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Traditional path planning methods, such as sampling-based and iterative approaches, allow for optimal path’s computation in complex environments. Nonetheless, environment exploration is subject to rules which can be obtained by domain experts and could be used for improving the search. The present work aims at integrating inductive techniques that generate path candidates with deductive techniques that choose the preferred ones. In particular, an inductive learning model is trained with expert demonstrations and with rules translated into a reward function, while logic programming is used to choose the starting point according to some domain expert’s suggestions. We discuss, as use case, 3-D path planning for neurosurgical steerable needles. Results show that the proposed method computes optimal paths in terms of obstacle clearance and kinematic constraints compliance, and is able to outperform state-of-the-art approaches in terms of safety distance-from-obstacles respect, smoothness, and computational time.

Despite offering numerous advantages, percutaneous treatments in interventional cardiology still present several limitations, including the recurrent use of fluoroscopy to track the route of the catheter during the intervention. In this study we propose an augmented reality (AR)-based navigation system for radiation-free interventional procedures using electromagnetic (EM) sensors. A customized tool embedding an EM sensor and a QRcode (automatically tracked in AR) was designed to perform the registration procedure. The accuracy of the system was assessed asking the user to evaluate the distance between the real position of the sensor and its holographic counterpart by means of a holographic measurement tool. Variability between intra-and inter-operator accuracy was assessed, each one performing 10 evaluation tests. Results showed a mean error of 2.70 ± 0.36 mm and 2.68 ± 0.79 mm for the intra-and inter-operator tests, respectively. To the best of our knowledge this is the first study that proposes a user independent procedure for calibrating an AR device with an EM system presenting a quantitative evaluation between intra-and inter-operators.

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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.