A novel direct‐indirect dual‐layer flat‐panel detector for contrast‐enhanced breast imaging: Experimental assessment
Bibliographic record
Abstract
BACKGROUND: In contrast-enhanced digital mammography (CEDM) and contrast-enhanced digital breast tomosynthesis (CEDBT), low-energy (LE) and high-energy (HE) images are acquired after injection of iodine contrast agent. Weighted subtraction is then applied to generate dual-energy (DE) images, where normal breast tissues are suppressed, leaving iodinated objects enhanced. Currently, clinical systems employ a dual-shot (DS) method, where LE and HE images are acquired with two separate exposures. However, patient motion between these exposures can cause residual breast tissue structure to appear in the recombined DE images, reducing the visibility of iodinated lesions. To address this issue, we propose a direct-indirect dual-layer flat-panel detector (DI-DLFPD), which eliminates patient motion artifact by acquiring LE and HE images simultaneously. PURPOSE: This study aims to: (1) experimentally validate CEDM and CEDBT imaging using a first-generation prototype DI-DLFPD, (2) compare lesion conspicuity with images obtained using the DS method, (3) optimize the k-edge filter for the DI-DLFPD based on breast thickness. METHODS: CEDM and CEDBT images were acquired using both the prototype DI-DLFPD and a conventional DS single-layer detector at comparable dose levels to evaluate image quality and iodine target conspicuity. The figure of merit (FOM) was defined as the square of signal difference to noise ratio (SDNR) divided by the mean glandular dose. The selection of k-edge filter for DI-DLFPD was assessed based on varying breast thickness. RESULTS: By eliminating patient motion artifacts, DI-DLFPD images exhibit a significant improvement (∼233%) in iodine object FOM compared to DS images affected by patient motion. Iodine quantification accuracy was also improved. The results suggest using a sliver filter for average breast thickness (4 cm) and a Tin filter for thicker breasts (8 cm) to achieve optimal iodine SDNR for DI-DLFPD. CONCLUSIONS: This study presents experimental results from the first-generation prototype DI-DLFPD for CEDM and CEDBT. A practical strategy was recommended, where the x-ray filter for DI-DLFPD was optimized based on the compressed breast thickness.
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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) | 0.000 | 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 | 0.001 | 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".