A novel image-guided robotic system with motion compensation for intraoperative radiation-free localization of pulmonary nodules.

The precise localization of small peripheral lung nodules (< 2 cm) poses a frequent challenge in clinical practice owing to the minimal invasion and low radiation dose requirements. The accuracy of lesion localization is often affected by the patient's respiratory movement during the localization process. In this study, an enhanced robot-assisted localization framework that integrates a novel motion compensation algorithm is proposed for radiation-free pulmonary nodule localization. Specifically, an algorithm for preoperative semi-automatic recognition of surface feature points based on point cloud optimization and clinical knowledge is proposed. The algorithm allows the optimized feature point distribution by minimizing the registration error of different distribution types and random initializations. Then, the laser-world hand-eye calibration algorithm is proposed to achieve accurate calibration of the system. Finally, motion compensation is applied during intraoperative registration to reduce the error caused by the physiological movement of the patient. The accuracy and feasibility of the system were verified through phantom, volunteer, and clinical experiments. The results demonstrate that our proposed automatic preoperative surface feature recognition, intraoperative registration, and localization methods provide a highly potential solution for clinical lesion localization. This study advanced the existing navigation and clinical application protocols of thoracic surgery robotic systems by implementing an innovative and robust positioning method for chest lesions. The integrated system was successfully validated in a clinical setting, demonstrating its potential to transform minimally invasive thoracic surgical procedures by simultaneously minimizing radiation exposure and complications.