A comprehensive EPID-based 3D validation technique for TrueBeam-delivered VMAT plans
Bibliographic record
Abstract
Purpose: To develop and validate a pre-treatment EPI dosimetry method on Varian TrueBeam linacs using continuous imaging, with reconstruction in a 3D cylindrical phantom geometry. Methods: Delivery of VMAT plans with continuous imaging is currently possible only in Research Mode on TrueBeam linacs, with images acquired in a proprietary format. An earlier technique was adapted to take advantage of technical improvements in EPID delivery, and was tested under various acquisition conditions. The dosimetry of VMAT plans was evaluated at isocentre and within patient volumes that had been transferred to the virtual phantom. Results: Approximately 60 portal image projections per arc were found to be adequate for 3D reconstruction in phantom volumes of 28cm diameter. Twelve prostate, CNS and Head & Neck deliveries were evaluated in Research mode relative to the corresponding Eclipse (v.10) treatment plans, and to measurements on an ArcCheck device in Treatment mode. Mean dose differences at isocentre were within 2% for the three-way comparison, and in PTV volumes were within 1% (s.d. 1%). However, some discrepancies were observed in ArcCheck results that may be related to the small dimensions of certain VMAT apertures. Conclusions: EPI dosimetry with 3D dose reconstruction is an accurate, comprehensive and efficient pre-treatment validation technique for VMAT delivery. Although currently limited to a research mode on TrueBeam, it has the potential to be implemented for clinical use.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".