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MR-Linac Radiotherapy - The Beam Angle Selection Problem.
Rik Bijman1, Linda Rossi1, Tomas Janssen2
1Department of Radiotherapy, Erasmus MC Cancer Institute, Rotterdam, Netherlands.
Frontiers in Oncology
|October 18, 2021
Summary
Computer-generated beam angle class solutions (CS) can replace patient-specific optimization for IMRT planning, improving efficiency without sacrificing quality. This approach is crucial for MR-linac treatments.
Area of Science:
- Medical Physics
- Radiation Oncology
- Radiotherapy Planning
Background:
- Volumetric Modulated Arc Therapy (VMAT) has reduced the need for static-beam IMRT beam angle optimization.
- The unavailability of VMAT in MR-linac systems necessitates re-evaluating optimal beam angle selection for IMRT.
- Rectal cancer serves as a model for investigating automated beam angle optimization strategies.
Purpose of the Study:
- To investigate computer-aided generation of beam angle class solutions (CS) as a replacement for patient-specific beam angle optimization (BAO).
- To assess the plan quality and efficiency of CS compared to BAO and equi-angular setups.
- To evaluate the potential of CS for simplifying clinical workflows in MR-linac treatment planning.
Main Methods:
- 23 rectal cancer patients treated on a Unity MR-linac were analyzed.
- Beam Angle Optimization (BAO) plans with 7-12 beams were generated.
- Class solutions (CS) were derived from BAO plans and compared with BAO and equi-angular (EQUI) plans.
Main Results:
- CS plans demonstrated highly similar quality to BAO plans for >7 beams.
- Both CS and BAO plans were superior to equi-angular setups.
- CS9 reduced bowel/bladder mean dose by 22% compared to EQUI9; EQUI required double the beams for comparable quality.
Conclusions:
- Computer-generated beam angle class solutions (CS) can replace individualized BAO without compromising plan quality.
- CS reduces planning complexity, calculation times, and simplifies clinical workflow for MR-linac treatments.
- CS and BAO significantly outperform equi-angular setups, and CS can prevent time-consuming re-optimization in adaptive treatments.

