SU‐FF‐T‐175: Dose Rate Optimization for Intensity‐Modulated Arc Therapy
Bibliographic record
Abstract
Purpose: We use multiple dynamic arcs with different MLC shapes at different gantry angles for Intensity‐modulated arc therapy (IMAT). This study is to develop a fast and simple algorithm to optimize the dose rate as a function of gantry angle for IMAT for linear accelerators with dose rate variation capability for a dynamic arc. Method and Materials: The dose rate as a function of gantry angle for a dynamic arc is expressed by an analytical expression that favors beam directions with lower dose deposited to critical structures and higher dose deposited to the target volume. Three parameters are included in the analytical expression for further optimization of the dose rate base on an objective function. The optimized plan is transferred to a forward treatment planning system for final dose calculation. The method is applied to a phantom and a few clinical cases using 6 MV and 18 MV beams for a Varian linear accelerator. Results: The method has been demonstrated with a test phantom and a few clinical radiotherapy cases. We have showed that dose distributions and dose volume histograms have been improved with the dose rate optimization for IMAT. It gives more uniform dose to the target volume and lower dose to the critical structure. Conclusions: We have developed a fast and simple technique to optimize the dose rate as a function of gantry angle for intensity‐modulated arc therapy, while retaining its advantages of an intuitive treatment planning process and efficient radiation delivery.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".