SU‐FF‐T‐429: Electron Monte Carlo Dose Calculation in Eclipse for Siemens Linear Accelerators
Bibliographic record
Abstract
Purpose: Initially, the implementation of the macro Monte Carlo (MC) method into Eclipse (Varian Medical Systems), named eMC, was only carried out for Varian accelerators. This leads to limitations in accuracy if eMC is applied to Siemens machines. In this work eMC has been adjusted in order to allow accurate dose calculations of electron beams for Siemens accelerators. Method and Materials: Several changes have been implemented into the eMC algorithm. First, the beam model has been modified by introducing a 2D fluence instead of a 1D radial fluence distribution for the primary electrons and by including all scrapers of the applicator. Second, the resolution of mono‐energetic depth dose curves used during beam configuration has been increased to determine the initial electron energy spectrum. Furthermore, the size of the sphere within the macro MC transport has been reduced when the incident energy of the electron is below certain thresholds. Calculated and measured dose distributions are compared for Siemens machines using electron energies of 6, 7, 9, 13, 17, 20 and 21 MeV and applicators ranging from 10×10 to 25×25 cm2. Results: Calculated and measured absolute depth dose curves agree within 1% or 1 mm for all energy and applicator combinations investigated. Calculated and measured absolute dose profiles at depths of dmax, R50 and in the photon tail generally agree to within 2%. Some dose values close to the field edge show slightly larger differences. Conclusion: In this work several improvements have been implemented into the eMC algorithm. Due to these improvements the eMC algorithm was able to successfully configure electron beams from Siemens machines. The results of the dose comparison suggest that eMC is suitable to predict dose distributions also for Siemens linear accelerators. Conflict of Interest: This work was supported by Varian Medical Systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".