Impact of image acquisition timing on image quality for dual energy contrast-enhanced breast tomosynthesis
Bibliographic record
Abstract
Dual-energy contrast-enhanced digital breast tomosynthesis (DE CE-DBT) image quality is affected by a large parameter space including the tomosynthesis acquisition geometry, imaging technique factors, the choice of reconstruction algorithm, and the subject breast characteristics. The influence of most of these factors on reconstructed image quality is well understood for DBT. However, due to the contrast agent uptake kinetics in CE imaging, the subject breast characteristics change over time, presenting a challenge for optimization . In this work we experimentally evaluate the sensitivity of the reconstructed image quality to timing of the low-energy and high-energy images and changes in iodine concentration during image acquisition. For four contrast uptake patterns, a variety of acquisition protocols were tested with different timing and geometry. The influence of the choice of reconstruction algorithm (SART or FBP) was also assessed. Image quality was evaluated in terms of the lesion signal-difference-to-noise ratio (LSDNR) in the central slice of DE CE-DBT reconstructions. Results suggest that for maximum image quality, the low- and high-energy image acquisitions should be made within one x-ray tube sweep, as separate low- and high-energy tube sweeps can degrade LSDNR. In terms of LSDNR per square-root dose, the image quality is nearly equal between SART reconstructions with 9 and 15 angular views, but using fewer angular views can result in a significant improvement in the quantitative accuracy of the reconstructions due to the shorter imaging time interval.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".