SU‐C‐108‐06: Monte Carlo Simulations for Trajectory‐Based Beam Delivery in Varian TrueBeam Developer Mode
Bibliographic record
Abstract
Purpose: Varian experimental trajectory based beam delivery mode on the TrueBeam linac comes well in advance of treatment planning software capable of managing the range of trajectories the linac is able to deliver. The goal of this study is to present a Monte Carlo based dose calculation system for trajectory based treatment delivered using Varian TrueBeam Developer Mode. Methods: The Varian TrueBeam linear accelerator advanced control features are designed to allow complex three dimensional trajectory beam delivery. In Developer Mode, the machine is capable of dynamically moving all its components simultaneously while the beam is on. Dose delivery is executed via a set of instructions written in XML which use the “Trajectory Model”, a central concept of the TrueBeam control system. In this model all beams are characterized by the relationship between monitor units (MU) and position of the moving components of the linear accelerator. A trajectory based beam was created which includes simultaneous collimator rotation, couch translation and rotation. An in‐house C++ code converts the XML file into DOSXYZnrc input file for source 20. This source supports all of the degrees of freedom available in Developer Mode. Therefore, it is possible to simulate any complex trajectory‐based beam delivery continuously, in a single run. The trajectory beam was delivered on a Varian TrueBeam linac in developer mode using a 6XFFF photon beam with a 1.5cm2 field size defined by the MLC. Monte Carlo dose distribution was compared to Gafchromic film measurement using FilmQA Pro. Results: Good agreement is observed between Monte Carlo and film measurement with over 90% of the points passing the 3%/3mm Gamma test. Conclusion: This work created a tool that can be employed by any user of the TrueBeam Developer Mode to calculate the dose for complex trajectory‐based beam delivery with the accuracy of Monte Carlo simulations. This work is supported by the Varian Master Research Agreement
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".