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Record W2472977564 · doi:10.1117/1.jbo.21.6.066018

Microwave-heating-coupled photoacoustic radar for tissue diagnostic imaging

2016· article· en· W2472977564 on OpenAlexafffund
Wei Wang, Andreas Mandelis

Bibliographic record

VenueJournal of Biomedical Optics · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Toronto
FundersSamsungNatural Sciences and Engineering Research Council of CanadaSamsung Advanced Institute of Technology
KeywordsPhotoacoustic imaging in biomedicineMaterials scienceMicrowaveEx vivoPhotoacoustic spectroscopyOpticsMicrowave imagingThermocoupleBiomedical engineeringNuclear magnetic resonanceIn vivoPhysicsMedicine

Abstract

fetched live from OpenAlex

An investigation of microwave (MW) heating effects on biotissue for enhancing photoacoustic radar (PAR) signals was conducted. Localized tissue heating generated by MWs was used to improve PAR imaging depth and signal-to-noise ratio (SNR). Elevated temperatures were measured with thermocouples in ex vivo bovine muscle. The measured temperature rise on the heated spot surface by MWs was in agreement with theoretical predictions. The study showed localized MW heating can increase the photoacoustic imaging depth by 11%, and the SNR by 5% in ex vivo bovine muscle.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.889
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

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 head0.006
GPT teacher head0.227
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 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

Citations5
Published2016
Admission routes2
Has abstractyes

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