Viscoelastic characterization of elliptical mechanical heterogeneities using a semi-analytical shear-wave scattering model for elastometry measures
Bibliographic record
Abstract
This paper presents a semi-analytical model of shear wave scattering by a viscoelastic elliptical structure embedded in a viscoelastic medium, and its application in the context of dynamic elastography imaging. The commonly used assumption of mechanical homogeneity in the inversion process is removed introducing a priori geometrical information to model physical interactions of plane shear waves with the confined mechanical heterogeneity. Theoretical results are first validated using the finite element method for various mechanical configurations and incidence angles. Secondly, an inverse problem is formulated to assess viscoelastic parameters of both the elliptic inclusion and its surrounding medium, and applied in vitro to characterize mechanical properties of agar-gelatin phantoms. The robustness of the proposed inversion method is then assessed under various noise conditions, biased geometrical parameters and compared to direct inversion, phase gradient and time-of-flight methods. The proposed elastometry method appears reliable in the context of estimating confined lesion viscoelastic parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".