SU-E-T-631: Beam Geometry Selection Using Sequential Beam Addition.

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Imaging

Background:

• Optimizing radiation therapy beam geometry is crucial for treatment efficacy and minimizing dose to healthy tissues.
• Traditional methods like equi-angular (EQA) beam arrangements may not always yield optimal dose distributions.
• A novel algorithm for beam geometry selection is needed to improve treatment planning.

Purpose of the Study:

• To investigate the efficacy of a sequential beam addition algorithm (SBA) for selecting optimal beam geometries in radiation therapy.
• To compare the performance of SBA against equi-angular (EQA) arrangements across different treatment sites.

Main Methods:

• The sequential beam addition algorithm (SBA) iteratively selects beams based on an objective function score.
• Candidate beam geometries were evaluated from pools of equi-angular and non-coplanar beams for breast, lung, and brain treatment sites.
• Scores were calculated for varying numbers of beams (up to 11) and compared to EQA and tangential beam arrangements.

Main Results:

• SBA consistently yielded superior scores compared to EQA across all evaluated treatment sites.
• For breast cancer, SBA significantly outperformed EQA and tangential beams with more than two beams.
• While SBA required fewer beams than EQA to achieve equivalent scores for lung cancer, differences were not clinically significant for brain cases using non-coplanar beams.

Conclusions:

• The sequential beam addition algorithm (SBA) is effective in identifying optimal beam arrangements, often requiring fewer beams than traditional methods.
• SBA offers an advantage in situations where beam geometry significantly impacts the objective function, potentially improving treatment planning efficiency.
• The findings suggest SBA as a valuable tool for optimizing radiation therapy beam configurations.