Robotic Automatic Drilling for Craniotomy: Algorithms and In Vitro Animal Experiments

Abstract

Craniotomy is used in the treatment of various craniocerebral diseases and injuries. However, there are still many complications in craniotomy, and the detection and protection of the dura mater during the drilling of the skull is a clinical challenge. Preserving the integrity of the dura mater is very important for maintaining the anatomical integrity of the brain, protecting the brain tissue, and preventing complications such as infection, hemorrhage, and cerebrospinal fluid leakage. In this study, a feed velocity and thrust force constraint control method for automatic skull-drilling is proposed. A skull-dura boundary detection algorithm based on monitoring the change of thrust force is proposed. A model that describes the relationship between the thrust force and the displacement of the cranial perforator (CP) during skull drilling-through is derived. A cranial drilling robot-assisted system is built, and experiments on animal skulls are carried out. Across 72 in vitro drilling experiments, the success rate of detecting skull penetration is 93.06 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> . After the skull-dura boundary is detected, the extra displacement of the CP is less than 1 mm. The proposed robot-assisted system not only improves the safety of cranial drilling but also reduces the difficulty and burden of operation for surgeons, and provides a platform for improving the intelligence of neurosurgery.