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Record W2048908637 · doi:10.1117/12.728400

Design criteria in choosing optimized OCT scanning regimes

2007· article· en· W2048908637 on OpenAlexaff
Carla C. Rosa, John A. Rogers, Justin Pedro, Richard B. Rosen, Adrian Podoleanu

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsNanoacademic Technologies
Fundersnot available
KeywordsPixelOptical coherence tomographyComputer scienceArtificial intelligenceFace (sociological concept)3d scanningComputer visionOpticsPhysics

Abstract

fetched live from OpenAlex

A comparative analysis on the performance of different scanning regimes in time domain optical coherence tomography is presented in terms of image size. Safety thresholds due to the different continuous irradiation time per transverse pixel in different scanning regimes are also considered. We present the maximum exposure level for a variety of scanning procedures, employing either A scanning (depth priority) or T scanning (transverse priority) when generating cross section images, en-face images or collecting 3D volumes. We present a comparison between such B-scan images, and different criteria to allow the user to choose the right mode of operation. Mainly, two criteria are detailed, a scanning criterion and a safety criterion. The scanning criterion depends on the number of pixels along the lateral and axial directions. The analysis shows that en-face scanning allows wider images while the longitudinal scanning is more suitable to deep cross sections. The safety criterion refers to safety levels to be observed in each scanning mode. We show that the flying spot OCT imaging has different safety limits for T- and A- based imaging modes. The analysis leads to maximum permissible optical power levels that favors T-scan imaging of wide objects. We then apply the analysis considering as object the eye.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.020
GPT teacher head0.269
Teacher spread0.249 · 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
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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicRetinal and Macular SurgeryFrench-language works237,207