Monte Carlo simulation of RapidArc radiotherapy delivery
Bibliographic record
Abstract
RapidArc radiotherapy technology from Varian Medical Systems is one of the most complex delivery systems currently available, and achieves an entire intensity-modulated radiation therapy (IMRT) treatment in a single gantry rotation about the patient. Three dynamic parameters can be continuously varied to create IMRT dose distributions-the speed of rotation, beam shaping aperture and delivery dose rate. Modeling of RapidArc technology was incorporated within the existing Vancouver Island Monte Carlo (VIMC) system (Zavgorodni et al 2007 Radiother. Oncol. 84 S49, 2008 Proc. 16th Int. Conf. on Medical Physics). This process was named VIMC-Arc and has become an efficient framework for the verification of RapidArc treatment plans. VIMC-Arc is a fully automated system that constructs the Monte Carlo (MC) beam and patient models from a standard RapidArc DICOM dataset, simulates radiation transport, collects the resulting dose and converts the dose into DICOM format for import back into the treatment planning system (TPS). VIMC-Arc accommodates multiple arc IMRT deliveries and models gantry rotation as a series of segments with dynamic MLC motion within each segment. Several verification RapidArc plans were generated by the Eclipse TPS on a water-equivalent cylindrical phantom and re-calculated using VIMC-Arc. This includes one 'typical' RapidArc plan, one plan for dual arc treatment and one plan with 'avoidance' sectors. One RapidArc plan was also calculated on a DICOM patient CT dataset. Statistical uncertainty of MC simulations was kept within 1%. VIMC-Arc produced dose distributions that matched very closely to those calculated by the anisotropic analytical algorithm (AAA) that is used in Eclipse. All plans also demonstrated better than 1% agreement of the dose at the isocenter. This demonstrates the capabilities of our new MC system to model all dosimetric features required for RapidArc dose calculations.
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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".