Yu, Rui Zou, Jiancai Huang, Xingjian Liu, Yu Sun

Bibliographic record

VenueOptics Express · 2025
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Toronto
FundersLiaoning Revitalization Talents Program
KeywordsOpticsLens (geology)Focus (optics)Optical coherence tomographyMaterials scienceIntegral imagingPhysicsComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.669
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.249
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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