Appropriate phantom for automatic exposure control compensation testing in mammography
Bibliographic record
Abstract
The testing of a mammographic AEC system involves the insertion of suitable phantoms to take the place of the varying thicknesses and composition of the breasts encountered clinically. In this paper we examine the usefulness of mixed-material phantoms, as compared to the single material phantoms recommended by the ACR and the British authorities. The combinations of material are chosen, for each test thickness in the range 2 to 8.5 cm, to closely simulate actual breast composition. It is quite possible, using single-material phantoms, to set up operating conditions and a technique chart for a mammographic unit that will yield images of reproducible optical density within 0.15 OD. While these data will satisfy the regulatory requirements, they are unlikely to meet this object in practice, since breasts are variable in their composition. We have used various combinations of four readily available phantom materials to simulate the adipose and glandular composition of breasts of thicknesses from 2 to 8 cm. From our data, a technique chart was developed that has been found to produce superior clinical images while meeting the ACR optical density requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".