Optimizing cone beam CT scatter estimation in <b> <tt>egs_cbct</tt> </b> for a clinical and virtual chest phantom
Bibliographic record
Abstract
PURPOSE: Cone beam computed tomography (CBCT) image quality suffers from contamination from scattered photons in the projection images. Monte Carlo simulations are a powerful tool to investigate the properties of scattered photons.egs_cbct, a recent EGSnrc user code, provides the ability of performing fast scatter calculations in CBCT projection images. This paper investigates how optimization of user inputs can provide the most efficient scatter calculations. METHODS: Two simulation geometries with two different x-ray sources were simulated, while the user input parameters for the efficiency improving techniques (EITs) implemented inegs_cbct were varied. Simulation efficiencies were compared to analog simulations performed without using any EITs. Resulting scatter distributions were confirmed unbiased against the analog simulations. RESULTS: The optimal EIT parameter selection depends on the simulation geometry and x-ray source. Forced detection improved the scatter calculation efficiency by 80%. Delta transport improved calculation efficiency by a further 34%, while particle splitting combined with Russian roulette improved the efficiency by a factor of 45 or more. Combining these variance reduction techniques with a built-in denoising algorithm, efficiency improvements of 4 orders of magnitude were achieved. CONCLUSIONS: Using the built-in EITs inegs_cbct can improve scatter calculation efficiencies by more than 4 orders of magnitude. To achieve this, the user must optimize the input parameters to the specific simulation geometry. Realizing the full potential of the denoising algorithm requires keeping the statistical uncertainty below a threshold value above which the efficiency drops exponentially.
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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) | 0.000 | 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".