Using branch-and-price to determine optimal treatment plans for volumetric modulated arc therapy (VMAT)

Abstract

Volumetric Modulated Arc Therapy (VMAT) is the state-of-the-art technique for external radiation therapy treatment. In this method, radiation can be delivered continuously on one or more arcs during the rotation of the gantry of the linear accelerator. This property makes VMAT powerful in obtaining high conformal plans in terms of dose distribution within short treatment times. However, the apertures composed by the leaves of the multileaf collimator (MLC) system that shapes continuously the radiation are interdependent, which makes treatment planning hard. We propose a mixed integer linear programming model for VMAT planning problem and exact branch-and-price algorithms to solve it. The objective of the model is to minimize total radiation that is delivered to the patient, and pricing subproblem is decomposable by rows of the MLC and can be solved as a shortest path problem. We generate a large set of test instances from a real data set and evaluate the performance of the proposed branch-and-price algorithm. Computational results reveal that new algorithms are efficient and capable of finding optimal solutions for large problem instances.