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Record W2012407707 · doi:10.1364/ol.33.001530

Speckle variance detection of microvasculature using swept-source optical coherence tomography

2008· article· en· W2012407707 on OpenAlexaff
Adrian Mariampillai, Beau A. Standish, Eduardo H. Moriyama, Mamta Khurana, Nigel R. Munce, Michael K. K. Leung, James Jiang, Alex Cable, Brian C. Wilson, I. Alex Vitkin, Victor X. D. Yang

Bibliographic record

VenueOptics Letters · 2008
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpeckle patternOptical coherence tomographyOpticsMaterials scienceSpeckle noiseSpeckle imagingLaser Doppler velocimetryCoherence (philosophical gambling strategy)MicrocirculationPreclinical imagingBiomedical engineeringBlood flowPhysicsIn vivoMedicine

Abstract

fetched live from OpenAlex

We report on imaging of microcirculation by calculating the speckle variance of optical coherence tomography (OCT) structural images acquired using a Fourier domain mode-locked swept-wavelength laser. The algorithm calculates interframe speckle variance in two-dimensional and three-dimensional OCT data sets and shows little dependence to the Doppler angle ranging from 75 degrees to 90 degrees . We demonstrate in vivo detection of blood flow in vessels as small as 25 microm in diameter in a dorsal skinfold window chamber model with direct comparison with intravital fluorescence confocal microscopy. This technique can visualize vessel-size-dependent vascular shutdown and transient vascular occlusion during Visudyne photodynamic therapy and may provide opportunities for studying therapeutic effects of antivascular treatments without on exogenous contrast agent.

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 score0.000
Threshold uncertainty score0.001

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 integrity0.0000.000
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 head0.011
GPT teacher head0.202
Teacher spread0.190 · 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

Citations706
Published2008
Admission routes1
Has abstractyes

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