SU‐G‐TeP4‐15: The Roucoulette: A Set of Quality Control Tests for Dynamic Trajectory (4Pi) Treatment Delivery Techniques
Bibliographic record
Abstract
Purpose: To present and validate a set of quality control tests for trajectory treatment delivery using synchronized dynamic couch (translation and rotation), MLC and collimator motion. Methods: The quality control tests are based on the Picket fence test, which consist of 5 narrow band 2mm width spaced at 2.5cm intervals, and adds progressively synchronized dynamic motions. The tests were exposed on GafChromic EBT3 films. The first test is a regular (no motion and MLC static while beam is on) Picket Fence test used as baseline. The second test includes simultaneous collimator and couch rotation, each stripe corresponding to a different rotation speed. Errors in these tests were introduced (0.5 degree and 1 degree error in rotation synchronization) to assess the error sensitivity of this test. The second test is similar to the regular Picket Fence but now including dynamic MLC motion and couch translation (including acceleration during delivery) while the beam is on. Finally in the third test, which is a combination of the first and second test, the Picket Fence pattern is delivered using synchronized collimator and couch rotation and synchronized dynamic MLC and couch translation including acceleration. Films were analyzed with FilmQA Pro. Results: The distance between the peaks in the dose profile where measured (18.5cm away from the isocentre in the inplane direction where non synchronized rotation would have the largest effect) and compared to the regular Picket Fence tests. For well synchronized motions distances between peaks where between 24.9–25.4 mm identical to the regular Picket Fence test. This range increased to 24.4–26.4mm and 23.4–26.4mm for 0.5 degree and 1 degree error respectively. The amplitude also decreased up to 15% when errors are introduced. Conclusion: We demonstrated that the Roucoulette tests can be used as a quality control tests for trajectory treatment delivery using synchronized dynamic motion.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".