Dynamic quantitative ultrasound imaging of mimicked breast lesions during shear wave propagation to emphasize differences in tissue statistical backscatter properties
Bibliographic record
Abstract
The main motivation in this study was to increase the accuracy of the breast tissue characterization by combining quantitative ultrasound (QUS) with ultrasound (US) dynamic elastography. To demonstrate that, an agar-gelatin breast mimicking phantom with two inclusions containing the same density of agar (US scatterers) but different proportions of gelatin corresponding to different mechanical properties was made. Transient plane shear waves (SW) at 200 Hz were transmitted through the phantom while the displacement of scatterers was imaged at 5 MHz with an ultrafast imaging technique. With segmented inclusions, the reciprocal (beta parameter) of the effective density of scatterers of a general distribution model of the echo envelope and its normalized range (normalized by the mean of beta during SW propagation) were estimated for each inclusion. The results showed that the relative difference of beta between the surrounding medium and both inclusions A and B were 55.6% (A) and 0.9% (B), respectively, whereas differences (in %) of the beta normalized range were 46.2% (A) and 52.6% (B), respectively. The static value beta failed to distinguish inclusion B from the surrounding; however, the dynamic range of beta succeeded in that task for the two inclusions. Thus, dynamic QUS might add information to QUS in a static framework.
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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 | 0.000 | 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) | 0.000 | 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".