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Record W2055523933 · doi:10.1364/ao.49.006170

Optical design of a line-focused forward-viewing scanner for optical coherence tomography

2010· article· en· W2055523933 on OpenAlexaff
Mohammad Mostafa Kamal, Sivakumar Narayanswamy, Muthukumaran Packirisamy

Bibliographic record

VenueApplied Optics · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsOpticsAchromatic lensStrehl ratioScannerLens (geology)ZemaxOptical coherence tomographyVignettingField of viewApodizationGradient-index opticsFocal lengthPhysicsAdaptive opticsComputer scienceRefractive index

Abstract

fetched live from OpenAlex

We report an optical design of a line-focused forward-viewing optical coherence tomography (OCT) scanner for high-speed endoscopic imaging. To avoid a complex lens system, an off-axis cylindrical mirror is used for focusing the line illumination onto the sample surface. Because of its insensitivity to the broadband spectrum, the mirror-focused scanner improves the image quality compared to a lens-focused scanner. In this work, a feasibility study is carried out on the use of a reflective-optics-focused line scanner in OCT imaging, instead of the traditional refractive optics scanner. The Strehl ratio, chromatic focal shift, and field analysis were carried out for a plano-convex cylindrical lens, an achromatic cylindrical lens, and a cylindrical-mirror-focused scanner. ZEMAX optical modeling analysis showed that mirror-focused scanning provides better Strehl ratio in comparison to plano-convex cylindrical-lens-focused scanning, and that the Strehl ratio is comparable to achromatic cylindrical-lens-focused scanning. However, field analysis on the edges of the scanning elements within the scan range shows that mirror-focused scanning is more robust when compared to a cylindrical achromatic lens. Overall, a mirror-focused scanner shows better performance compared to lenses.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.495
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.243
Teacher spread0.221 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations8
Published2010
Admission routes1
Has abstractyes

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