Simultaneous trajectory generation and volumetric modulated arc therapy optimization
Bibliographic record
Abstract
PURPOSE: Trajectory-based treatment planning involves the combination of a gantry-couch trajectory with volumetric modulated arc therapy (VMAT) treatment plan optimization. This work presents the implementation of an optimization methodology that generates a trajectory simultaneous with treatment plan optimization (simTr-VMAT). METHODS: The optimization algorithm is based on the column generation approach, in which a treatment plan is iteratively constructed through the solution of a subproblem called the "pricing problem." The property of the pricing problem to rank candidate apertures based on their associated price is leveraged to select an optimal aperture while simultaneously determining the trajectory path. A progressively increasing gantry-couch grid resolution is used to provide an initial coarse sampling of the angular solution space while maintaining fine control point spacing with the final treatment plan. The trajectory optimization was applied and compared to coplanar VMAT treatment plans for a lung patient, a glioblastoma patient, and a prostate patient. Algorithm validation was performed through the generation of 5000 random trajectories and optimization using column generation VMAT for each patient case, representing the solution space for the trajectory optimization problem. The simTr-VMAT trajectories were compared against these random trajectories based on a quality metric that prefers trajectories with few control points and low objective function value over long, inefficient trajectories. RESULTS: For the lung patient, the simTr-VMAT plan resulted in a decrease of the mean dose of 1.5 and 1.0 Gy to the heart and ipsilateral lung, respectively. For the glioblastoma patient, the simTr-VMAT plan resulted in improved planning target volume coverage with a decrease in mean dose to the eyes, lens, nose, and contralateral temporal lobe between 2 and 7 Gy. The prostate patient showed no clinically relevant dosimetric improvement. The simTr-VMAT treatment plans ranked at the 99.6, 96.3, and 99.4 percentiles compared to the distribution of randomly generated trajectories for the lung, glioblastoma, and prostate patients, respectively. CONCLUSION: The simTr-VMAT optimization methodology resulted in treatment plans with equivalent or improved dosimetric outcomes compared to coplanar VMAT treatment plans, with the trajectories resulting from the optimization ranking among the optimal trajectories for each patient case.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".