Robot–patient registration for optical tracker-free robotic fracture reduction surgery. (January 2023) - Ha, Ho-Gun; Han, Gukyeong; Lee, Seongpung; Nam, Kwonsun; Joung, Sanghyun; Park, Ilhyung; Hong, Jaesung
Highlights: Fracture state which is a three-dimensional positional relationship between bones in real-world is simulated in the virtual space of the navigation system. Refined registration which adopts particle swarm optimization with the minimum cross-reprojection error based on bidirectional X-ray images was proposed. Attachable robot features were developed to address the unrecognizable features due to crossed occlusion between the robot and bone. Average translational and rotational errors of the phantom experiments were 1.88 mm and 2.45°, respectively, and the corresponding errors in the ex-vivo experiments were 2.64 mm and 3.32°. Abstract: Background and objective: Image-guided robotic surgery for fracture reduction is a medical procedure in which surgeons control a surgical robot to align the fractured bones by using a navigation system that shows the rotation and distance of bone movement. In such robotic surgeries, it is necessary to estimate the relationship between the robot and patient (bone), a task known as robot–patient registration, to realize the navigation. Through the registration, a fracture state in real-world can be simulated in virtual space of the navigation system. Methods: This paper proposes an approach to realize robot–patient registration for an optical-tracker-free robotic fracture-reduction system. Instead of the optical tracker which is a three-dimensional position localizer, X-ray images are used to realize the robot–patient registration, combining the relationship of both the robot and patient with regards to C-arm. The proposed method consists of two steps of registration, where initial registration is followed by refined registration which adopts particle swarm optimization with the minimum cross-reprojection error based on bidirectional X-ray images. To address the unrecognizable features due to interference between the robot and bone, we also developed attachable robot features. The allocated robot features could be clearly extracted from the X-ray images, and precise registration could be realized through the particle swarm optimization. Results: The proposed method was evaluated in phantom and ex-vivo experiments involving a caprine cadaver. For the phantom experiments, the average translational and rotational errors were 1.88 mm and 2.45°, respectively, and the corresponding errors in the ex vivo experiments were 2.64 mm and 3.32° The results demonstrated the effectiveness of the proposed robot–patient registration. Conclusions: The proposed method enable to estimate the three-dimensional relationship between fractured bones in real-world by using only two-dimensional images, and the relationship is accurately simulated in virtual reality for the navigation. Therefore, a reduction procedure for successful treatment of bone fractures in image-guided robotic surgery can be expected with the aid of the proposed registration method.