Poster — Wed Eve—29: Validation of VMAT Delivery Using a Commercial 2D Diode Array
Bibliographic record
Abstract
Volumetric modulated arc therapy (VMAT) is a new modality that combines IMRT with rotational delivery. RapidArc (Varian Medical Systems) incorporates the capability to vary dose rate, gantry speed and dynamic multileaf collimators to optimize dose and delivery efficiency. While new systems are being developed for VMAT verification, diode arrays are efficient and convenient tools for beam‐wise verification of IMRT delivery, and also have been employed in composite rotational delivery verification. Mapcheck2 (Sun Nuclear Corporation) is a new diode array with 1527 detectors. In this study, we investigate the use of Mapchekc2 in the MapPhan solid water phantom to validate VMAT delivery. 5 VMAT plans were generated in Eclipse using the RapidArc optimizer. All plans were delivered several times to verify consistency of delivery and measurement. Gamma analysis was used to verify the results compared to calculated dose distributions. The system was found to be sensitive to small changes in position or orientation. RapidArc beam delivery has been verified to correspond well with calculated dose distributions. Gamma values were below 1 in greater than 90% of measured points for all treatment plans. Delivery was found to be insensitive to initial dose rate. The MapPhan, with Mapcheck2 2D array in solidwater is a convenient way to verify VMAT plans. MapPhan results showed that RapidArc delivery is stable and reproducible.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".