Sci‐Thur AM: YIS – 09: Treatment Time Optimization for Trajectory‐Based Deliveries
Bibliographic record
Abstract
Introduction: Couch‐gantry trajectory based deliveries are capable of producing high quality Stereotactic Radiosurgery (SRS) treatments in a time efficient manner. In this work we present two methods for treatment time optimization of these deliveries. Methods: Dosimetrically optimal plans were calculated by optimizing MLC and dose rates along a trajectory which encapsulates 4π geometry. This trajectory was used to benchmark time optimization methods. Two methods of treatment time optimization were developed in this study. The first of which relies on a trajectory which samples phase space variably dependent on a selected sampling frequency. Treatment plans were created with decreased sampling frequencies until the treatment quality was deleteriously effected and the most time efficient delivery was selected. The second method uses a gradient descent algorithm to remove time inefficient sections of a trajectory and subsequently re‐optimizes the MLC and dose rates to form a new, time optimized trajectory. Results: Both methods were capable of producing treatment plans which were dose equivalent to the benchmark 4π geometry plan. The methods reduced treatment time of 12Gy fractions by 18% and 13% for the first and second method respectively. Both methods had a more pronounced time saving benefits for 2Gy fractions, for which delivery time was decreased by 53% and 34% for the first and second method respectively. Conclusion: The methods presented were capable of producing highly conformal SRS treatment plans. These treatments took an average time of 109 seconds for fractionated deliveries and 394 seconds as single fraction deliveries.
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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) | 0.000 | 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) | 0.009 | 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".