Conversion of dose–volume constraints to dose limits

Abstract

The purpose of this study is to introduce two techniques for converting dose–volume constraints to dose limits for treatment planning optimization, and to evaluate their performance. The first technique, called dose-sorting, is based on the assumption that higher dose limits should be assigned to the constraint points receiving higher doses, and vice versa. The second technique, the hybrid technique, is a hybrid of the dose-sorting technique and the mixed integer linear programming (MILP) technique. Among all constraint points in an organ at risk, the dose limits for the points far from a dose–volume constraint are determined by dose-sorting, while the dose limits for the points close to a dose–volume constraint are determined by MILP. We evaluated the performance of the two new techniques for one treatment geometry by comparing them with the MILP technique. The dose-sorting technique had a high probability of finding the global optimum when no more than three organs at risk have dose–volume constraints. It was much faster than the MILP technique. The hybrid technique always found the global optimum when the MILP percentage (the percentage of constraint points for which the dose limits are determined by the MILP technique) was large enough, but its computation time increased dramatically with the MILP percentage. In conclusion, the dose-sorting technique and the hybrid technique with a low MILP percentage are clinically feasible.