Implantation accuracy has a great impact on the outcomes of hip resurfacing such as recovery of hip function. Computer assisted orthopedic surgery has demonstrated clear advantages for the patients, with improved placement accuracy and fewer outliers, but the intrusiveness, cost, and added complexity have limited its widespread adoption. To provide seamless computer assistance with improved immersion and a more natural surgical workflow, we propose an augmented-reality (AR) based navigation system for hip resurfacing. The operative femur is registered by processing depth information from the surgical site with a commercial depth camera. By coupling depth data with robotic assistance, obstacles that may obstruct the femur can be tracked and avoided automatically to reduce the chance of disruption to the surgical workflow. Using the registration result and the pre-operative plan, intra-operative surgical guidance is provided through a commercial AR headset so that the user can perform the operation without additional physical guides. To assess the accuracy of the navigation system, experiments of guide hole drilling were performed on femur phantoms. The position and orientation of the drilled holes were compared with the pre-operative plan, and the mean errors were found to be approximately 2 mm and 2°, results which are in line with commercial computer assisted orthopedic systems today.
In robot-assisted orthopaedic surgery, registration is a key step, which defines the position of the patient in the robot frame so that the preoperative plan can be performed. Current registration methods have their limitations, such as the requirement of immobilising the limb or maintaining the line of sight (LOS). These issues cause inconvenience for the surgeons and interrupt the surgical workflow in the operating room.Targeting these issues, we propose a smart camera-robot registration system for joint replacement. The bone geometry, which is measured directly by a depth camera, is aligned to a preoperatively obtained bone model to calculate the pose of the target. Simultaneously, in order to avoid registration failure caused by LOS disruptions, the depth camera tracks objects that may occlude the target bone, and a robot manipulator is used to move the camera away from the nearest obstacle. An appropriate camera motion to “escape” the obstacle is calculated based on the position and velocity of the obstacle, with the aim of avoiding the occlusion efficiently without changing the general target position in the camera frame. The inverse kinematics of the robot is used to project the Cartesian velocity of the end-effector into the joint space, with kinematic singularities considered for stable robotic control. An admittance controller is designed as the human-robot interface so that the surgeon can directly set the robot configuration by hand according to a given intraoperative scenario.Simulations and experiments with a redundant manipulator were conducted to test the performance of a proof-of-concept implementation. The results show that the proposed obstacle avoidance method can effectively increase the distance between the obstacle and the LOS, which lowers the risk of registration failure due to obstacle occlusion. This pilot study is promising in reducing distractions to the surgeon and could help achieve a fluent and surgeon-centred workflow.
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