Sci-Thurs PM: Planning-08: Improving IMRT Treatment Planning Using Multi-Objective Beam Orientation Optimization

The quality of an IMRT treatment plan is ultimately limited by the trial‐and‐error planning process undertaken by the treatment planner using current treatment planning systems (TPS). We propose a novel multi‐objective genetic algorithm to optimize beam orientations and improve the quality and efficiency of IMRTtreatment planning. Our approach generates a database of Pareto‐optimal solutions for each patient, to be utilized by the human operator during IMRTtreatment planning for the graphical exploration of trade‐offs between multiple planning objectives. A 3D patient structure matrix is generated using the coordinates of the region‐of‐interest contours from patient plans stored in the TPS. A sphere of possible radiation beam orientations is pre‐computed and used to define a solution space. Multi‐objective optimization is achieved through a sophisticated genetic algorithm, called “Ferret”, which incorporates fitness functions based on clinical objectives for the planning target volume and the organ's‐at‐risk. Ferret intelligently varies all beam orientations to simultaneously optimize all objectives. Over many generations, the algorithm maps out the entire family of Pareto‐optimal solutions spanning a multi‐dimensional trade‐off surface. These optimal solutions are stored in a database where trade‐offs between the competing clinical objectives can be visualized graphically. The efficacy of the Pareto‐optimal solutions provided by Ferret was evaluated through a retrospective study comparing the quality of these treatment plans to standard plans for gastric, prostate, and oropharyngeal cancer patients. In each case, the utilization of the solution database and the graphical exploration of trade‐offs between multiple planning objectives produced superior treatment plans compared to conventional plans.