SU‐F‐E‐19: A Novel Method for TrueBeam Jaw Calibration
Bibliographic record
Abstract
Purpose: A simple jaw calibration method is proposed for Varian TrueBeam using an EPID‐Encoder combination that gives accurate fields sizes and a homogeneous junction dose. This benefits clinical applications such as mono‐isocentric half‐beam block breast cancer or head and neck cancer treatment with junction/field matching. Methods: We use EPID imager with pixel size 0.392 mm × 0.392 mm to determine the radiation jaw position as measured from radio‐opaque markers aligned with the crosshair. We acquire two images with different symmetric field sizes and record each individual jaw encoder values. A linear relationship between each jaw's position and its encoder value is established, from which we predict the encoder values that produce the jaw positions required by TrueBeam's calibration procedure. During TrueBeam's jaw calibration procedure, we move the jaw with the pendant to set the jaw into position using the predicted encoder value. The overall accuracy is under 0.1 mm. Results: Our in‐house software analyses images and provides sub‐pixel accuracy to determine field centre and radiation edges (50% dose of the profile). We verified the TrueBeam encoder provides a reliable linear relationship for each individual jaw position (R2>0.9999) from which the encoder values necessary to set jaw calibration points (1 cm and 19 cm) are predicted. Junction matching dose inhomogeneities were improved from >±20% to <±6% using this new calibration protocol. However, one technical challenge exists for junction matching, if the collimator walkout is large. Conclusion: Our new TrueBeam jaw calibration method can systematically calibrate the jaws to crosshair within sub‐pixel accuracy and provides both good junction doses and field sizes. This method does not compensate for a larger collimator walkout, but can be used as the underlying foundation for addressing the walkout issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".