Sci‐AM2 Sat ‐ 05: Dose verification for rotating multileaf collimator IMRT
Bibliographic record
Abstract
A new method of delivering IMRT has been proposed in which the entire MLC is rotated between each segment. Current linacs were not designed for IMRT delivery with collimator rotation and extensive quality assurance testing must be done before it can be used clinically. This work describes the different areas of testing that need to be considered, namely (1) the commissioning and QA of the Dynamic Beam Delivery (DBD) Toolbox (Varian Medical Systems, Palo Alto) required for collimator rotation control on a Varian CL21EX linac, (2) accurate fluence modeling of rotated apertures, and (3) dosimetric verification of full IMRT treatments delivered with a rotating MLC (RMLC). The DBD toolbox was tested and the collimator rotation angle was found to be accurate and reproducible to within 0.5 degrees. Fluence distributions of varying complexity were generated using the RMLC algorithm and the accuracy of the fluence modeling was validated using film based verification methods. IMRT treatment plans for a prostate, nasopharynx, and c‐shape target were generated with the RMLC segmentation algorithm and were delivered to a phantom. Measured and calculated dose distributions were compared using dose difference, distance‐to‐agreement, gamma factor maps and two‐dimensional profiles. The level of agreement was comparable to clinically accepted plans. Our results show that we can accurately control collimator rotation and precisely model fluence distributions generated from rotating MLC apertures. We also show that the RMLC technique is capable of delivering 3D clinical dose distributions accurately and reproducibly. This work was supported in part by Varian Medical Systems.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".