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Record W2995816902 · doi:10.1364/oe.27.038435

Single-scan interferometric second harmonic generation microscopy using a kHz phase-scanner

2019· article· en· W2995816902 on OpenAlexafffund
Maxime Pinsard, Lee-Pierre Belley, Jean-Marc Piau, C. Y. Côté, Heide Ibrahim, François Légaré

Bibliographic record

VenueOptics Express · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsAxis Photonique (Canada)Institut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsOpticsInterferometrySecond-harmonic imaging microscopySIGNAL (programming language)Materials sciencePhase (matter)Second-harmonic generationPixelScannerMicroscopyLaser scanningImage resolutionImage sensorPhase modulationLaserPhysicsPhase noiseComputer science

Abstract

fetched live from OpenAlex

In conventional laser-scanning microscopy, images are formed by acquiring the signal from pixel to pixel. Here, we report more than one order of magnitude reduction in acquisition time of Interferometric Second Harmonic Generation (I-SHG) by scanning the phase within each pixel, to characterize the relative polarity of various samples. Using an electro-optic phase-scanner, we show that the phase-shift patterns required for interferometry can be applied at each pixel during the scanning of the sample, allowing single-scan I-SHG (1S-ISHG) measurements. Requiring exposure times comparable to standard SHG intensity images, the additional phase information of the signal can thus be retrieved in parallel to its amplitude at the time-scale of seconds. Moreover, slower modulations can be used to enhance the precision of the phase measurement, without any spatial or temporal shift between interferograms, in contrast to conventional frame phase-shifting I-SHG (standard I-SHG). This continues to extend I-SHG to dynamical processes, and opens it to large-scale studies, as well as to imaging samples where the signal-to-noise ratio is an issue.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
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
Bibliometrics0.0000.000
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 head0.026
GPT teacher head0.309
Teacher spread0.283 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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