Inverse Reinforcement Learning Intra-operative Path Planning for Steerable Needle

Objective: This paper presents a safe and effective keyhole neurosurgery intra-operative planning framework for flexible neurosurgical robots. The framework is intended to support neurosurgeons during the intraoperative procedure to react to a dynamic environment. Methods: The proposed system integrates inverse reinforcement learning path planning algorithm combined with 1) a pre-operative path planning framework for fast and intuitive user interaction, 2) a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion and 3) a simulated robotic system. Results: Simulation results performed on a human brain dataset show that the inverse reinforcement learning intra-operative planning method can guide a steerable needle with bounded curvature to a predefined target pose with an average targeting error of 1.34 0.52 (25th=1.02, 75th=1.36) mm in position and 3.16 1.06 (25th=2, 75th=4.94) degrees in orientation under a deformable simulated environment, with a re-planning time of 0.02 sec and a success rate of 100%. Conclusion: With this work, we demonstrate that the presented intra-operative steerable needle path planner is able to avoid anatomical obstacles while optimising surgical criteria. Significance: The results demonstrate that the proposed method is fast and can securely steer flexible needles with high accuracy and robustness.