Coupling quantitative ultrasound using echo envelop statistics and shear wave propagation to provide new image contrast of mimicked liver lesions
Bibliographic record
Abstract
In quantitative ultrasound, biomarkers are sensitive to the scatterers’ positioning and number density, and therefore, shear wave (SW) propagation may modify their time-varying behaviors and provide new image contrast. Herein, we considered echo envelop statistics by using one parameter of the homodyned-K distribution (HKD): the diffuse-to-total signal power ratio 1/(κ+1). A liver lesion-mimicking phantom was simulated with an inclusion and a background of elastic moduli of 40 and 4 kPa, and scatterers’ number densities per resolution cell of 15 and 5, respectively. Ten sets of spatially correlated scatterers were created, and a plane SW was simulated to reposition time and spatially varying scatterers according to the SW motion. Ultrasound images were simulated using MUST with added Gaussian noise to attain 20-dB SNR. Dynamic and static analyses were made over 30 frames with and without SW motion, respectively. Validation was based on the contrast of 1/(κ+1) between the inclusion and the background. HKD image contrast increased with SW motion with values from 0.12 ± 0.10 to 0.48 ± 0.16 (no units) (p < 0.001). Results suggest that the variation in scatterers’ organization under SW propagation may provide new information over its static counterpart to enhance the contrast of liver lesions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".