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

Area of Science:

• Neurosurgery
• Medical Imaging
• Augmented Reality

Background:

• Accurate registration is crucial for augmented reality (AR) in image-guided neurosurgery to visualize deep brain lesions.
• Existing methods require precise matching of virtual lesions with anatomical structures under a microscope.

Purpose of the Study:

• To develop a real-time augmented surgical navigation system (MASN) for neurosurgery.
• To enhance lesion visualization and localization using AR technology.

Main Methods:

• Utilized contactless line-structured light registration for patient-image alignment.
• Employed microscope calibration optimization with a dimensional invariant calibrator for real-time tracking.
• Integrated a 3D medical model with surgical microscope video via visible optical tracking.

Main Results:

• Achieved an average patient-image registration root mean square error (RMSE) of 0.78 ± 0.14 mm.
• Obtained a pixel match ratio error (PMRE) of 0.646% for microscope calibration.
• System experiments yielded an RMSE of 0.79 ± 0.10 mm and PMRE of 3.30 ± 1.08%.

Conclusions:

• The developed microscope AR surgical navigation (MASN) system demonstrates feasibility and efficiency.
• MASN effectively overlays virtual lesions onto the microscopic view, aiding surgeons in localizing hidden deep-seated lesions.