One-dimensional scatter grid for the SenoScan slot-scanning digital mammography system
Bibliographic record
Abstract
The SenoScan full-field digital mammography scanner uses a scanning slot detector that is 10 mm wide and 220 mm long. The X-ray beam is collimated to just outside the area of the detector. One important advantage of slot scanning is its inherent scatter rejection. As previously reported, the SenoScan slot scatter rejection is better than that obtained using a 3.5:1 mammography grid, and somewhat worse than that with a 5:1 grid. Additional scatter reduction can potentially improve the contrast in images of thick breasts. We evaluate a custom-designed grid for the slot scanning system. The grid is one-dimensional, offering scatter rejection along the longitudinal axis of the detector. We evaluate the reduction in scatter fraction, grid absorption and changes in the signal-difference-to-noise ratio (SDNR). Based on phantom studies, our results show effective scatter reduction by the grid with minimal reduction of SDNR. Grid absorption and scatter elimination do not necessarily lead to an increase in patient dose, especially if there is a improvement in the number of digital values in the image that are within the useful dynamic range of the detector. A benefit of removing the scatter contribution is an improvement in system dynamic range, because electronic detector gain adjustments can compensate for the drop in the digital pixel values.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".