Micro-vibrations underlying temporal enhanced ultrasound: The effect of scatterer size and elasticity
Bibliographic record
Abstract
Temporal-enhanced ultrasound (TeUS) imaging is a recently developed method based on the analysis of US time series. In this report, we show experimentally that the TeUS amplitude is sensitive to scatterer size and their density, as well as to the microvibration amplitude. Since the microvibration amplitude depends on the local elasticity of the medium, TeUS is able to differentiate tissues based on their elasticity, in addition to scatterer density and cross section. It is argued that interference effects, i.e., US speckle, are enhancing the TeUS effect. In this study, ultrasound phantoms were designed to mimic tissues with three different viscoelasticities and three different scatterer sizes. Into each of the nine phantoms, a flexible tubing was embedded and used to generate local microvibrations at 1 Hz and with an amplitude below 20 μm. Time series of raw ultrasound images were analyzed to extract the B-mode intensity, the scatterer displacement, and the shear modulus at a variety of different microvibration amplitudes.
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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 | 0.000 | 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".