Objective: Augmented reality (AR) navigation using a position sensor in endoscopic surgeries relies on the quality of patient-image registration and hand-eye calibration. Conventional methods collect the necessary data to compute two output transformation matrices separately. However, the AR display setting during surgery generally differs from that during preoperative processes. Although conventional methods can identify optimal solutions under initial conditions, AR display errors are unavoidable during surgery owing to the inherent computational complexity of AR processes, such as error accumulation over successive matrix multiplications, and tracking errors of position sensor. Methods: We propose the simultaneous optimization of patient-image registration and hand-eye calibration in an AR environment before surgery. The relationship between the endoscope and a virtual object to overlay is first calculated using an endoscopic image, which also functions as a reference during optimization. After including the tracking information from the position sensor, patient-image registration and hand-eye calibration are optimized in terms of least-squares. Results: Experiments with synthetic data verify that the proposed method is less sensitive to computation and tracking errors. A phantom experiment with a position sensor is also conducted. The accuracy of the proposed method is significantly higher than that of the conventional method. Conclusion: The AR accuracy of the proposed method is compared with those of the conventional ones, and the superiority of the proposed method is verified. Significance: This study demonstrates that the proposed method exhibits substantial potential for improving AR navigation accuracy.

Two important and difficult tasks during a bone drilling procedure are guiding the orientation of the drilling axis toward the target and maintaining the orientation against the drilling force. To accomplish these tasks, a remote center of motion (RCM) mechanism is adopted to align the orientation of the drilling axis without changing the entry point. However, existing RCM mechanisms do not provide sufficient resolution and rigidity to address hard tissue cases. We propose a new type of RCM mechanism that uses two sets of linear actuators and a gearless-arc guide to have a high resolution and rigidity. In addition, we designed a single motor-based drilling mechanism based on rolling friction. To achieve automatic control of the guiding and drilling process, we incorporated a computer-tomography-based navigation system that was equipped with an optical tracking system. The effectiveness of the integrated robotic system was demonstrated through a series of experiments and ex vivo drilling tests on swine femurs. The proposed robotic system withstood a maximum external force of 51 N to maintain the joint angle, and the average drilling error was less than 1.2 mm. This study confirms the feasibility of the proposed bone drilling robotic system with a high-resolution and high-rigidity RCM mechanism. This drilling system is the first successful trial based on an RCM mechanism and a single motor-based drilling mechanism, reducing the footprint and required motors with respect to previous bone surgical robots.

Lee S, et al. Yonsei Med J. 2018 Aug;59(6):769-780. https://doi.org/10.3349/ymj.2018.59.6.769

End-effector type robots have been popularly applied to robot-aided therapy for rehabilitation purpose. However, those robots have a key drawback for the purpose: lack of the user's posture (joint angle) information. This paper proposes a novel end-effector rehabilitation robot system that contains a contactless motion sensor to monitor upper- extremity posture during robot-aided reaching exercise. The sensor allows the posture estimation without complicated procedures but has an inaccuracy problem such as occlusion and an unreliable segment length. Therefore, we developed a posture monitoring method, which is an analytical method without training procedure, based on the combined use of the information obtained from the sensor and the robot. Eight healthy subjects participated in the experiment with planar reaching exercise for validation. The results of joint angle estimation, high correlation coefficient (about 0.95) and small errors (about 3.55 degree), show that the proposed system can provide affordable upper-extremity posture estimation.

Conventional navigation systems used in transsphenoidal pituitary surgery have limitations that may lead to organ damage, including long image registration time, absence of alarms when approaching vital organs and lack of 3-D model information. To resolve the problems of conventional navigation systems, this study proposes a U-Net-based, automatic segmentation algorithm for optical nerves and internal carotid arteries, by training patient computed tomography angiography images. The authors have also developed a bendable endoscope and surgical tool to eliminate blind regions that occur when using straight, rigid, conventional endoscopes and surgical tools during transsphenoidal pituitary surgery. In this study, the effectiveness of a U-Net-based navigation system integrated with bendable surgical tools and a bendable endoscope has been demonstrated through phantom-based experiments. In order to measure the U-net performance, the Jaccard similarity, recall and precision were calculated. In addition, the fiducial and target registration errors of the navigation system and the accuracy of the alarm warning functions were measured in the phantom-based environment.