SU‐E‐T‐505: Monte Carlo Simulations for TrueBeam Using 4D IAEA Phase Spaces
Bibliographic record
Abstract
Purpose: To provide a tool for Monte Carlo simulations of dynamic beam delivery in the highly relevant situations where the full linac model is unavailable (such as is currently the case of the TrueBeam linac), which also includes the capability of reading and scoring 4D phase spaces. Methods: We have redesigned DOSXYZnrc source 20 to enable it to dynamically load a BEAMnrc shared library at run time and preserve synchronization between dynamic linac components and the beam motion in the patient geometry, even when starting from a phase space. We have enabled BEAMnrc and DOSXYZnrc to use the actual z coordinate of each particle, when reading a 3D IAEA phase space. Moreover, these codes can now read and output 4D phase spaces by reading/scoring the MU index of each particle. These 4D phase spaces can be used for further synchronization. Results: The updated source 20 has been validated, using film and ion chamber measurements (typically, differences around 1%), and comparisons with a commercial treatment planning system (3D gamma pass rates over 97% for a 3%‐3 mm criterion), for a variety of interesting clinical applications, including jaw‐tracking, non‐coplanar, and multiple‐arc VMAT, for both standard and flattening filter free (FFF) photon beams, starting from curved or general 4D IAEA phase space files. Conclusion: It is now possible to use source 20 for Monte Carlo simulations of TrueBeam and other modern radiotherapy technologies, starting from phase spaces (including curved or, more generally, 4D IAEA phase spaces). The simulations are performed in a single run, continuously, regardless of the complexity of the beam delivery. The new features have been extensively tested in clinical applications, with excellent results. The new 4D phase space capability could prove invaluable in other applications, such as in vivo portal dosimetry or adaptive planning. This work was supported, in part, by research grants provided by British Columbia Cancer Agency and 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.016 | 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".