Multi-objective radiation therapy optimization method

摘要

A novel and powerful fluence and beam orientation optimization package for radiotherapy optimization, called PARETO (Pareto-Aware Radiotherapy Evolutionary Treatment Optimization), makes use 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. PARETO generates a database of Pareto non-dominated solutions and allows the graphical exploration of trade-offs between multiple planning objectives during IMRT treatment planning PARETO offers automated and truly multi-objective treatment plan optimization, which does not require any objective weights to be chosen, and therefore finds a large sample of optimized solutions defining a trade-off surface, which represents the range of compromises that are possible.

主权项

1. A radiation therapy optimization method for a multi-objective optimization problem comprising a planning-target-volume surrounded by organs-at-risk in a patient to be treated using radiation, the method comprising:
representing computed tomography data of the patient as a three-dimensional grid of coordinates divided into voxels which contain the planning-target-volume and the organs-at-risk; using a multi-objective optimizer to search successive generations of trial solutions to generate a database of optimized solutions that form a Pareto non-dominated set of solutions, which sample an estimation of a Pareto front to the multi-objective optimization problem where all solutions on the Pareto front are regarded as equally optimal, each trial solution being specified by a set of parameters defining beam orientations and fluence patterns which together define the radiation; evaluating each generation of trial solutions by:
estimating a radiation dose proxy to each voxel for each trial solution to the multi-objective optimization problem;associating a fitness function with each of the organs-at risk and the planning-target-volume;calculating a set of fitness values for each trial solution by evaluating the fitness functions associated with the trial solution using the respective radiation dose proxies of the respective voxels associated with the trial solution such that the fitness values are optimized with respect to Pareto-optimality of the trial solutions; anddetermining the Pareto non-dominated set of solutions to be optimized solutions by comparing the fitness values of the trial solutions to one another and to the fitness values of the optimized solutions of previous generations of the trial solutions; continuing to generate successive generations of the trial solutions according to a defined convergence criterion until the optimized solutions are determined according to prescribed criteria to be approximate Pareto-optimal solutions to the multi-objective optimization problem; and displaying the Pareto non-dominated set of solutions to a user by means of an interactive graphical interface; wherein the set of parameters defining the radiation of each trial solution includes beam orientations and fluence patterns for each one of a number of beams of radiation; and wherein the number of beams of radiation is optimized by: first beams closer than a certain minimum angle being merged together to form a single beam at an intermediate angle defined by fluence parameters constructed by averaging together parameters of the first beams; a penalty function being defined to prefer solutions requiring a fewer number of beams; beams being sorted by gantry angle, thus rearranging an order of their defining parameters, and being re-inserted into a genetic algorithm population; a size of a space of the parameters being reduced by sorting beams by gantry angle; and beam averaging being applied recursively.