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Record W4391247464 · doi:10.1117/12.3005753

Numerical correction of bi-directional scanning distortion in FDML OCT

2024· article· en· W4391247464 on OpenAlexaff
Mohammad Shahidul Islam, Yusi Miao, Myeong Jin Ju

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOptical coherence tomographyOversamplingGalvanometerOpticsDistortion (music)AmplitudeComputer sciencePhase (matter)Coherence (philosophical gambling strategy)LaserPhysicsBandwidth (computing)Telecommunications

Abstract

fetched live from OpenAlex

Optical Coherence Tomography (OCT) is a non-invasive imaging technique, essential in medical diagnostics due to its ability to produce high-resolution images of internal structures of biological tissues. One of the unique features of the FDML based MHz-OCT is the optical buffering that increases the A-scan rate by creating successive time-delayed copies of the original sweep. However, due to the optical buffering, numerous studies have reported that A-lines originates from different buffer can have different amplitude and phase. Another challenge associated with high A-scan laser source is to pair with the high-speed mechanical scanning protocol to avoid oversampling. Most of the FDML based OCT system is oversampled due to the mechanical limitation of the galvanometer. In this paper, an optimization method is applied to the backward scanning data to eliminate the distortions. Moreover, the phase and amplitude misalignment issues are also numerically corrected. The amplitude inconsistencies in the acquired interferogram are also addressed and solved.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score0.243

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 science0.0000.000
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 head0.008
GPT teacher head0.237
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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