TH-E-BRC-08: Monte Carlo Calculation of Breathing Interplay Effect and Dose Calculation Discretization Error for VMAT and TomoTherapy Stereotactic Lung Treatments
Bibliographic record
Abstract
Purpose: The commercial release of volumetric modulated arc therapy techniques and the growing number of helical TomoTherapy users have triggered renewed interest in dose verification methods for exploring the impact of patient motion on dose distributions without the need to approximate time-varying parameters such as gantry position or MLC leaf motion. To this end we have developed a Monte Carlo-based calculation method capable of simulating a wide variety of treatment techniques without the need to resort to discretization approximations. The method was applied to VMAT and TomoTherapy stereotactic radiotherapy lung treatments to study the effect of breathing motion interplay and dose calculation discretization errors. Methods: The ability to perform complete position- probability-sampled Monte Carlo dose calculations was implemented in BEAMnrc/DOSXZYnrc user codes. The method includes full accelerator head simulations of TomoTherapy and Elekta linacs, and a realistic representation of beam delivery continuous motion via the interactive sampling of geometry parameters using a random time variable associated to each simulated particle. Patient motion is simulated by selecting the appropriate voxelized 4D-CT breathing phase for each particle using the provided treatment breathing curve. Ten different patient anatomies (7 clinical cases, 3 phantoms) were planned for Elekta VMAT (Monaco) and helical TomoTherapy (Hi-Art). Breathing frequency was varied from 2 to 8 seconds and amplitude ranged from 3 to 15 mm. Results: Results show that breathing motion is properly addressed with the ITV method for most cases studied. Minor dose discrepancies were observed with both systems under relatively unusal clinical conditions. Conclusions: We have developed a Monte Carlo-based calculation method capable of simulating a wide variety of treatment techniques without the need to resort to discretization approximations. The method was applied to VMAT and TomoTherapy stereotactic radiotherapy lung treatments to quantify breathing interplay effect and dose calculation discretization errors. The authors gratefully acknowledge Elekta AB for funding this work.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".