MétaCan
Menu
Back to cohort
Record W2962345832 · doi:10.1364/boe.10.003938

Elimination of imaging artifacts in second harmonic generation microscopy using interferometry

2019· article· en· W2962345832 on OpenAlexafffund
Maxime Pinsard, Margaux Schmeltz, Jarno N. van der Kolk, Shunmoogum A. Patten, Heide Ibrahim, Lora Ramunno, Marie‐Claire Schanne‐Klein, François Légaré

Bibliographic record

VenueBiomedical Optics Express · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsArmand Frappier MuseumWilfrid Laurier UniversityUniversity of OttawaInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Paris-SaclayCanada Foundation for Innovation
KeywordsOpticsMicroscopyInterferometrySecond-harmonic generationPhase imagingMedical imagingImage processingMultiphoton fluorescence microscopeInterference microscopyMaterials scienceComputer scienceFluorescence microscopePhysicsComputer visionArtificial intelligenceLaserFluorescenceImage (mathematics)

Abstract

fetched live from OpenAlex

Conventional second harmonic generation (SHG) microscopy might not clearly reveal the structure of complex samples if the interference between all scatterers in the focal volume results in artefactual patterns. We report here the use of interferometric second harmonic generation (I-SHG) microscopy to efficiently remove these artifacts from SHG images. Interfaces between two regions of opposite polarity are considered because they are known to produce imaging artifacts in muscle for instance. As a model system, such interfaces are first studied in periodically-poled lithium niobate (PPLN), where an artefactual incoherent SH signal is obtained because of irregularities at the interfaces, that overshadow the sought-after coherent contribution. Using I-SHG allows to remove the incoherent part completely without any spatial filtering. Second, I-SHG is also proven to resolve the double-band pattern expected in muscle where standard SHG exhibits in some regions artefactual single-band patterns. In addition to removing the artifacts at the interfaces between antiparallel domains in both structures (PPLN and muscle), I-SHG also increases their visibility by up to a factor of 5. This demonstrates that I-SHG is a powerful technique to image biological samples at enhanced contrast while suppressing artifacts.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.535

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 head0.013
GPT teacher head0.302
Teacher spread0.288 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueBiomedical Optics ExpressSame topicAdvanced Fluorescence Microscopy TechniquesFrench-language works237,207