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Record W2061680707 · doi:10.1002/jbio.201100138

Multimodal optical imaging with multiphoton microscopy and optical coherence tomography

2012· review· en· W2061680707 on OpenAlexafffund
Shuo Tang, Yifeng Zhou, Myeong Jin Ju

Bibliographic record

VenueJournal of Biophotonics · 2012
Typereview
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOptical coherence tomographyMicroscopyOpticsMultiphoton fluorescence microscopeCoherence (philosophical gambling strategy)Fluorescence-lifetime imaging microscopyFluorescence microscopeBiomedical engineeringOptical imagingMaterials scienceComputer sciencePhysicsFluorescenceMedicine

Abstract

fetched live from OpenAlex

Two types of combined multiphoton microscopy and optical coherence tomography (MPM/OCT) are compared for multimodal optical imaging. Single-scale multiphoton microscopy and optical coherence microscopy (MPM/OCM) is shown to acquire multiple contrasts from MPM and OCT simultaneously. Multi-scale MPM/OCT is shown to provide multiple field-of-views (FOVs), where OCT provides tissue level imaging and MPM provides cellular level imaging. In both types, the MPM includes two channels which are two-photon excited fluorescence (TPEF) and second harmonic generation (SHG). Representative images using each system are demonstrated on biological specimens. A detailed comparison of the two types of MPM/OCT shows that each system has its own pros and cons. MPM/OCM is high-resolution but with limited FOV, and OCM may or may not provide additional information than MPM depending on the samples. Multi-scale MPM/OCT can change FOV but need both low and high NA objectives. For future development, the two types of MPM/OCT can be further integrated to achieve both functions on a single system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2012
Admission routes2
Has abstractyes

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