TU‐G‐BRA‐01: 4D Monte Carlo Simulations of Beam and Patient Motion Using EGSnrc/BEAMnrc
Bibliographic record
Abstract
Purpose: To develop a 4D Monte Carlo method that simulates continuous beam and patient motion using the EGSnrc Monte Carlo code. Methods: The simulation method links the collimator and gantry settings on a particle‐by‐particle basis to the respiratory phase of the patient at which it is delivered. This synchronization was achieved by implementing two novel particle sources into the defDOSXYZnrc Monte Carlo code which calculates dose in a deforming patient geometry. Validation in a virtual lung phantom was performed for static and tracking beam scenarios. Dose delivery of a RapidArc plan on a commercial dynamic thorax phantom was simulated using RPM and linac log files from the plan delivery. The effects of interplay between the beam and phantom motion were quantified by performing simulations with and without motion synchronization. Results: Simulations in the virtual phantom demonstrate recovery of the static dose profile when the MLC sequence tracks the tumor motion. Simulations of the RapidArc plan delivery showed dose variations of up to 9% of the prescribed dose between plans calculated with and without interplay effects. Conclusions: A 4D Monte Carlo method for EGSnrc has been developed which can calculate dose for combined beam and patient motion. Unlike discretized approaches, this method can calculate the dose distribution in a single simulation. Using treatment log files, this tool can reconstruct the dose actually delivered to a patient, accounting for respiratory motion and interplay effects.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".