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Record W2059441307 · doi:10.1364/oe.17.022041

Combined photoacoustic and ultrasound biomicroscopy

2009· article· en· W2059441307 on OpenAlexaff
Tyler Harrison, Janaka C. Ranasinghesagara, Huihong Lu, Kory W. Mathewson, Andrew Walsh, Roger J. Zemp

Bibliographic record

VenueOptics Express · 2009
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhotoacoustic Doppler effectUltrasoundPhotoacoustic imaging in biomedicineSpeckle patternMaterials scienceOpticsPhotoacoustic effectTransducerLaserBiomedical engineeringUltrasonic sensorContext (archaeology)Blood flowAcousticsMedicineRadiologyPhysics

Abstract

fetched live from OpenAlex

We report on the development of an imaging system capable of combined ultrasound and photoacoustic imaging based on a fast-scanning single-element 25-MHz ultrasound transducer and a unique light-delivery system. The system is capable of 20 ultrasound frames per second and slower photoacoustic frame rates limited by laser pulse-repetition rates. Laser and ultrasound pulses are interlaced for co-registration of photoacoustic and ultrasound images. In vivo imaging of a human finger permits ultrasonic visualization of vessel structures and speckle changes indicative of blood flow, while overlaid photoacoustic images highlight some small vessels that are not clear from the ultrasound scan. Photoacoustic images provide optical absorption contrast co-registered in the structural and blood-flow context of ultrasound with high-spatial resolution and may prove important for clinical diagnostics and basic science of the microvasculature.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.210
Teacher spread0.205 · 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

Citations92
Published2009
Admission routes1
Has abstractyes

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