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Record W2906430788 · doi:10.1117/1.jbo.23.12.126503

Optimization of frequency-doubled Er-doped fiber laser for miniature multiphoton endoscopy

2018· article· en· W2906430788 on OpenAlexafffund
Lin Huang, Xin Zhou, Shuo Tang

Bibliographic record

VenueJournal of Biomedical Optics · 2018
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCalifornia HIV/AIDS Research Program
KeywordsMaterials scienceFiber laserOpticsLaserOptical fiberFemtosecondDispersion-shifted fiberPhotonic-crystal fiberOptoelectronicsFiberWavelengthFiber optic sensor

Abstract

fetched live from OpenAlex

Frequency-doubled femtosecond Er-doped fiber laser is a low-cost and portable excitation source suitable for multiphoton endoscopy. The frequency-doubled wavelength at 780 nm is used to excite the intrinsic fluorescence signal. The frequency-doubling with a periodically poled MgO : LiNbO3 (PPLN) is integrated in the distal end of the imaging head to achieve fiber connection. The imaging speed is further improved by optimizing the excitation laser source. A 0.3-mm length of PPLN crystal is selected and the Er-doped fiber laser is manipulated to match its bandwidth with the acceptance bandwidth of the PPLN. Through this optimization, a reduced pulsewidth of 80 fs of the frequency-doubled pulse is achieved. All-fiber dispersion compensation and pulse compression by single mode fiber is conducted, which makes the fiber laser directly fiber-coupled to the imaging head. An imaging speed of 4 frames / s is demonstrated on ex vivo imaging of unstained biological tissues, which is 10 times faster than our previous study using a 1-mm-long PPLN. The results show that miniature multiphoton endoscopy using frequency-doubled Er-doped fiber laser has great potential for clinical 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)0.001
metaresearch head score (Gemma)0.001
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 score0.413
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.336
Teacher spread0.316 · 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

Citations19
Published2018
Admission routes2
Has abstractyes

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