Dynamic micro-elastography applied to the viscoelastic characterization of a mimicking artery and a porcine aorta
Bibliographic record
Abstract
Because early signs of most cardiovascular diseases involve hardening of arteries, the development of non-invasive methods to provide in vivo assessment of mechanical properties of vessel walls could be of great importance in clinical practice. Most important limitations of methods proposed so far are the investigation of only the mean longitudinal wall elasticity parameters along a vessel segment in a restricted frequency range below 500 Hz. We propose to adapt the dynamic microelastography method to study the radial viscoelasticity of thinwalled cylindrical geometry phantoms. The technique firstly implies the generation of a low frequency (300–600 Hz) plane transient shear wave in the vascular phantom and the tracking of this wave with an ultrasound biomicroscope (Vevo 770, Visualsonics) providing in post-processing a very high frame rate (16000 images per second). An inverse problem was formulated as a least-square minimization between analytical simulations and experimental measurements to retrieve storage (G′) and loss (G″) moduli as functions of the shearing frequency. Result on a 3-mm wall mimicking artery permitted to validate the feasibility and the reliability of the inverse problem formulation. Then, G′ and G″ of a porcine aorta showed that both parameters are strongly dependant on frequency increasing, allowing to assume that such a biological tissue is mechanically governed by complex viscoelastic laws.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".