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Record W2034590609 · doi:10.1117/12.841828

Spatial resolution of ultrasound-modulated optical tomography used for the detection of absorbing and scattering objects in thick scattering media

2010· article· en· W2034590609 on OpenAlexaff
Guy Rousseau, Alain Blouin, Jean‐Pierre Monchalin

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsOpticsMaterials scienceScatteringOptical tomographyInterferometryAbsorption (acoustics)Image resolutionLight scatteringTomographyPhysics

Abstract

fetched live from OpenAlex

Ultrasound-modulated optical tomography (UOT) combines the spatial resolution of ultrasonic waves and the spectroscopic properties of light to detect optically absorbing and/or scattering objects in highly scattering media. In this work, a double-pass confocal Fabry-Perot interferometer is used as a bandpass filter to selectively detect the ultrasoundtagged photons. The limited etendue of the confocal Fabry-Perot interferometer is compensated by using a singlefrequency laser emitting high-peak-power optical pulses. Compared to photoacoustic tomography, UOT is not only sensitive to optical absorption but also to scattering properties. In this paper, we consider the detection of absorbing and scattering objects embedded in thick (30 to 60 mm) tissue-mimicking phantoms and biological tissues. The experimental evaluation of the spatial resolution of the technique is compared to that expected from the ultrasonic beam intensity profile. Preliminary results indicate that the edge spread function is influenced by the level of absorption of the embedded object.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
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 head0.208
Teacher spread0.201 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

Explore more

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