Dynamic collimator trajectory algorithm for multiple metastases dynamic conformal arc treatment planning
Bibliographic record
Abstract
PURPOSE: To develop an algorithm for dynamic collimator positioning to optimize beam's eye view (BEV) fitting of targets in dynamic conformal arc (DCA)-based radiotherapy procedures, of particular use in multiple metastases stereotactic radiosurgery procedures. METHODS: A trajectory algorithm was developed to dynamically modify the angle of the collimator as a function of arc-based control point to provide optimized collimation of target volume(s). Central to this algorithm is a concept denoted herein as "whitespace" defined as any nontarget area in the BEV that is not covered by any collimation system and is open to exposure from the radiation beam. Calculating whitespace at all collimator angles and every control point, a two-dimensional topographical map depicting the tightness-of-fit of the MLC was generated. A bidirectional gradient trajectory algorithm identified a number of candidate trajectories of continuous collimator motion. Minimization of integrated whitespace was used to identify an optimal solution for the navigation of the parameter space. Plans with dynamic collimator trajectories were designed for multiple metastases targets and were compared with fixed collimator angle dynamic conformal arc (DCA) plans and standard VMAT plans. RESULTS: = 0.90) with mean dose to proximal normal tissue. Seventeen cases were studied using our algorithm to generate dynamic conformal arc (DCA) plans with optimized collimator trajectories for three and four target SRS patients and comparing them to DCA plans generated with optimized fixed collimator angles per arc and standard VMAT plans generated via template. Optimized collimator trajectories were found to produce a reduction in monitor units of up to 49.7 ± 5.1% when compared to VMAT across 17 patients, and all organ-at-risk and normal brain metrics were found to be superior or comparable. CONCLUSION: Dynamic collimator trajectories have the potential to improve DCA deliveries through increased efficiency, especially in the treatment of multiple cranial metastases. Implementation of this technology should not be hindered by mechanical safety considerations as collimator motions do not modify or introduce any new risks of collisions with patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".