Treatment planning using a dose–volume feasibility search algorithm

Abstract

Purpose: An approach to treatment plan optimization is presented that inputs dose–volume constraints and utilizes a feasibility search algorithm that seeks a set of beam weights so that the calculated dose distributions satisfy the dose–volume constraints. In contrast to a search for the “best” plan, this approach can quickly determine feasibility and point out the most restrictive of the predetermined constraints. Methods and Materials: The cyclic subgradient projection (CSP) algorithm was modified to incorporate dose–volume constraints in a treatment plan optimization schema. The algorithm was applied to determine beam weights for several representative three-dimensional treatment plans. Results: Using the modified CSP algorithm, we found that either a feasible solution to the dose–volume constraint problem was found or the program determined, after a predetermined set of iterations was performed, that no feasible solution existed for the particular set of dose–volume constraints. If no feasible solution existed, we relaxed several of the dose–volume constraints and were able to achieve a feasible solution. Conclusion: Feasibility search algorithms can be used in radiation treatment planning to generate a treatment plan that meets the dose–volume constraints established by the radiation oncologist. In the absence of a feasible solution, these algorithms can provide information to the radiation oncologist as to how the dose–volume constraints may be modified to achieve a feasible solution.