Estimation of polyvinyl alcohol cryogel mechanical properties with four ultrasound elastography methods and comparison with gold standard testings
Bibliographic record
Abstract
Tissue-mimicking phantoms are very useful in the field of tissue characterization and essential in elastography for the purpose of validating motion estimators. This study is dedicated to the characterization of polyvinyl alcohol cryogel (PVA-C) for these types of applications. A strict fabrication procedure was defined to optimize the reproducibility of phantoms having a similar elasticity. Following mechanical stretching tests, the phantoms were used to compare the accuracy of four different elastography methods. The four methods were based on a one-dimensional (1-D) scaling factor estimation, on two different implementations of a 2-D Lagrangian speckle model estimator (quasistatic elastography methods), and on a 1-D shear wave transient elastography technique (dynamic method). Young's modulus was investigated as a function of the number of freeze-thaw cycles of PVA-C, and of the concentration of acoustic scatterers. Other mechanical and acoustic parameters-such as the speed of sound, shear wave velocity, mass density, and Poisson's ratio-also were assessed. The Poisson's ratio was estimated with good precision at 0.499 for all samples, and the Young's moduli varied in a range of 20 kPa for one freeze-thaw cycle to 600 kPa for 10 cycles. Nevertheless, above six freeze-thaw cycles, the results were less reliable because of sample geometry artifacts. However, for the samples that underwent less than seven freeze-thaw cycles, the Young's moduli estimated with the four elastography methods showed good matching with the mechanical tensile tests with a regression coefficient varying from 0.97 to 1.07, and correlations R2 varying from 0.93 to 0.99, depending on the method.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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 | 0.000 |
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".