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Record W1993334624 · doi:10.1117/1.3200922

Photoacoustic technique for assessing optical scattering properties of turbid media

2009· letter· en· W1993334624 on OpenAlexaff
Janaka C. Ranasinghesagara, Jian Yan, Xuhui Chen, Kory W. Mathewson, Roger J. Zemp

Bibliographic record

VenueJournal of Biomedical Optics · 2009
Typeletter
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOpticsMaterials sciencePhotoacoustic imaging in biomedicineAttenuation coefficientScatteringPhotoacoustic effectLight scatteringAbsorption (acoustics)Photoacoustic spectroscopyLaserFluencePhysics

Abstract

fetched live from OpenAlex

Measurement of tissue optical properties impacts both optical diagnostic and theraputic applications. Although existing photoacoustic imaging techniques provide optical absorption contrast, we present a photoacoustic technique that demonstrates sensitivity to the optical scattering coefficient of a turbid medium. By incrementing the distance between a surface illumination spot and a subsurface absorber and measuring the photoacoustic amplitude of the absorber, we can effectively estimate the Green's function of light transport in a turbid medium. Our results for different concentrations of Intralipid indicate that this technique has the ability to distinguish small changes of the reduced scattering coefficient. It has the potential to be used for in vivo studies to obtain reduced scattering coefficients of biological tissues. These findings will potentially improve the calculation of subcutaneous fluence in photoacoustic-based techniques and laser dosimetry studies in live tissues.

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)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.018
GPT teacher head0.239
Teacher spread0.221 · 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

Citations20
Published2009
Admission routes1
Has abstractyes

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