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Record W2035387697 · doi:10.1109/tuffc.2014.6722613

Rheological assessment of a polymeric spherical structure using a three-dimensional shear wave scattering model in dynamic spectroscopy elastography

2014· article· en· W2035387697 on OpenAlexaff
Emmanuel Montagnon, Anis Hadj-Henni, Cédric Schmitt, Guy Cloutier

Bibliographic record

VenueIEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2014
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsViscoelasticityRheologyShear modulusAcoustic radiation forceElastographyMaterials scienceMagnetic resonance elastographyShear (geology)SPHERESInverse problemDynamic modulusAcousticsMechanicsPhysicsOpticsMathematical analysisMathematicsDynamic mechanical analysisUltrasoundComposite material

Abstract

fetched live from OpenAlex

With the purpose of assessing localized rheological behavior of pathological tissues using ultrasound dynamic elastography, an analytical shear wave scattering model was used in an inverse problem framework. The proposed method was adopted to estimate the complex shear modulus of viscoelastic spheres from 200 to 450 Hz. The inverse problem was formulated and solved in the frequency domain, allowing assessment of the complex viscoelastic shear modulus at discrete frequencies. A representative rheological model of the spherical obstacle was determined by comparing storage and loss modulus behaviors with Kelvin-Voigt, Maxwell, Zener, and Jeffrey models. The proposed inversion method was validated by using an external vibrating source and acoustic radiation force. The estimation of viscoelastic properties of three-dimensional spheres made softer or harder than surrounding tissues did not require a priori rheological assumptions. The proposed method is intended to be applied in the context of breast cancer imaging.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.256
Teacher spread0.245 · 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.

Study designSimulation or modeling
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

Citations17
Published2014
Admission routes1
Has abstractyes

Explore more

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