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Record W2031282806 · doi:10.1364/ol.38.002711

Multifocus optical-resolution photoacoustic microscopy using stimulated Raman scattering and chromatic aberration

2013· article· en· W2031282806 on OpenAlexafffund
Parsin Haji Reza, Alexander Forbrich, Roger J. Zemp

Bibliographic record

VenueOptics Letters · 2013
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaTerry Fox FoundationAlberta Innovates - Technology Futures
KeywordsOpticsChromatic aberrationMaterials scienceLens (geology)WavelengthLaserMicroscopyDepth of focus (tectonics)Focal lengthRaman scatteringNumerical apertureRaman spectroscopyChromatic scalePhysics

Abstract

fetched live from OpenAlex

In this Letter, multifocus optical-resolution photoacoustic microscopy is demonstrated using wavelength tuning and chromatic aberration for depth scanning. Discrete focal zones at several depth locations were created by refocusing light from a polarization-maintaining single-mode fiber pumped by a nanosecond fiber laser. The fiber and laser parameters were chosen to take advantage of stimulated Raman scattering (SRS) in the fiber to create a multiwavelength output that could then be bandpass filtered. The collimator lens and objective lens are chosen to take advantage of chromatic aberration in which each generated SRS wavelength peak focuses at a slightly different depth. The maximum amplitude of photoacoustic signals is mapped to form C-scan images. Additionally, all wavelength peaks fired simultaneously offers improved depth-of-field structural imaging at the cost of slight degradation of mainlobe-to-sidelobe ratios. Wavelength-tuned depth scanning over more than 440 μm is demonstrated, significantly greater than the ~100 μm depth of field predicted from our focused Gaussian beams. The improved depth of focus could be valuable for structural imaging of microvascular morphology without the need for mechanical scanning in the depth direction.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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 designSimulation or modeling
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

Citations49
Published2013
Admission routes2
Has abstractyes

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