This paper aims to develop a navigation system based on mixed reality, which can display multimodal medical images in an immersive environment and help surgeons locate the target area and surrounding important tissues precisely. To be displayed properly in mixed reality, medical images are processed in this system. High-quality cerebral vessels and nerve fibers with proper colors are reconstructed and exported to mixed reality environment. Multimodal images and models are registered and fused, extracting their key information. The multiple processed images are fused with the real patient in the same coordinate system to guide the surgery. The multimodal image system is designed and validated properly. In phantom experiments, the average error of preoperative registration is 1.003 mm and the standard deviation is 0.096 mm. The average proportion of well-registered areas is 94.9%. In patient experiments, the surgeons who participated in the experiments generally indicated that the system had excellent performance and great application prospect for neurosurgery. This article proposes a navigation system of multimodal images for neurosurgery based on mixed reality. Compared with other navigation methods, this system can help surgeons locate the target area and surrounding important tissues more precisely and rapidly.

Aimed at a 3-DoF reconfigurable parallel kinematic machine, a method for tuning PID parameters of its servo system is presented. At first, position, velocity and acceleration inverse solution models of machine driven by the outer translation pairs that contain parallelogram strut structures are developed, and the inverse dynamics model of the rigid body has been set up by means of virtual work principle. Based on the above, making the mean square error of the trajectory of the moving platform least as the optimum goal, and considering the real-timely variational inertial load along with its pose at the same time, the overshoot, adjusting time of the unit step response of the servo system and the optimum range of PID parameters are given through simulations. At last, the correctness and effectiveness of the method are proved by the experiments.