Precise Path Planning for Robot-assisted Craniotomy: A CT-driven Virtual Center Method.

Objective: Craniotomy is a critical prerequisite for numerous neuro-surgeries, including intracranial tumor resection and cerebral hemorrhage decompression. However, conventional manual craniotomy methods are often time-consuming, labor-intensive, and associated with limited efficiency and safety. Robotic systems offer significant potential to enhance craniotomy procedures by enabling precise positioning and stable motion control, thereby improving safety, accuracy, and efficiency. In this study, we proposed a novel path planning method for robotic craniotomy that automatically generates surgical paths using solely computed tomography (CT) images. Approach: The craniotomy process is divided into two stages: drilling and subsequent milling to connect the drilled holes. The drilling path is determined by the intersection of the skull structure and surgeon-defined drilling intents. A virtual-center method is introduced to adaptively compute an initial milling path from the drilling path, which is further optimized to minimize invasiveness and smoothed for robotic cranial milling. Results: Validation and evaluation were conducted using 10 skull phantoms and 3 living dogs. The results of high success rates demonstrated that our method generated clinically approved outcomes at both anatomical profile and in vivo levels. Significance: This research proposes an CT image-based preoperative path planning method for robotic craniotomy operations. The proposed approach demonstrates seamless integration with force-based robotic surgical systems, highlighting their potential to enhance current craniotomy techniques while establishing a foundation for future developments in autonomous robotic neurosurgery. .