Bibliographic record
Abstract
IMRT treatment plans are conventionally produced by the optimization of fluence maps followed by a leaf sequencing step. An alternative approach to fluence based dose optimization is to optimize directly the leaf positions and weights of the segmented fields. This approach is referred to as direct aperture optimization. Here we describe a direct aperture optimization algorithm in which the entire MLC is rotated between each segment field. We call our approach Rotating Aperture Optimization (RAO). The advantages of collimator rotation in IMRT include higher spatial resolution, more flexibility in the generation of aperture shapes and less interleaf error. Since leaf positions are optimized directly, MLC constraints are taken into account during the optimization. Additional constraints like minimum aperture area for each segment shape can also be added. The number of segments per beam is specified by the user. We have tested our RAO algorithm on a complex c‐shape target, a prostate cancer patient and a nasopharynx carcinoma patient. DVH comparisons show that RAO produces highly conformal plans with only 6 segments per beam angle. We show that setting a minimum aperture size for each segment leads to more efficient delivery without compromising the quality of the dose distribution. Our results also show a significant reduction in the number of MU (40% for the nasopharynx and 27% for the prostate) when compared with a fluence based collimator rotated leaf sequencing technique. 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".