Application of constrained optimization to radiotherapy planning.

Abstract

Essential for the calculation of photon fluence distributions for intensity modulated radiotherapy (IMRT) is the use of a suitable objective function. The objective function should reflect the clinical aims of tumor control and low side effect probability. Individual radiobiological parameters for patient organs are not yet available with sufficient accuracy. Some of the major drawbacks of some current optimization methods include an inability to converge to a solution for arbitrary input parameters, and/or a need for intensive user input in order to guide the optimization. In this work, a constrained optimization method was implemented and tested. It is closely related to the demanded clinical aims, avoiding the drawbacks mentioned above. In a prototype treatment planning system for IMRT, tumor control was guaranteed by setting a lower boundary for target dose. The aim of low complication is fulfilled by minimizing the dose to organs at risk. If only one type of tissue is involved, there is no absolute need for radiobiological parameters. For different organs, threshold dose, relative seriality of the organs or an upper dose limit could be set. All parameters, however, were optional, and could be omitted. Dose-volume constraints were not used, avoiding the possibility of local minima in the objective function. The approach was benchmarked through the simulation of both a head and neck and a lung case. A cylinder phantom with precalculated dose distributions of individual pencil beams was used. The dose to regions at risk could be significantly reduced using at least seven ports of beam incidence. Increasing the number of ports beyond seven produced only minor further gain. The relative seriality of organs was modeled through the use of an added exponent to the dose. This approach however increased calculation time significantly. The alternative of setting an upper limit is much faster and allows direct control of the maximum dose. Constrained optimization guarantees high tumor control probability, it is computationally more efficient than adding penalty terms to the objective function, and the input parameters are dose limits known in clinical practice.