MO-FG-BRA-01: 4D Monte Carlo Simulations for Verification of Dose Delivered to a Moving Anatomy
Bibliographic record
Abstract
Purpose: To validate 4D Monte Carlo (MC) simulations of dose delivery by an Elekta Agility linear accelerator to a moving phantom. Methods: Monte Carlo simulations were performed using the 4DdefDOSXYZnrc/EGSnrc user code which samples a new geometry for each incident particle and calculates the dose in a continuously moving anatomy. A Quasar respiratory motion phantom with a lung insert containing a 3 cm diameter tumor was used for dose measurements on an Elekta Agility linac with the phantom in stationary and moving states. Dose to the center of tumor was measured using calibrated EBT3 film and the RADPOS 4D dosimetry system. A VMAT plan covering the tumor was created on the static CT scan of the phantom using Monaco V.5.10.02. A validated BEAMnrc model of our Elekta Agility linac was used for Monte Carlo simulations on stationary and moving anatomies. To compare the planned and delivered doses, linac log files recorded during measurements were used for the simulations. For 4D simulations, deformation vectors that modeled the rigid translation of the lung insert were generated as input to the 4DdefDOSXYZnrc code as well as the phantom motion trace recorded with RADPOS during the measurements. Results: Monte Carlo simulations and film measurements were found to agree within 2mm/2% for 97.7% of points in the film in the static phantom and 95.5% in the moving phantom. Dose values based on film and RADPOS measurements are within 2% of each other and within 2σ of experimental uncertainties with respect to simulations. Conclusion: Our 4D Monte Carlo simulation using the defDOSXYZnrc code accurately calculates dose delivered to a moving anatomy. Future work will focus on more investigation of VMAT delivery on a moving phantom to improve the agreement between simulation and measurements, as well as establishing the accuracy of our method in a deforming anatomy. This work was supported by the Ontario Consortium of Adaptive Interventions in Radiation Oncology (OCAIRO), funded by the Ontario Research Fund Research Excellence program.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".