In proton therapy, achieving uniform tumor irradiation requires modulation of proton energy deposition to form a spread-out Bragg peak (SOBP). This study introduces a genetic algorithm (GA)-based approach for optimizing SOBP design and range modulation wheel (RMW) configuration, addressing limitations of conventional analytical and semi-analytical methods. The GA was implemented in MATLAB, validated using standard benchmark functions, and coupled with Monte Carlo simulations performed by the MCNP6.1 code to generate Bragg curve libraries for a 230 MeV proton beam passing through varying RMW thicknesses. Additional libraries were created for ten other proton energies to enhance the algorithm’s adaptability across clinical energy ranges. The GA optimized the selection and weighting of Bragg curves to produce SOBPs that met stringent quality assurance (QA) criteria. Results showed excellent agreement between generated and reference SOBPs, with deviations below 0.3% in width and full compliance with QA standards across all tested energies. To support practical implementation, a software application, SOBP-GA Generator, was developed, enabling users to generate Bragg curve libraries and generate customized SOBPs efficiently. Overall, the proposed GA-based framework demonstrated high precision, computational efficiency, and flexibility in generating SOBP profiles suitable for various proton energies. These findings suggest that the method can serve as a robust and automated tool for SOBP optimization and RMW design in clinical proton therapy applications.

Keywords