Multilayer x-ray detector for contrast-enhanced digital subtraction mammography
Bibliographic record
Abstract
Contrast-enhanced digital subtraction mammography relies on the growth of new blood vessels (i.e. tumor angiogenesis) during the development of cancer. The growth accompanies an increase in tumor cell population to provide sufficient materials for cell proliferation. Since cancers will accumulate an injected contrast agent more than other tissues, it is possible to use one of several methods to enhance the area of lesions and remove the contrast of normal tissue. Large area flat panel detectors may be used for contrast-enhanced mammography wherein the subtraction of two acquired images is used to create the resulting enhanced image. Existing methods include temporal subtraction and dual energy subtraction, however these methods suffer from artifacts due to patient motion between the registration of images to be subtracted. In this paper we propose using a multilayer flat panel detector for contrast-enhanced digital subtraction mammography. The detector is designed to acquire both images simultaneously, thus avoiding motion artifacts in the resulting subtracted image. We study the multilayer detector design and examine the optimal weight factor and the signal difference to noise ratio. We find that the multilayer detector has the potential for energy discrimination, and thus the ability to be used for contrast-enhanced digital subtraction mammography.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".