SU-E-T-52: A Monte Carlo Model of the Varian IGRT Couch for RapidArc
Bibliographic record
Abstract
Purpose: To evaluate the impact of the Varian IGRT treatment couch on quality assurance results and to present a Monte Carlo model of the IGRT couch that can be included on both a phantom and patient CT dataset. Methods: Three clinical RapidArc treatment plans were generated for various tumor sites. The correct set of Hounsfield Units for each part of the couch structure was determined experimentally. For each patient three verification plans were produced, one including the thick model of the couch, one including the thin model of the couch and one without any couch included. Monte Carlo dose calculations were performed in a water equivalent phantom for each of these plans. Various values of the physical densities for the couch shell were used. Results were compared to measurements using a calibrated Farmer ionization chamber with an active volume of 0.6 cm̂3. Comparison of RapidArc and Monte Carlo 3D doses was performed using a 3 dimensional Gamma-factor analysis with a 3%/3mm DTA criteria. Results: Good agreement was observed between chamber measurements and the treatment planning system for each verification plans. Differences as high as 2.6% was observed between the calculated dose by the planning system with and without the couch. The optimal physical density to model the couch shell was determined to be 0.65g/cm̂3 giving the best agreement with both chamber measurements and the planning system with Gamma values below 1 in over 95% of the points considered for all plans. Conclusions: This work indicates the possibility to accurately model the Varian IGRT couch for Monte Carlo RapidArc simulations and to apply it on patient CT dataset.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".