Measurement driven, electron beam modeling and commissioning for a Monte Carlo treatment planning system with improved accuracy
Bibliographic record
Abstract
With the development of modern linear accelerators, the dosimetry of complex electron beams technique became a challenge for physicists. Over the past few years, lots of efforts have been done on developing accurate and fast dose algorithms for electrons. Numerous Monte Carlo (MC) models of therapeutic electron beams are presented in the literature. However, beam models built solely with manufacturer specifications of the medical accelerator do not systematically provide acceptable agreements with measurements. Clinically accurate beam models are crucial to MC treatment planning as electron dose calculations found in commercial treatment planning system (TPS) are generally inaccurate for complex geometry or with heterogeneities. Therefore, there is a strong motivation to use highly accurate MC simulations as standard information for commissioning commercial TPS. The current research project consists in developing an improved accurate electron beam model based on detailed information of the linear accelerator and to incorporate it into an in-house TPS: The McGill Monte Carlo Treatment Planning (MMCTP).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".