Recently robot-assisted deep brain stimulation has been widely applied to alleviate symptoms of Parkinson patients. Considering that the surgical outcome is highly associated with preoperative planning, while the present electrode implantation planning is manually performed on 2D images, which is time-consuming and highly dependent on neurosurgeon’s experience. To tackle above issues, we present a novel electrode insertion planning pipeline for precise robotic surgery. First, a data and knowledge co-driven framework is proposed to effectively associate the patients’ brain and “secure implantation channel”. Then, an automatic method is introduced to determine the locations of the stimulation targets and structures at risk during the surgery. Besides, the corresponding signed distance fields are generated for measuring the closest distances from electrode to structures at risk. In addition, we proposed a multi-constraints based planning method in data and knowledge co-defined region to search the optimal electrode insertion path while avoiding cerebral obstacles. We evaluated our method on 10 PD patients undergoing DBS treatment. Our pipeline achieved average obstacle-avoidance distances of 2.15 mm for sulci and 5.68 mm for ventricles, outperforming manual planning by surgeons. Consequently, our approach can produce clinically significant plans with precise stimulation targets and secure insertion trajectories for neurosurgical robotics, demonstrating strong potential for clinical application.
Kui et al. (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: