Reducing treatment time for robotic radiosurgery by considering path efficiency during node selection.

Area of Science:

• Medical Physics
• Radiation Oncology
• Robotics in Medicine

Background:

• Robotic radiosurgery enables precise non-coplanar beam delivery using a linac-equipped robot traversing predefined nodes.
• While high-quality treatment plans are achievable, prolonged treatment times, largely due to robot traversal, pose a challenge.

Purpose of the Study:

• To decrease robotic radiosurgery treatment duration by developing and integrating algorithms that minimize robot traversal time.
• The developed algorithms are incorporated into a commercial treatment planning system for practical application.

Main Methods:

• An optimization framework for robotic radiosurgery planning was established, featuring a heuristic method for node selection.
• Two novel algorithms were introduced to reduce traversal time: one based on network centrality and another on direct traversal time computation.
• Comparative analysis was performed on brain and liver cases, evaluating treatment time, plan quality, monitor units, and node network structure.

Main Results:

• The traversal time reduction algorithms achieved significant decreases, up to 49% in brain cases and 31% in the liver case.
• Overall treatment time reductions reached up to 30% for brain cases and 13% for the liver case.
• Plan quality metrics, including DVHs, dose distributions, and conformity indices, showed minimal differences between optimized and non-optimized plans.

Conclusions:

• Optimizing node selection for efficient robot traversal paths effectively reduces overall treatment time in robotic radiosurgery.
• The study demonstrates that substantial time reductions can be achieved while maintaining the high quality of radiation therapy plans.