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Record W2949127041 · doi:10.1101/406108

Aperture Phase Modulation with Adaptive Optics: A Novel Approach for Speckle Reduction and Structure Extraction in Optical Coherence Tomography

2018· preprint· en· W2949127041 on OpenAlexaff
Pengfei Zhang, Suman K. Manna, Eric B. Miller, Yifan Jian, Ratheesh K. Meleppat, Marinko V. Šarunic, Edward N. Pugh, Robert J. Zawadzki

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSimon Fraser University
FundersNational Eye InstituteUniversity of California, DavisNational Institutes of HealthNational Science Foundation
KeywordsSpeckle patternOptical coherence tomographySpeckle noiseOpticsAdaptive opticsNumerical apertureSpeckle imagingAperture (computer memory)Point spread functionPhase modulationCoherence (philosophical gambling strategy)Modulation (music)Noise (video)PhysicsPhase noiseComputer scienceArtificial intelligenceWavelengthImage (mathematics)Acoustics

Abstract

fetched live from OpenAlex

Abstract: Speckle is an inevitable consequence of the use of coherent light in optical coherence tomography (OCT), and often acts as noise that obscures micro-structures of biological tissue. We here present a novel method of suppressing speckle noise intrinsically compatible with adaptive optics (AO) in OCT system: by modulating the phase inside the imaging system pupil aperture with a segmented deformable mirror, thus producing minor perturbations in the point spread function (PSF) to create un-correlated speckle pattern between B-scans, and further averaging to wash out the speckle but maintain the structures. It is a well-controlled and universal method which can efficiently determine the optimal range of phase modulation that minimizing speckle noise while maximizing image resolution and signal strength for different systems and/or samples. As an active method, its effectiveness and efficiency were demonstrated by both ex-vivo non-biological and in-vivo biological applications.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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