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.
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.