Single-layer and dual-layer contrast-enhanced mammography using amorphous selenium flat panel detectors
Bibliographic record
Abstract
The accumulation of injected contrast agents allows the image enhancement of lesions through the use of contrast-enhanced mammography. In this technique, the combination of two acquired images is used to create an enhanced image. There exist several methods to acquire the images to be combined, which include dual energy subtraction using a single detection layer that suffers from motion artifacts due to patient motion between image acquisition. To mitigate motion artifacts, a detector composed of two layers may be used to simultaneously acquire the low and high energy images. In this work, we evaluate both of these methods using amorphous selenium as the detection material to find the system parameters (tube voltage, filtration, photoconductor thickness and relative intensity ratio) leading to the optimal performance. We then compare the performance of the two detectors under the variation of contrast agent concentration, tumor size and dose. The detectability was found to be most comparable at the lower end of the evaluated factors. The single-layer detector not only led to better contrast, due to its greater spectral separation capabilities, but also had lower quantum noise. The single-layer detector was found to have a greater detectability by a factor of 2.4 for a 2.5 mm radius tumor having a contrast agent concentration of 1.5 mg ml(-1) in a 4.5 cm thick 50% glandular breast. The inclusion of motion artifacts in the comparison is part of ongoing research efforts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".