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Record W2011909192 · doi:10.1364/boe.3.000972

Feasibility of optical coherence elastography measurements of shear wave propagation in homogeneous tissue equivalent phantoms

2012· article· en· W2011909192 on OpenAlexafffund
Marjan Razani, Adrian Mariampillai, Cuiru Sun, Timothy W. H. Luk, Victor X. D. Yang, Michael C. Kolios

Bibliographic record

VenueBiomedical Optics Express · 2012
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAcoustic radiation forceOptical coherence tomographyElastographyMaterials scienceOpticsShear modulusTransducerShear wavesShear (geology)Imaging phantomWave propagationAcousticsPhysicsUltrasoundComposite material

Abstract

fetched live from OpenAlex

In this work, we explored the potential of measuring shear wave propagation using optical coherence elastography (OCE) based on a swept-source optical coherence tomography (OCT) system. Shear waves were generated using a 20 MHz piezoelectric transducer (circular element 8.5 mm diameter) transmitting sine-wave bursts of 400 μs, synchronized with the OCT swept source wavelength sweep. The acoustic radiation force (ARF) was applied to two gelatin phantoms (differing in gelatin concentration by weight, 8% vs. 14%). Differential OCT phase maps, measured with and without the ARF, demonstrate microscopic displacement generated by shear wave propagation in these phantoms of different stiffness. We present preliminary results of OCT derived shear wave propagation velocity and modulus, and compare these results to rheometer measurements. The results demonstrate the feasibility of shear wave OCE (SW-OCE) for high-resolution microscopic homogeneous tissue mechanical property characterization.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.315
Teacher spread0.257 · 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

Citations88
Published2012
Admission routes2
Has abstractyes

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