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Record W2528603488 · doi:10.1364/boe.7.004441

Combined frequency domain photoacoustic and ultrasound imaging for intravascular applications

2016· article· en· W2528603488 on OpenAlexafffund
Robin F. Castelino, Michael B Hynes, Chelsea E. Munding, Sergey A. Telenkov, F. Stuart Foster

Bibliographic record

VenueBiomedical Optics Express · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsIntravascular ultrasoundPhotoacoustic imaging in biomedicineUltrasound imagingUltrasoundMedical imagingOptical coherence tomographyBiomedical engineeringUltrasonic imagingRadiologyOpticsMedical physicsMedicinePhysics

Abstract

fetched live from OpenAlex

Intravascular photoacoustic (IVPA) imaging has the potential to characterize lipid-rich structures based on the optical absorption contrast of tissues. In this study, we explore frequency domain photoacoustics (FDPA) for intravascular applications. The system employed an intensity-modulated continuous wave (CW) laser diode, delivering 1W over an intensity modulated chirp frequency of 4-12MHz. We demonstrated the feasibility of this approach on an agar vessel phantom with graphite and lipid targets, imaged using a planar acoustic transducer co-aligned with an optical fibre, allowing for the co-registration of IVUS and FDPA images. A frequency domain correlation method was used for signal processing and image reconstruction. The graphite and lipid targets show an increase in FDPA signal as compared to the background of 21dB and 16dB, respectively. Use of compact CW laser diodes may provide a valuable alternative for the development of photoacoustic intravascular devices instead of pulsed laser systems.

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)0.000
metaresearch head score (Gemma)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.204
Teacher spread0.200 · 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

Citations10
Published2016
Admission routes2
Has abstractyes

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