Poster - Thur Eve - 55: An automated XML technique for isocentre verification on the Varian TrueBeam
Bibliographic record
Abstract
Isocentre verification tests, such as the Winston-Lutz (WL) test, have gained popularity in the recent years as techniques such as stereotactic radiosurgery/radiotherapy (SRS/SRT) treatments are more commonly performed on radiotherapy linacs. These highly conformal treatments require frequent monitoring of the geometrical accuracy of the isocentre to ensure proper radiation delivery. At our clinic, the WL test is performed by acquiring with the EPID a collection of 8 images of a WL phantom fixed on the couch for various couch/gantry angles. This set of images is later analyzed to determine the isocentre size. The current work addresses the acquisition process. A manual WL test acquisition performed by and experienced physicist takes in average 25 minutes and is prone to user manipulation errors. We have automated this acquisition on a Varian TrueBeam STx linac (Varian, Palo Alto, USA). The Varian developer mode allows the execution of custom-made XML script files to control all aspects of the linac operation. We have created an XML-WL script that cycles through each couch/gantry combinations taking an EPID image at each position. This automated acquisition is done in less than 4 minutes. The reproducibility of the method was verified by repeating the execution of the XML file 5 times. The analysis of the images showed variation of the isocenter size less than 0.1 mm along the X, Y and Z axes and compares favorably to a manual acquisition for which we typically observe variations up to 0.5 mm.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".