Poster — Thur Eve — 58: Beam Orientation Optimization for IMRT Treatment Planning Using PARETO

Abstract

Intensity‐modulated radiation therapy (IMRT) treatment planning requires tradeoffs to be made between delivering a prescribed dose to the planning target volume (PTV) and sparing the organ's‐at‐risk (OARs). Traditionally in clinical practice, treatment planners manually optimize beam orientations, objectives, and/or weights in a time‐consuming, trial‐and‐error process to find some acceptable compromise, with no guarantee that this solution is actually optimal. We propose a novel and powerful fluence and beam orientation optimization package for radiotherapy optimization, called PARETO (Pareto‐Aware Radiotherapy Evolutionary Treatment Optimization), which consists of a multi‐objective genetic algorithm capable of optimizing several objective functions simultaneously and mapping the structure of their trade‐off surface efficiently and in detail. Fitness functions, based on mean dose for the OARs and PTV, as well as fluence gradients are optimized. PARETO intelligently varies all beam orientations and beam fluence to simultaneously optimize all objectives. Over many generations, the entire family of Pareto‐optimal treatment plans, spanning a multi‐dimensional trade‐off surface, is mapped out. Pareto‐optimal solutions are stored in a database and trade‐offs between the competing objectives can be visualized graphically and explored. The efficacy of the solutions provided by PARETO was evaluated using a commercial treatment planning system with five coplanar IMRT treatment plans for a homogenous phantom consisting of three OARs surrounding a central PTV. This work demonstrated that the fitness functions within PARETO have a strong correlation to the dose distribution. Thus, from many Pareto‐optimal plans, the clinician may select the plan which they decide is the most appropriate multi‐objective compromise for a patient.