MétaCan
Menu
Back to cohort
Record W2344816689 · doi:10.1117/12.2213655

Coherence gated wavefront sensorless adaptive optics for two photon excited fluorescence retinal imaging(Conference Presentation)

2016· article· en· W2344816689 on OpenAlexaff
Yifan Jian, Michelle Cua, Stefano Bonora, Edward N. Pugh, Robert J. Zawadzki, Marinko V. Šarunic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAdaptive opticsOpticsWavefrontPhysicsDeformable mirrorWavefront sensorOptical coherence tomographyZernike polynomialsGuide starComputer science

Abstract

fetched live from OpenAlex

We present a novel system for adaptive optics two photon imaging. We utilize the bandwidth of the femtosecond excitation beam to perform coherence gated imaging (OCT) of the sample. The location of the focus is directly observable in the cross sectional OCT images, and adjusted to the desired depth plane. Next, using real time volumetric OCT, we perform Wavefront Sensorless Adaptive Optics (WSAO) aberration correction using a multi-element adaptive lens capable of correcting up to 4th order Zernike polynomials. The aberration correction is performed based on an image quality metric, for example intensity. The optimization time is limited only by the OCT acquisition rate, and takes ~30s. Following aberration correction, two photon fluorescence images are acquired, and compared to results without adaptive optics correction. This technique is promising for multiphoton imaging in multi-layered, scattering samples such as eye and brain, in which traditional wavefront sensing and guide-star sensorless adaptive optics approaches may not be suitable.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.260
Teacher spread0.238 · 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 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicOptical Coherence Tomography ApplicationsFrench-language works237,207