RESUMO
BACKGROUND: Robotic radiosurgery treatments allow for precise non-coplanar beam delivery by utilizing a robot equipped with a linac that traverses through a set of predetermined nodes. High quality treatment plans can be produced but treatment times can grow large, with one substantial component being the robot traversal time. PURPOSE: The aim of this study is to reduce the treatment time for robotic radiosurgery treatments by introducing algorithms for reducing the robot traversal time. The algorithms are integrated into a commercial treatment planning system. METHODS: First, an optimization framework for robotic radiosurgery planning is detailed, including a heuristic optimization method for node selection. Second, two methods aimed at reducing the traversal time are introduced. One utilizes a centrality measure focusing on the structure of the node network, while the other is based on the direct computation of traversal times during optimization. A comparison between plans with and without the time-reducing algorithms is made for three brain cases and one liver case with basis in treatment time, plan quality, monitor units, and network structure of the selected nodes. RESULTS: Large decreases in traversal times are obtained by the traversal time reducing algorithms, with reductions of up to 49 % in the brain cases and 31 % in the liver case. The resulting reductions in treatment times are up to 30 % and 13 %, respectively. Small differences in plan quality are observed, with similar dose-volume histograms, dose distributions, and conformity/gradient indices. CONCLUSIONS: The total treatment time of the robotic radiosurgery treatments can be reduced by selecting nodes with more efficient robot traversal paths, while maintaining plan quality.