Correlation Mechanism of Cranial Bone Mechanical Characteristics and CT Imaging Features in Robot-Assisted Craniotomy

In robot-assisted neurosurgical craniotomy, cranial drilling serves as a core step in establishing precise surgical access. However, the limited surgical field restricts direct observation of the drill-bone interface interaction dynamics. Existing systems lack the capability to predict mechanical responses based on preoperative imaging, which constrains the implementation of intelligent adaptive robotic drilling. In this paper, we developed a craniotomy robotic platform integrating a six-axis force/torque sensor and an optical navigation system. Aiming at the cranial drilling scenario in craniotomy surgery, through 1006 drilling experiments at multiple sites on 74 ex vivo animal skulls, we systematically investigated the quantitative mapping relationship between CT HU values and drilling forces/torques. A predictive model based on quadratic polynomial regression was established, achieving R² = 0.852 for the drilling force model and R² = 0.816 for the torque model, significantly outperforming both linear and power-law models. Cross-species validation experiments demonstrated robust generalization performance of the model across bovine and ovine skulls, with prediction errors below 6% in high-density regions. This study realized mechanical sample generalization verification at multiple anatomical sites of the skull for craniotomy drilling, providing experimental evidence and methodological support for preoperative mechanical prediction and adaptive force control of craniotomy surgical robots, and can improve the safety and intelligence level of surgery.