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Record W2142957282 · doi:10.1088/0031-9155/55/3/004

<i>In vivo</i>endoscopic multi-beam optical coherence tomography

2010· article· en· W2142957282 on OpenAlexafffund
Beau A. Standish, Kenneth K. C. Lee, Adrian Mariampillai, Nigel R. Munce, Michael K. K. Leung, Victor X. D. Yang, I. Alex Vitkin

Bibliographic record

VenuePhysics in Medicine and Biology · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSunnybrook Health Science CentreUniversity Health NetworkToronto Metropolitan UniversityOntario Institute for Cancer ResearchUniversity of Toronto
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsOptical coherence tomographyOpticsFull width at half maximumPreclinical imagingMaterials scienceIn vivoFocus (optics)TomographyResolution (logic)Aperture (computer memory)Beam (structure)Ghost imagingBiomedical engineeringEx vivoCoherence (philosophical gambling strategy)PhysicsComputer scienceMedicineArtificial intelligenceBiologyAcoustics

Abstract

fetched live from OpenAlex

A multichannel optical coherence tomography (multi-beam OCT) system and an in vivo endoscopic imaging probe were developed using a swept-source OCT system. The distal optics were micro-machined to produce a high numerical aperture, multi-focus fibre optic array. This combination resulted in a transverse design resolution of <10 microm full width half maximum (FWHM) throughout the entire imaging range, while also increasing the signal intensity within the focus of the individual channels. The system was used in a pre-clinical rabbit study to acquire in vivo structural images of the colon and ex vivo images of the oesophagus and trachea. A good correlation between the structural multi-beam OCT images and H&E histology was achieved, demonstrating the feasibility of this high-resolution system and its potential for in vivo human endoscopic imaging.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.339
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations47
Published2010
Admission routes2
Has abstractyes

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