Evaluation of scatter effects on image quality for breast tomosynthesis
Bibliographic record
Abstract
Digital breast tomosynthesis uses a limited number (typically 10-20) of low-dose x-ray projections to produce a pseudo-three-dimensional volume tomographic reconstruction of the breast. The purpose of this investigation was to characterize and evaluate the effect of scattered radiation on the image quality for breast tomosynthesis. In a simulation, scatter point spread functions generated by a Monte Carlo simulation method were convolved over the breast projection to estimate the distribution of scatter for each angle of tomosynthesis projection. The results demonstrate that in the absence of scatter reduction techniques, images will be affected by cupping artifacts, and there will be reduced accuracy of attenuation values inferred from the reconstructed images. The effect of x-ray scatter on the contrast, noise, and lesion signal-difference-to-noise ratio (SDNR) in tomosynthesis reconstruction was measured as a function of the tumor size. When a with-scatter reconstruction was compared to one without scatter for a 5 cm compressed breast, the following results were observed. The contrast in the reconstructed central slice image of a tumorlike mass (14 mm in diameter) was reduced by 30%, the voxel value (inferred attenuation coefficient) was reduced by 28%, and the SDNR fell by 60%. The authors have quantified the degree to which scatter degrades the image quality over a wide range of parameters relevant to breast tomosynthesis, including x-ray beam energy, breast thickness, breast diameter, and breast composition. They also demonstrate, though, that even without a scatter rejection device, the contrast and SDNR in the reconstructed tomosynthesis slice are higher than those of conventional mammographic projection images acquired with a grid at an equivalent total exposure.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".