Viscoelastic characterization of an elliptic structure in dynamic elastography imaging using a semi-analytical shear wave scattering model
Bibliographic record
Abstract
In the context of dynamic elastography, the quantitative estimation of elasticity usually relies on homogeneity, linearity and isotropy assumptions. However, the presence of confined mechanical heterogeneities such as tumors, make those assumptions coarse. In this study, a semi-analytical model of shear wave scattering by elliptical structures is proposed in order to take into account physical interactions due to the presence of a mechanical heterogeneity including viscous effects. The model was validated using the finite element method as a reference in a forward problem approach. Then, an inversion method based on a least-square optimization was applied to in-vitro results obtained on agar-gelatin phantoms. Finally, the robustness of the inversion procedure was assessed considering various signal-to-noise ratios. Theoretical results were found in good agreement with the forward problem formulation. The inverse problem allowed robust viscoelastic assessments of phantom materials without any assumption on their rheological behavior.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".