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Record W4316663279 · doi:10.1364/oe.476768

Triple-clad W-type fiber mitigates multipath artifacts in multimodal optical coherence tomography

2023· article· en· W4316663279 on OpenAlexafffund
A. Tanskanen, Jeanie Malone, Geoffrey Hohert, Calum MacAulay, Pierre Lane

Bibliographic record

VenueOptics Express · 2023
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSimon Fraser UniversityVancouver General HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsOptical coherence tomographyOpticsOptical fiberImage qualityCladding (metalworking)Materials scienceSingle-mode optical fiberComputer sciencePhysicsComputer vision

Abstract

fetched live from OpenAlex

Multimodal endoscopic optical coherence tomography (OCT) can be implemented with double-clad fiber by using the presumed single-mode core for OCT and the higher numerical aperture cladding for a secondary modality. However, the quality of OCT in double-clad fiber (DCF) based systems is compromised by the introduction of multipath artifacts that are nt present in single-mode fiber OCT systems. Herein, the mechanisms for multipath artifacts in DCF are linked to its modal contents using a commercial software package and experimental measurement. A triple-clad W-type fiber is proposed as a method for achieving multimodal imaging with single-mode quality OCT in an endoscopic system. Simulations of the modal contents of a W-type fiber are compared to DCF and single-mode fiber. Finally, a W-Type fiber rotary catheter is used in a DCF-based endoscopic OCT and autofluorescence imaging (AFI) system to demonstrate multipath artifact free OCT and AFI of a human fingertip.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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.

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

Citations6
Published2023
Admission routes2
Has abstractyes

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