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Record W1967953277 · doi:10.1117/1.jbo.18.2.026001

In vivo dynamic process imaging using real-time optical-resolution photoacoustic microscopy

2013· article· en· W1967953277 on OpenAlexafffund
Wei Shi, Peng Shao, Parsin Haji Reza, Alexander Forbrich, Roger J. Zemp

Bibliographic record

VenueJournal of Biomedical Optics · 2013
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesCanadian Federation of University WomenUniversity of AlbertaCancer Research Institute
KeywordsPhotoacoustic imaging in biomedicineMicroscopyOpticsMaterials scienceResolution (logic)Photoacoustic spectroscopyProcess (computing)Optical microscopeOptical imagingPhotoacoustic effectPreclinical imagingImage resolutionBiomedical engineeringIn vivoComputer scienceScanning electron microscopeArtificial intelligencePhysicsMedicine

Abstract

fetched live from OpenAlex

The authors demonstrate in vivo dynamic process imaging using a label-free real-time optical-resolution photoacoustic microscope (OR-PAM). This reflection-mode system takes advantage of a 532-nm fiber laser source with a high pulse repetition rate of up to 600 kHz combined with a fast-scanning mirror system. Microvasculature in SCID mouse ears is imaged at near real-time (0.5 fps) for a 1×1 mm2 field of view (FOV) with micron-scale lateral resolution. We also demonstrate imaging of cardiac-induced microhemodynamics in murine microvasculature at real-time frame-rates (30 fps) over a 250×250 μm2 FOV using real-time C-scan OR-PAM with ability to provide sustained imaging with near real-time feedback for focusing and positioning.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.249
Teacher spread0.245 · 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 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

Citations25
Published2013
Admission routes2
Has abstractyes

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