WE‐C‐BRB‐04: Fast and Accurate Hybrid Source Model for Modulated Electron Radiotherapy
Bibliographic record
Abstract
Purpose: To develop a highly accurate and fast method for calculating electron beam dose distributions in Modulated Electron Radiation Therapy (MERT). Method: An algorithm has been developed for creating phase‐ space files at the exit of a linear accelerator for any arbitrary intensity and energy electron beam without the need of full Monte Carlo simulations. The model assigns each particle to one of the 3 following sources: primary, secondary collimator and electron collimator scatter. The primary component is derived by fast MC transport in air. The scatter components are derived by the use of MC pre‐calculated leaf kernels. Each kernel includes the fluence distribution, energy distribution and scatter probability of generating an electron from a leaf. The original position is sampled from Gaussian or uniform distributions. The direction is estimated by geometrical means. According to the projection of the direction, a particle is rejected if it is expected to suffer a leaf‐hit. A leaf‐hit counter is used to calculate the output of scatter particles based on the pre‐calculated scatter probabilities. To account for multiple coulomb scattering in air a MC‐corrected version of the Fermi‐Eyges scattering theory was implemented. Results: Depth and profile dose distributions were derived for the largest and smallest square field sizes, as well as for irregular and off‐axis fields. The model agreed with full MC dose distributions within 3% in all cases. Output at the depth of maximum dose exhibited discrepancies less than 2.6% in all cases. The model was 16–22 times faster in generating a phase‐space file than a full MC simulation with the BEAMnrc code. Conclusions: Fast, dynamic electron beam calculations open up the possibility for real time delivery of MERT in the clinic and renew interest in electron beam therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".