Automated Flexible Needle Trajectory Planning for Keyhole Neurosurgery Using Reinforcement Learning

Planning a safe trajectory for minimally invasive (keyhole) neurosurgery procedures require avoiding critical anatomical structures such as blood vessels and ventricles while optimizing the needle trajectory parameters such as length and curvature to comply with the needle kinematics. In this paper, we propose a reinforcement learning-based method for obtaining kinematically feasible trajectories for flexible needle insertions. Proposed approach utilizes Bezier curve control points that are generated by a reward-based reinforcement learning framework, called Flexible Needle Path Generation (FNPG). FNPG framework is trained using an environment that consists of (1) critical structures (e.g. ventricles) obtained through atlas based segmentation of MRI-T1 images, (2) blood vessels segmented from MR angiography (MRA) data and, (3) simulated brain tumor with varying size and location. The curvilinear paths obtained through the FNPG framework are compared with the traditional sampling based algorithm RRT
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. The results show that the FNPG approach can produce smoother and shorter trajectories compared to RRT
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while avoiding the critical anatomical structures.