Experimental results of the first prototype direct-indirect dual-layer flat-panel detector for contrast-enhanced digital mammography and contrast-enhanced digital breast tomosynthesis
Bibliographic record
Abstract
Contrast enhanced digital mammography (CEDM) and contrast enhanced digital breast tomosynthesis (CEDBT) highlight the uptake of iodinated contrast agent in breast lesions in dual-energy (DE) subtracted images. In conventional methods, low-energy (LE) and high-energy (HE) images are acquired with two separate exposures, referred to as the dual-shot (DS) method. Patient motion between two exposures could result in residual breast tissue structure in DE images, which reduces iodinated lesion conspicuity. We propose to use a direct-indirect dual-layer flat-panel detector (DI-DLFPD) to acquire LE and HE images simultaneously, thereby eliminating the motion artifact. The DI-DLPFD system comprise a k-edge filter at the tube output, an amorphous-selenium (a-Se) direct detector as the front layer, and a cesium iodide (CsI) indirect detector as the back layer. This study presents the CEDM and CEDBT results from the first prototype DI-DLFPD. For comparison, CEDM and CEDBT images were also acquired with DS technique, with simulated 2mm patient motion between LE and HE exposures. The figure of merit (FOM) used to assess iodinated object detectability is the dose normalized signal difference to noise ratio squared. Our results showed that DI-DLFPD images exhibit complete cancellation of breast tissue structure, which led to significant improvement in iodinated object detectability and more accurate iodine quantification, compared to DS images with simulated patient motion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".