Including the effect of motion artifacts in noise and performance analysis of dual-energy contrast-enhanced mammography
Bibliographic record
Abstract
In contrast-enhanced mammography (CEM), the dual-energy dual-exposure technique, which can leverage existing conventional mammography infrastructure, relies on acquiring the low- and high-energy images using two separate exposures. The finite time between image acquisition leads to motion artifacts in the combined image. Motion artifacts can lead to greater anatomical noise in the combined image due to increased mismatch of the background tissue in the images to be combined, however the impact has not yet been quantified. In this study we investigate a method to include motion artifacts in the dual-energy noise and performance analysis. The motion artifacts are included via an extended cascaded systems model. To validate the model, noise power spectra of a previous dual-energy clinical study are compared to that of the model. The ideal observer detectability is used to quantify the effect of motion artifacts on tumor detectability. It was found that the detectability can be significantly degraded when motion is present (e.g., detectability of 2.5 mm radius tumor decreased by approximately a factor of 2 for translation motion on the order of 1000 μm). The method presented may be used for a more comprehensive theoretical noise and performance analysis and fairer theoretical performance comparison between dual-exposure techniques, where motion artifacts are present, and single-exposure techniques, where low- and high-energy images are acquired simultaneously and motion artifacts are absent.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".