Beam angle and fluence map optimization for PARETO multi-objective intensity modulated radiation therapy treatment planning

Abstract

In this work we introduce PARETO, a multiobjective optimization tool that simultaneously optimizes beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning using a powerful genetic algorithm. We also investigate various objective functions and compare several parameterizations for modeling beam fluence in terms of fluence map complexity, solution quality, and run efficiency. We have found that the combination of a conformity-based Planning Target Volume (PTV) objective function and a dose-volume histogram or equivalent uniform dose -based objective function for Organs-At-Risk (OARs) produced relatively uniform and conformal PTV doses, with well-spaced beams. For two patient data sets, the linear gradient and beam group fluence parameterizations produced superior solution quality using a moderate and high degree of modulation, respectively, and had comparable run times. PARETO promises to improve the accuracy and efficiency of treatment planning by fully automating the optimization and producing a database of non-dominated solutions for each patient.