Optimization based trajectory planning for real-time 6DoF robotic patient motion compensation systems

Area of Science:

• Robotics in Medicine
• Radiation Oncology
• Motion Compensation Systems

Background:

• Robotic stabilization is crucial for precise radiation delivery to moving tumors.
• Current methods may lead to suboptimal trajectories, increasing healthy tissue exposure.
• Six degree-of-freedom (6DoF) robots require advanced control for accurate motion compensation.

Purpose of the Study:

• To develop and investigate a novel feedback planning method for optimal 6D robotic trajectory generation.
• To minimize radiation exposure to normal tissues during dynamic tumor target motion.
• To account for robot mechanics, patient safety, and system constraints in real-time optimization.

Main Methods:

• Simulations of two 6DoF robotic systems (Stewart-Gough platform, linac treatment table).
• Motion planning formulated as an optimization problem solved with L-BFGS algorithm.
• Comparison of three planning methods: platform-D, target-S, and target-D (novel method).

Main Results:

• The target-D planning method demonstrated the smallest net trajectory error for synthetic and patient motion.
• Optimal planning achieved a 45% smaller target trajectory error compared to platform-D.
• Target-D planning maintained clinical tolerances (≤0.5mm, ≤0.5deg) for 100% of head motion compensation treatments.

Conclusions:

• A flexible 6D target trajectory optimization framework for robotic motion compensation was investigated.
• The proposed method allows control over mechanical limits, velocities, and acceleration.
• This approach enhances precision and safety in image-guided radiation therapy.