Evaluation of scatter effects on image quality for breast tomosynthesis
Bibliographic record
Abstract
Digital breast tomosynthesis uses a limited number of low-dose x-ray projections to produce a three-dimensional (3D) tomographic reconstruction of the breast. The purpose of this investigation was to characterize and evaluate the effect of scatter radiation on image quality for breast tomosynthesis. Generated by a Monte Carlo simulation method, scatter point spread functions (PSF) were convolved over the field of view (FOV) to estimate the distribution of scatter for each angle of tomosynthesis projection. The results demonstrated that in the absence of scatter reduction techniques, the scatter-to-primary ratio (SPR) levels for the average breast are quite high (~0.4 at the centre of mass), and increased with increased breast thickness and with larger FOV. Associated with such levels of x-ray scatter are cupping artifacts, as well as reduced accuracy in reconstruction values. The effect of x-ray scatter on the contrast, noise, and signal-difference-to-noise ratio (SDNR) in tomosynthesis reconstruction was measured as a function of tumour size. For example, the contrast in the reconstructed central slice of a tumour-like mass (14 mm in diameter) was degraded by 30% while the inaccuracy of the voxel value was 28%, and the reduction of SDNR was 60%. We have quantified the degree to which scatter degrades the image quality over a wide range of parameters, including x-ray beam energy, breast thickness, breast diameter, and breast composition. However, even without a scatter rejection device, the contrast and SDNR in the reconstructed tomosynthesis slice is higher than that 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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".