Computational synthesis of ultrasound breast images from a three-dimensional anatomical model
Bibliographic record
Abstract
A three-dimensional breast anatomy model has been implemented using spline surfaces and fractal structures to represent the architecture of the lactiferous ducts, mammary fat lobules, skin, and supporting connective tissues. The model randomly varies user-specified structural parameters to provide an unlimited number of realizations of the gross anatomy. Cross-sectional views extracted by slicing through a realization of the model are input to a two-dimensional k-space (i.e., spatial frequency domain) ultrasound propagation simulator. The k-space simulator iterates pressure and particle velocity fields in 30-ns steps to compute scattering from the structures defined by the anatomical model and small random variations in compressibility that are added to generate speckle. A synthetic aperture method is employed to simulate B-mode imaging with a 5 MHz, 192-element linear array operated using multiple transmit focal zones and dynamic receive focusing. Simulated images of random-scattering phantoms possess approximately Rayleigh speckle statistics. The anatomical model is expected to yield images with speckle statistics comparable to clinical breast images. The long-term objectives of these simulations are to investigate sources of focus aberration in ultrasound breast imaging and the impact of aberration on cancer detection. [Work supported by an NSERC Discovery Grant.]
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".