A comparison of three inverse treatment planning algorithms

Abstract

Three published inverse treatment planning algorithms for physical optimization of external beam radiotherapy are compared. All three algorithms attempt to minimize a quadratic objective function of the dose distribution. It is shown that the algorithms are based on the common framework of Newton's method of multi-dimensional function minimization. The approximations used within this framework to obtain the different algorithms are described. The use of these algorithms requires that the number of weights of elemental dose distributions be equal to the number of sample points taken in the dose volume. The primary factor in determining how the algorithms are implemented is the dose computation model. Two of the algorithms use pencil beam dose models and therefore directly optimize individual pencil beam weights, whereas the third algorithm is implemented to optimize groups of pencil beams, each group converging upon a common point. All dose computation models assume that the irradiated medium is homogeneous. It is shown that the two different implementations produce similar results for the simple optimization problem of conforming dose to a convex target shape. Complex optimization problems consisting of non-convex target shapes and dose limiting structures are shown to require a pencil beam optimization method.