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Record W2071686453 · doi:10.1117/12.909157

In vivo combined photoacoustic and Doppler ultrasound imaging

2012· article· en· W2071686453 on OpenAlexaff
Yan Jiang, Tyler Harrison, Alexander Forbrich, Roger J. Zemp

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhotoacoustic Doppler effectDoppler effectPhotoacoustic imaging in biomedicineBlood flowUltrasoundMaterials scienceBiomedical engineeringPhotoacoustic spectroscopyFlow velocityOxygen saturationIn vivoLaser Doppler velocimetryDoppler ultrasoundDoppler imagingOpticsOxygenFlow (mathematics)AcousticsPhysicsRadiologyMedicine

Abstract

fetched live from OpenAlex

The long-term goal of our research is to develop photoacoustic and Doppler ultrasound imaging methods for noninvasive estimation of the oxygen consumption rate (MRO2) in vivo. Previously, we have demonstrated a combined photoacoustic and high-frequency Doppler ultrasound system and shown the feasibility of flow velocity and oxygen saturation (sO2) estimation using double-ink flow phantoms. In this work, the results of in vitro sheep blood experiments are presented. Blood oxygen flux has been estimated at different sO2 levels and mean flow speeds, and the uncertainty of the measurement has been quantified. In vivo experiments have been performed on Swiss Webster mice to provide coregistered photoacoustic and Doppler flow images with imaging depths of ~2mm. Doppler bandwidth broadening technique has been used to obtain transverse flow velocity. The diameter of the blood vessel is ~500μm and the mean flow speed is 15cm/s. We are working towards sO2 estimation in vivo and 3D oxygen consumption imaging of tumors at depths beyond OR-PAM.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.211
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 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicPhotoacoustic and Ultrasonic ImagingFrench-language works237,207