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Record W2077391028 · doi:10.1117/12.761862

On the speckle size in optical coherence tomography

2008· article· en· W2077391028 on OpenAlexaff
Guy Lamouche, C.-E. Bisaillon, Sébastien Vergnole, J.‐P. Monchalin

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSpeckle patternOptical coherence tomographyOpticsCoherence (philosophical gambling strategy)PhysicsPoint spread functionSpeckle imagingImage resolutionSpeckle noiseCoherence length

Abstract

fetched live from OpenAlex

Speckle is always present in Optical Coherence Tomography (OCT) measurements. To a first approximation, the speckle size is determined by the OCT resolution length and the point spread function of the focusing optics in the sample arm. But the speckle size is also affected by the tissue microstructure. We demonstrate this phenomena by performing measurements on optical phantoms with a controlled density of scatterers using time-domain OCT. In the very low density limit, the scatterers are easily identified on the OCT cross-section and, in fact, one can hardly speak of a speckle pattern. The corresponding speckle size is the resolution length axially and the point spread function of the focusing optics transversally. As the number of scatterers increases, a true speckle field appears and the measured speckle size decreases. In the high density limit, the speckle size reaches an asymptotic value that is about 70% of its low-density regime values. In addition to experimental results, theoretical estimates of the limiting speckle size values are presented. Our work contributes to a better understanding of speckle in optical coherence tomography.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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 designTheoretical or conceptual
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

Citations10
Published2008
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coherence Tomography ApplicationsFrench-language works237,207