Structural intervention cardiology (SIC) interventions are crucial procedures for correcting heart valves, walls, and muscle form defects. However, the possibility of embolization or perforation, as well as the lack of transparent vision and autonomous surgical equipment, make it difficult for the clinician. This paper proposes a robot-assisted tendon-driven catheter and machine learning-based path planner to overcome these challenges. Firstly, an analytical inverse kinematic model is constructed to convert the tip location in the Cartesian space to the tendons' displacement. Then inverse reinforcement learning algorithm is employed to calculate the optimal path to avoid possible collisions between the catheter tip and the atrial wall. Moreover, a closed-loop feedback controller is adopted to improve positioning accuracy in a direct distal position measurement manner. Simulation and experiments are designed and conducted to demonstrate the feasibility and performance of the proposed system.

Keyhole neurosurgery is challenging, due to the complex anatomy of the brain and the inherent risk of damaging vital structures while reaching the surgical target. This paper presents a path planner for safe and effective neurosurgical interventions. The strengths of the proposed framework lay in the integration of multiple risk structures combined into a deductive method for fast and intuitive user interaction, and a modular architecture. The tool is intended to support neurosurgeons at quickly determining the most appropriate surgical trajectory through the brain matter with minimized risk; the user interface guides the user through the decision making process and helps save planning time of neurosurgical interventions. Risk structures and trajectories can be visualized in an intuitive way, thanks to a 3D brain surgery simulator developed with Unity. A qualitative evaluation with clinical experts shows the practical relevance, while a quantitative performance and functionality analysis proves the robustness and effectiveness of the system with respect to literature.

Path planning algorithms for steerable needles in medical applications must guarantee the anatomical obstacle avoidance, reduce the insertion length, and ensure the compliance with the needle kinematics. The majority of the solutions from the literature focus either on fast computation or on path optimality, the former at the expense of suboptimal paths, the latter by making unbearable the computation in case of a high-dimensional workspace. In this article, we implement a three-dimensional path planner for neurosurgical applications, which keeps the computational cost consistent with standard preoperative planning algorithms and fine-tunes the estimated pathways in accordance to multiple optimization objectives. From a user-defined entry point, our method confines a sample-based path search within a subsection of the original workspace considering the degree of curvature admitted by the needle. An evolutionary optimization procedure is used to maximize the obstacle avoidance and reduce the insertion length. The pool of optimized solutions is examined through a cost function to determine the best path. Simulations on one dataset showed the ability of the planner to save time and overcome the state of the art in terms of obstacle avoidance, insertion length, and probability of failure, proving this algorithm as a valid planning method for complex environments.

Path planning algorithms for steerable catheters, must guarantee anatomical obstacles avoidance, reduce the insertion length and ensure the compliance with needle kinematics. The majority of the solutions in literature focuses on graph based or sampling based methods, both limited by the impossibility to directly obtain smooth trajectories. In this work we formulate the path planning problem as a reinforcement learning problem and show that the trajectory planning model, generated from the training, can provide the user with optimal trajectories in terms of obstacle clearance and kinematic constraints. We obtain 2D and 3D environments from MRI images processing and we implement a GA3C algorithm to create a path planning model, able to generalize on different patients anatomies. The curvilinear trajectories obtained from the model in 2D and 3D environments are compared to the ones obtained by A* and RRT* algorithms. Our method achieves state-of-the-art performances in terms of obstacle avoidance, trajectory smoothness and computational time proving this algorithm as valid planning method for complex environments.