Poster — Thur Eve — 28: Enabling trajectory‐based radiotherapy on a TrueBeam accelerator with the Eclipse treatment planning system
Bibliographic record
Abstract
The TrueBeam linear accelerator platform has a developer's mode which permits the user dynamic control over many of the machine's mechanical and radiation systems. Using this research tool, synchronous couch and gantry motion can be programmed to simulate isocentric treatment with a shortened SAD, with benefits such as smaller projected MLC leaf widths and an increased dose rate. In this work, water tank measurements were used to commission a virtual linear accelerator with an 85 cm SAD in Eclipse, from which several arc‐based radiotherapy treatments were generated, including an inverse optimized VMAT delivery. For each plan, the pertinent treatment delivery information was extracted from control points specified in the Eclipse‐exported DICOM files using the pydicom package in Python, allowing construction of an XML control file. The dimensions of the jaws and MLC positions, defined for an 85 cm SAD in Eclipse, were scaled for delivery on a conventional SAD linear accelerator, and translational couch motion was added as a function of gantry angle to simulate delivery at 85 cm SAD. Ionization chamber and Gafchromic film measurements were used to compare the radiation delivery to dose calculations in Eclipse. With the exception of the VMAT delivery, ionization chamber measurements agreed within 3.3% of the Eclipse calculations. For the VMAT delivery, the ionization chamber was located in an inhomogeneous region, but gamma evaluation of the Gafchromic film plane resulted in a 94.5% passing rate using criteria of 3 mm/3%. The results indicate that Eclipse calculation infrastructure can be used.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 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".