Microscopic augmented reality calibration with contactless line-structured light registration for surgical navigation

The use of AR technology in image-guided neurosurgery enables visualization of lesions that are concealed deep within the brain. Accurate AR registration is required to precisely match virtual lesions with anatomical structures displayed under a microscope. The purpose of this work was to develop a real-time augmented surgical navigation system using contactless line-structured light registration, microscope calibration, and visible optical tracking. Contactless discrete sparse line-structured light point cloud is utilized to construct patient-image registration. Microscope calibration optimization with dimensional invariant calibrator is employed to enable real-time tracking of the microscope. The visible optical tracking integrates a 3D medical model with surgical microscope video in real time, generating an augmented microscope stream. The proposed patient-image registration algorithm yielded an average root mean square error (RMSE) of 0.78 ± 0.14 mm. The pixel match ratio error (PMRE) of the microscope calibration was found to be 0.646%. The RMSE and PMRE of the system experiments are 0.79 ± 0.10 mm and 3.30 ± 1.08%, respectively. Experimental evaluations confirmed the feasibility and efficiency of microscope AR surgical navigation (MASN) registration. By means of registration technology, MASN overlays virtual lesions onto the microscopic view of the real lesions in real time, which can help surgeons to localize lesions hidden deep in tissue.