Geant4 Simulation of Scatter Radiation Removal: Comparison and Validation of Anti-scatter Grid and Air Gap for X-ray Mammography
Bibliographic record
Abstract
Abstract: X-ray mammography modality provides excellent low-contrast resolution images with low scatter radiation, making it the gold standard in diagnosing breast cancer. Anti-scatter grid and air gap techniques are typically used to further minimize the scatter radiation and improve image quality. Thus, Geant4 simulation was used to investigate the effectiveness of these techniques in removing scatter radiation in X-ray mammography. The effectiveness of an anti-scatter grid was evaluated using the Bucky factor, where it linearly increased with increasing the anti-scatter grid ratio. It was found that increasing the grid frequency affects the Bucky factor depending on the design of the grid ratio. This research proved that designing an anti-scatter grid with high grid frequency (80 lp/mm), low grid ratio (2:1), and proper orientation minimized common anti-scatter grid artifacts. The effectiveness of the air gap technique was also evaluated using the air gap dose factor. It increased non-linearly with increasing magnification. This research validated that using smaller pixel sizes and small focal spot sizes improved spatial resolution with magnification. Our simulation validated that the anti-scatter grid and air gap were effective techniques in removing scatter radiation. By comparing these techniques, the anti-scatter grid was more effective in removing scatter radiation at the expense of increasing the radiation absorbed dose with the exception of 2.0 magnification. It’s recommended to be extremely cautious when using 2.0 magnification or a grid ratio higher or equal to 8:1. These parameters may cause the radiation absorbed dose to be increased by several folds. Keywords: Geant4, Gate, X-ray mammography, Scatter removal, Anti-scatter grid, Air gap.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".