Viscoelastic characterization of soft tissues by Dynamic Micro-Elastography (DME) in the frequency range of 300–1500 Hz
Bibliographic record
Abstract
Mechanical characterization of living tissues and organs are of interest because information on their viscoelastic properties in the presence of diseases can affect therapy planning. This article proposes the Dynamic Micro-Elastography (DME) method to characterize elasticity and viscosity parameters from the acoustical properties (velocity and attenuation) of monochromatic and transient shear waves in a large frequency range (300 – 1500 Hz). To overcome spatial and temporal limitations of conventional systems, we used a high frequency transducer (25 MHz) and a shear wave gated strategy to reconstruct ultra fast RF frame sequences (16000 images per second). Viscoelasticity of agar-gelatin materials, porcine blood clots and porcine liver samples were investigated. As previously observed in the literature at lower frequencies, the liver tissue appeared highly viscous between 300 – 1500 Hz, whereas the gel and blood clot presented constant elasticity values over that range of frequency. A second experiment undertaken on a small animal organ (rat liver) proved that such high frequency waves, tracked with our high resolution system, permit to study its mechanical properties. To conclude, this characterization tool is adequate to investigate soft tissue rheological behavior evolution as a function of frequency. Moreover, because wavelengths of propagating shear waves are very small (≪ 2 mm at 1500 Hz) and the motion tracking system very accurate, DME could also be applied to characterize millimetric organs.
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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 | 0.001 | 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".