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Record W2098746729 · doi:10.1117/1.jbo.18.3.036004

Rapid radial optical coherence tomography image acquisition

2013· article· en· W2098746729 on OpenAlexafffund
Evgeniy Lebed, Sieun Lee, Marinko V. Šarunic, Mirza Faisal Beg

Bibliographic record

VenueJournal of Biomedical Optics · 2013
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsOptical coherence tomographyCartesian coordinate systemComputer scienceInterpolation (computer graphics)Computer visionImage qualityData acquisitionArtificial intelligenceOpticsCoherence (philosophical gambling strategy)TomographyDiffuse optical imagingOptical tomographyImage processingIterative reconstructionPhysicsImage (mathematics)Mathematics

Abstract

fetched live from OpenAlex

We demonstrate how compressive sampling can be used to expedite volumetric optical coherence tomography (OCT) image acquisition. We propose a novel method to interpolate OCT volumetric images from data acquired by radial B-scans in the Cartesian coordinate system. Due to the inherent polar symmetry in the human eye, the (r, θ, z) coordinate system provides a natural domain to perform the interpolation. We demonstrate that the method has minimal effect on image quality even when up to 88% of the data is not acquired. The potential outcome of this work could lead to significant reductions in OCT volume acquisition time in clinical practice.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations13
Published2013
Admission routes2
Has abstractyes

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