Maximizing the potential of direct aperture optimization through collimator rotation
Bibliographic record
Abstract
Intensity-modulated radiation therapy (IMRT) treatment plans are conventionally produced by the optimization of fluence maps followed by a leaf sequencing step. An alternative to fluence based inverse planning is to optimize directly the leaf positions and field weights of multileaf collimator (MLC) apertures. This approach is typically referred to as direct aperture optimization (DAO). It has been shown that equivalent dose distributions may be generated that have substantially fewer monitor units (MU) and number of apertures compared to fluence based optimization techniques. Here we introduce a DAO technique with rotated apertures that we call rotating aperture optimization (RAO). The advantages of collimator rotation in IMRT have been shown previously and include higher fluence spatial resolution, increased flexibility in the generation of aperture shapes and less interleaf effects. We have tested our RAO algorithm on a complex C-shaped target, seven nasopharynx cancer recurrences, and one multitarget nasopharynx carcinoma patient. A study was performed in order to assess the capabilities of RAO as compared to fixed collimator angle DAO. The accuracy of fixed and rotated collimator aperture delivery was also verified. An analysis of the optimized treatment plans indicates that plans generated with RAO are as good as or better than DAO while maintaining a smaller number of apertures and MU than fluence based IMRT. Delivery verification results show that RAO is less sensitive to tongue and groove effects than DAO. Delivery time is currently increased due to the collimator rotation speed although this is a mechanical limitation that can be eliminated in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".