Estimation of the Scatterer Size Distributions in Quantitative\n Ultrasound Using Constrained Optimization
Bibliographic record
Abstract
Quantitative ultrasound (QUS) parameters such as the effective scatterer\ndiameter (ESD) reveal tissue properties by analyzing ultrasound backscattered\necho signal. ESD can be attained through parametrizing backscatter coefficient\nusing form factor models. However, reporting a single scatterer size cannot\naccurately characterize a tissue, particularly when the media contains\nscattering sources with a broad range of sizes. Here we estimate the\nprobability of contribution of each scatterer size by modeling the measured\nform factor as a linear combination of form factors from individual sacatterer\nsizes. We perform the estimation using two novel techniques. In the first\ntechnique, we cast scatterer size distribution as an optimization problem, and\nefficiently solve it using a linear system of equations. In the second\ntechnique, we use the solution of this system of equations to constrain the\noptimization function, and solve the constrained problem. The methods are\nevaluated in simulated backscattered coefficients using Faran theory. We\nevaluate the robustness of the proposed techniques by adding Gaussian noise.\nThe results show that both methods can accurately estimate the scatterer size\ndistribution, and that the second method outperforms the first one.\n
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".