Simultaneous minimization of leaf travel distance and tongue-and-groove effect for segmental intensity-modulated radiation therapy
Bibliographic record
Abstract
This paper introduces a method to simultaneously minimize the leaf travel distance and the tongue-and-groove effect for IMRT leaf sequences to be delivered in segmental mode. The basic idea is to add a large enough number of openings through cutting or splitting existing openings for those leaf pairs with openings fewer than the number of segments so that all leaf pairs have the same number of openings. The cutting positions are optimally determined with a simulated annealing technique called adaptive simulated annealing. The optimization goal is set to minimize the weighted summation of the leaf travel distance and tongue-and-groove effect. Its performance was evaluated with 19 beams from three clinical cases; one brain, one head-and-neck and one prostate case. The results show that it can reduce the leaf travel distance and (or) tongue-and-groove effect; the reduction of the leaf travel distance reaches its maximum of about 50% when minimized alone; the reduction of the tongue-and-groove reaches its maximum of about 70% when minimized alone. The maximum reduction in the leaf travel distance translates to a 1 to 2 min reduction in treatment delivery time per fraction, depending on leaf speed. If the method is implemented clinically, it could result in significant savings in treatment delivery time, and also result in significant reduction in the wear-and-tear of MLC mechanics.
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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 | 0.000 |
| 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) | 0.001 | 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 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".