High-Frequency Quantitative Ultrasound Elastography for the Mechanical Assessment of Thin Biomaterials In Vitro
Bibliographic record
Abstract
OBJECTIVE: High-frequency ultrasound elastography (USE) can measure the mechanical properties of biomaterials and engineered tissues in vitro. Previously developed USE systems have been limited by contact acoustic radiation force (ARF) excitations and insufficient spatiotemporal resolution for sub-millimetre sub-surface mechanical property measurements. METHODS: We present a novel high-frequency USE system with a highly focused (f-number 1) 15 MHz ARF excitation transducer and a broadband (f-number 3) 40 MHz ARF tracking transducer. RESULTS: When comparing shear moduli measured via USE with shear rheometry, shear moduli of 1% and 5% agar-silica phantoms estimated by USE, were 8.8 ± 2.2 kPa and 117.0 ± 12.3 kPa (8.0 ± 0.4 kPa by rheometry, p = 0.573 for 1%; 114.4 ± 7.2 kPa, p = 0.777 for 5%) and oil-agar silica phantoms were 105.0 ± 3.4 kPa (0%) and 77.0 ± 22.1 kPa (10%) by USE (101.0 ± 4.8 kPa by rheometry; p = 0.311 for 0%; 75.8 ± 5.3 kPa; p = 0.938 for 10%). The speed of sound, acoustic impedance, and acoustic attenuation of these samples were also determined. We also used in silico analysis to mimic our experimental system and analyze the spectral content of the resulting shear waves in elastic and viscoelastic tissues with parametric changes to the ARF excitation duration, shear modulus, and viscosity. Notably, we observed a nonlinear dependency of shear wave frequency on ARF excitation duration and material properties, where shear wave frequency was most sensitive to tissue elastic modulus at longer ARF durations but more sensitive to tissue viscosity at shorter ARF durations. CONCLUSION: Our system enables noninvasive, nondestructive estimation of the mechanical properties of thin biomaterials via focused axial localization of the ARF, opening new avenues for future USE applications in engineered tissue systems.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".