Analysis of multilayer and single layer X-ray detectors for contrast-enhanced mammography using imaging task
Bibliographic record
Abstract
A multilayer (single-shot) detector has previously been proposed for contrast-enhanced mammography. The multilayer detector has the benefit of avoiding motion artifacts due to simultaneous acquisition of both high and low energy images. A single layer (dual-shot) detector has the benefit of better control over the energy separation since the incident beams can be produced and filtered separately. In this paper the performance of the multilayer detector is compared to that of a single layer detector using an ideal observer detectability index which is determined from an extended cascaded systems model and a defined imaging task. The detectors are assumed to have amorphous selenium direct conversion layers, however the same theoretical techniques used here may be applied to other types of integrating detectors. The anatomical noise caused by variation of glandularity within the breast is known to dominate the noise power spectrum at low frequencies due to its inverse power law dependence and is thus taken into account in our model to provide an accurate estimate of the detectability index. The conditions leading to the optimal detectability index, such as tube voltage, filtration, and weight factor are reported for both detector designs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".