Effect of scatter and an antiscatter grid on the performance of a slot‐scanning digital mammography system
Bibliographic record
Abstract
The use of a grid increases perceptibility of low contrast objects in mammography. Slot-scan mammography provides a more dose efficient reduction of the scattered radiation reaching the detector than obtained with an antiscatter grid in screen-film or flat-panel digital mammography. In this paper, the potential of using a grid in a slot-scan system to provide a further reduction of scattered radiation is investigated. The components of the digital signal: primary radiation, off-focus radiation, scattered radiation, and optical fluorescence glare in a CsI(Tl) detector were quantified. Based on these measurements, the primary and scatter transmission factors (Tp, Ts), scatter-to-primary ratio (SPR), signal-difference-to-noise ratio (SDNR), and the SDNR improvement factor (K(SDNR)) were obtained. Our results showed that the SPR ranged from 0.05 to 0.19 for breast thicknesses between 2 and 8 cm, respectively. The values of K(SDNR) ranged from 0.85 to 0.94. Because the slot-scanning system has an inherently low SPR, the increase in dose required when the grid is used outweighs the benefit of the small increase in SDNR. It is possible that greater benefit could be achieved by using a grid with a higher Tp, such as obtained using air-core technology.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".