TH‐C‐12A‐05: Dynamic Couch Motion for Improvement of Radiation Therapy Trajectories in DCA and VMAT
Bibliographic record
Abstract
Purpose: To investigate the potential improvement in dosimetric external beam radiation therapy plan quality using an optimized dynamic gantry and couch motion trajectory which minimizes exposure to the organs at risk. Methods: Patient‐specific anatomical information of head‐and‐neck and cranial cancer patients was used to quantify the geometric overlap between target volumes and organs‐at‐risk (OARs) based on their two‐dimensional projection from source to a plane at isocentre as a function of gantry and couch angle. QUANTEC dose constraints were then used as weighting factors for the OARs to generate a map of couch‐gantry coordinate space indicating degree of overlap at each point in space. A couch‐gantry collision space was generated by direct measurement on a Varian Truebeam linac using an anthropomorphic solid‐water phantom. A dynamic, fully customizable algorithm was written to generate a navigable ideal trajectory for the patient specific couch‐gantry space. The advanced algorithm includes weighting factors which can be used to balance the implementation of absolute minimum values of overlap, with the clinical practicality of largescale couch motion and delivery time. Optimized trajectories were calculated for cranial DCA treatments and for head‐and‐neck VMAT treatments and compared to conventional DCA and VMAT treatment trajectories. Results: Comparison of optimized treatment trajectories with conventional treatment trajectories indicates a decrease in dose to the organs‐at‐risk between 4.64% and 6.82% (2.39 and 3.52 Gy) of the prescription dose per patient per organ at risk. Conclusion: Using simultaneous couch and gantry motion during radiation therapy to minimize the geometrical overlap in the beams‐eye‐view target volumes and the organs‐at‐risk can have an appreciable dose reduction to organs‐at‐risk.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".