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Record W2949317765 · doi:10.7567/1347-4065/ab17cd

Measurement of shear wave absorption with correction of the diffraction effect for viscoelasticity characterization of soft tissues

2019· article· en· W2949317765 on OpenAlexafffund
Zhen Qu, Yuu Ono

Bibliographic record

VenueJapanese Journal of Applied Physics · 2019
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDiffractionViscoelasticitySpeckle patternImaging phantomMaterials scienceOpticsElasticity (physics)AcousticsAttenuation coefficientPhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract An ultrasonic method for measuring shear wave (SW) propagation properties in soft tissues was investigated for viscoelasticity characterization. A continuous SW was induced into a specimen by an external vibration source. The displacements within the specimen due to the SW propagation were measured by a speckle-echo tracking method using a conventional B-mode scan of focused ultrasound. SW velocity and absorption coefficient of the specimen were retrieved from the measured SW waveforms with the compensation of the SW diffraction. The effect of positioning error of the ultrasound probe on the diffraction calculation was studied by numerical simulations. It was shown that the estimation error of the SW absorption could be reduced by selecting an appropriate SW measurement region where the diffraction estimation precision is less sensitive to the positioning error of the probe. Finally, the shear elasticity and viscosity of a soft tissue mimicking phantom were estimated.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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