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Record W2030229003 · doi:10.1117/1.3491126

Photoacoustic and high-frequency power Doppler ultrasound biomicroscopy: a comparative study

2010· article· en· W2030229003 on OpenAlexafffund
Yan Jiang, Tyler Harrison, Janaka C. Ranasinghesagara, Roger J. Zemp

Bibliographic record

VenueJournal of Biomedical Optics · 2010
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotoacoustic Doppler effectDoppler effectUltrasoundOpticsMaterials scienceBlood flowUltrasonic sensorTransducerLaserImaging phantomBiomedical engineeringLaser Doppler velocimetryPhotoacoustic imaging in biomedicinePhotoacoustic spectroscopyPhotoacoustic effectAcousticsPhysicsMedicineRadiology

Abstract

fetched live from OpenAlex

Both photoacoustic imaging and power Doppler ultrasound are capable of producing images of the vasculature of living subjects, however, the contrast mechanisms of the two modalities are very different. We present a quantitative and objective comparison of the two methods using phantom data, highlighting relative merits and shortcomings. An imaging system for combined photoacoustic and high-frequency power Doppler ultrasound microscopy is presented. This system uses a swept-scan 25-MHz ultrasound transducer with confocal dark-field laser illumination optics. A pulse-sequencer enables ultrasonic and laser pulses to be interlaced so that photoacoustic and power Doppler ultrasound images can be coregistered. Experiments are performed on flow phantoms with various combinations of vessel size, flow velocity, and optical wavelength. For the task of blood volume detection, power Doppler is seen to be advantageous for large vessels and high flow speeds. For small vessels with low flow speeds, photoacoustic imaging is seen to be more effective than power Doppler at the detection of blood as quantified by receiver operating characteristic analysis. A combination of the two modes could provide improved estimates of fractional blood volume in comparison with either mode used alone.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.686

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 integrity0.0000.001
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 head0.007
GPT teacher head0.247
Teacher spread0.239 · 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

Citations12
Published2010
Admission routes2
Has abstractyes

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