Position‐probability‐sampled Monte Carlo calculation of VMAT, 3DCRT, step‐shoot IMRT, and helical tomotherapy dose distributions using BEAMnrc/DOSXYZnrc
Bibliographic record
Abstract
PURPOSE: The commercial release of volumetric modulated arc therapy techniques using a conventional linear accelerator and the growing number of helical tomotherapy users have triggered renewed interest in dose verification methods, and also in tools for exploring the impact of machine tolerance and patient motion on dose distributions without the need to approximate time-varying parameters such as gantry position, MLC leaf motion, or patient 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. METHODS: The ability to perform complete position-probability-sampled Monte Carlo dose calculations was implemented in the BEAMnrc/DOSXZYnrc user codes of EGSnrc. The method includes full accelerator head simulations of our tomotherapy and Elekta linacs, and a realistic representation of continous motion via the sampling of a time variable. The functionality of this algorithm was tested via comparisons with both measurements and treatment planning dose distributions for four types of treatment techniques: 3D conformal, step-shoot intensity modulated radiation therapy, helical tomotherapy, and volumetric modulated are therapy. RESULTS: For static fields, the absolute dose agreement between the EGSnrc Monte Carlo calculations and measurements is within 2%/1 mm. Absolute dose agreement between Monte Carlo calculations and treatment planning system for the four different treatment techniques is within 3%/3 mm. Discrepancies with the tomotherapy TPS on the order of 10%/5 mm were observed for the extreme example of a small target located 15 cm off-axis and planned with a low modulation factor. The increase in simulation time associated with using position-probability sampling, as opposed to the discretization approach, was less than 2% in most cases. CONCLUSIONS: A single Monte Carlo simulation method can be used to calculate patient dose distribution for various types of treatment techniques delivered with either tomotherapy or a conventional linac. The method simplifies the simulation process, improves dose calculation accuracy, and involves an acceptably small change in computation time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".