Full-size anthropomorphic phantom for 2D and 3D breast x-ray imaging
Bibliographic record
Abstract
Anthropomorphic breast phantoms are used to create images that mimic aspects of clinical breast images and are useful in optimization and characterization of breast imaging systems. Here, a full-sized compressed physical breast phantom is designed and manufactured with 100 m resolution, high reproducibility and x-ray properties similar to that of breast tissues. The phantom design is based on a digital model derived from the morphology and distribution of large, medium and small scale fibroglandular and inter-glandular adipose tissue observed in clinical breast computerized tomography (bCT) images. The physical phantom consists of four slabs of a polyamide-12 component that mimics adipose tissue fabricated using selective laser sintering (SLS). The fibroglandular component is a low viscosity resin doped with a small amount of zinc oxide nanoparticles (<110 nm) to increase attenuation. The phantom was imaged on a Senographe Pristina and compared to image simulations of the virtual phantom. The power spectral parameter, β was 3.8±0.2 and 3.9±0.5 for the physical and virtual phantoms in a digital mammogram. The corresponding Laplacian fractional entropy (LFE) averaged 0.22 and 0.14 across the range 0.125–1.29 mm-1. Very good texture cancellation was obtained in contrast-enhanced spectral mammography.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".