Online Trajectory Generation Strategies for Needle-Based Interventions
Abstract
Needle-based interventions such as brachytherapy are among the most common minimally invasive procedures performed. Despite the numerous advantages of such procedures, Surgeons are met with an array of challenges, most significantly, determining a strategy in real time to compensate for needle deflection as the needle passes through various layers of tissue, all having different mechanical properties. This thesis focuses on exploring new state estimation and control strategies to enhance the quality of needle-based interventions. These strategies include the use of machine learning for path planning and state estimation, while congruently exploring how the shape of a deflected needle can be used to explore reachable needle trajectories. Results and limitations are presented for the proposed strategies. A particular focus is made so that the strategies find a needle manipulation strategy that requires as few manipulations as possible.