Sci-Sat AM: Stereo - 05: The Development of Quality Assurance Methods for Trajectory based Cranial SRS Treatments
Bibliographic record
Abstract
The goal of this work was to develop and validate non-planar linac beam trajectories defined by the dynamic motion of the gantry, couch, jaws, collimator and MLCs. This was conducted on the Varian TrueBeam linac by taking advantage of the linac's advanced control features in a non-clinical mode (termed developers mode). In this work, we present quality assurance methods that we have developed to test for the positional and temporal accuracy of the linac's moving components. The first QA method focuses on the coordination of couch and gantry. For this test, we developed a cylindrical phantom which has a film insert. Using this phantom we delivered a plan with dynamic motion of the couch and gantry. We found the mean absolute deviation of the entrance position from its expected value to be 0.5mm, with a standard deviation of 0.5mm. This was within the tolerances set by the machine's mechanical accuracy and the setup accuracy of the phantom. We also present an altered picket fence test which has added dynamic and simultaneous rotations of the couch and the collimator. While the test was shown to be sensitive enough to discern errors 1° and greater, we were unable to identify any errors in the coordination of the linacs collimator and couch. When operating under normal conditions, the Varian TrueBeam linac was able to pass both tests and is within tolerances acceptable for complex trajectory based treatments.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".