Poster — Thur Eve — 21: Monte Carlo Simulation of X‐Ray Scatter in Diagnostic Range with EGSnrc and GEANT4
Bibliographic record
Abstract
In diagnostic x‐ray imaging, the attenuation of matter to x‐rays is substantially due to Compton and Rayleigh scattering in low Z materials. Consequently, the contrast of the x‐ray images is degraded by scattered photons. Because the total coherent scatter cross‐section is only about 10% of total Compton scatter cross‐section, Compton scatter plays a crucial role in x‐ray image quality. The x‐ray scatter effect can be corrected with experimentally measured or simulated data to improve image quality. It is necessary to acquire accurate x‐ray scatter information. Monte Carlo simulation is an important way of acquiring the x‐ray scatter information and the only practical way to analyze the Compton and Rayleigh scatter separately. However, due to the different ways of modeling the physics and various approximations in different Monte Carlo simulation packages, inconsistent simulation results have been observed. Using the Monte Carlo simulation packages of EGSnrc and GEANT4 we investigated the effects of the electron‐binding effects on the simulated x‐ray scattering in the diagnostic range. It is found that the Compton scatter and Rayleigh scatter are significantly different when simulated with different simulation packages and different physics models, e.g. with or without electron binding effect simulated. These differences are probably due to the ways the scattering process is modeled in the Monte Carlo simulation packages. Because the crucial role of simulated x‐ray scatter in scatter correcting in diagnostic imaging, improved modeling to the x‐ray scatter process in the Monte Carlo simulation package is desired.
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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".