A Monte Carlo model of the Varian IGRT couch top for RapidArc QA
Bibliographic record
Abstract
The objectives of this study are to evaluate the effect of couch attenuation on quality assurance (QA) results and to present a couch top model for Monte Carlo (MC) dose calculation for RapidArc treatments. The IGRT couch top is modelled in Eclipse as a thin skin of higher density material with a homogeneous fill of foam of lower density and attenuation. The IGRT couch structure consists of two longitudinal sections referred to as thick and thin. The Hounsfield Unit (HU) characterization of the couch structure was determined using a cylindrical phantom by comparing ion chamber measurements with the dose predicted by the treatment planning system (TPS). The optimal set of HU for the inside of the couch and the surface shell was found to be respectively -960 and -700 HU in agreement with Vanetti et al (2009 Phys. Med. Biol. 54 N157-66). For each plan, the final dose calculation was performed with the thin, thick and without the couch top. Dose differences up to 2.6% were observed with TPS calculated doses not including the couch and up to 3.4% with MC not including the couch and were found to be treatment specific. A MC couch top model was created based on the TPS geometrical model. The carbon fibre couch top skin was modelled using carbon graphite; the density was adjusted until good agreement with experimental data was observed, while the density of the foam inside was kept constant. The accuracy of the couch top model was evaluated by comparison with ion chamber measurements and TPS calculated dose combined with a 3D gamma analysis. Similar to the TPS case, a single graphite density can be used for both the thin and thick MC couch top models. Results showed good agreement with ion chamber measurements (within 1.2%) and with TPS (within 1%). For each plan, over 95% of the points passed the 3D gamma test.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".