Monte Carlo modeling of the origin of contaminant electrons on a 0.5T bi‐planar Linac‐MR
Bibliographic record
Abstract
INTRODUCTION: In the last decade, hybrid linear accelerator magnetic resonance imaging (Linac-MR) devices have evolved into FDA-cleared clinical tools, facilitating magnetic resonance guided radiotherapy (MRgRT). The addition of a magnetic field to radiation therapy has previously demonstrated dosimetric and electron effects regardless of magnetic field orientation. PURPOSE: This study uses Monte Carlo simulations to investigate the importance and efficacy of the magnetic field design in mitigating surface dose enhancement in the Aurora-RT, focusing specifically on contaminant electrons, their origin, and energy spectrum. METHODS: The Aurora-RT 0.5 T Biplanar Linac-MR device was modeled using the BEAMnrc package using the updated EM macros, a magnetic field map generated from Opera 3D. Simulation generated phasespace data at the distal side of the first magnetic pole plate (89 cm) and at machine isocenter (120 cm) were analyzed with respect to electron energy spectra and electron creation origins, both with and without the static magnetic field. RESULTS: The presence of the main magnetic field was verified to affect the origin and distribution of contaminant electrons, removing them from the air column up to 60 cm from the target, and focusing them along the CAX within the region below. Analysis of the remaining electron energy fluence reveals the net removal of electrons with energies > 2 MeV and generation of electrons with energies < 2 MeV in the presence of the static magnetic field as compared to no magnetic field. Moreover, in the presence of the magnetic field the integral energy contained in the contaminant electrons increases from 89 cm to isocenter but is still 15% less overall than the integral energy contained in contaminant electrons without the magnetic field. CONCLUSION: This study provides an analysis of contaminant electrons in the Aurora-RT 0.5 T Linac-MR, emphasizing the role of magnetic field design in successfully minimizing electron contaminants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".