Computational models of distributed aberration in ultrasound breast imaging
Bibliographic record
Abstract
Two methods for simulation of ultrasound wavefront distortion are introduced and compared with aberration produced in simulations using digitized breast tissue specimens and a conventional multiple time-shift screen model. In the first method, aberrators are generated using a computational model of breast anatomy. In the second method, 10 to 12 irregularly shaped, strongly scattering inclusions are superimposed on the multiple-screen model to create a screen-inclusion model. Linear 2-D propagation of a 7.5-MHz planar, pulsed wavefront through each aberrator is computed using a first-order k-space method. The anatomical and screen-inclusion models reproduce two characteristics of arrival-time fluctuations observed in simulations using the digitized specimens that are not represented in simulations using the multiple-screen model: non-Gaussian first-order statistics and sharp changes in the rms arrival-time fluctuation as a function of propagation distance. The anatomical and screen-inclusion models both produce energy- level fluctuations similar to the digitized specimens, but the anatomical model more closely matches the pulse-shape distortion produced by the specimens. Both aberration models can readily be extended to 3-D, and the screen-inclusion model has the advantage of simplicity of implementation. Both models should enable more rigorous evaluation of adaptive focusing algorithms than is possible using conventional time-shift screen models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".