Improving mixed-reality neuronavigation with blue-green light: a comparative multimodal laboratory study.

Area of Science:

• Neurosurgery and Medical Imaging
• Human-Computer Interaction
• Optical Engineering

Background:

• Neuronavigation systems are crucial for surgical precision.
• Mixed-reality neuronavigation (MRN) offers immersive guidance.
• The impact of lighting conditions on MRN accuracy remains underexplored.

Purpose of the Study:

• To compare the accuracy of mixed-reality neuronavigation (MRN) against magnetic neuronavigation (MN).
• To investigate the effect of blue-green light (BGL) on MRN accuracy.
• To evaluate the influence of operator experience on navigation performance.

Main Methods:

• A phantom-based experiment using CT-scanned phantoms with fiducials.
• Registration of 3D models onto 3D-printed skulls.
• Accuracy assessment of MN and MRN under indirect white light (NL), direct white light (WL), and BGL.
• Inclusion of senior and junior operators to analyze experience-based performance differences.

Main Results:

• No significant difference in accuracy between operators for magnetic neuronavigation (MN).
• Senior operator achieved higher precision but longer times in mixed-reality neuronavigation (MRN).
• Blue-green light (BGL) consistently enhanced MRN accuracy in both experiments.

Conclusions:

• Blue-green light (BGL) substantially improves mixed-reality neuronavigation (MRN) accuracy.
• Integrating BGL into MRN systems can enhance user performance and experience.
• Further validation in clinical settings and exploration of BGL in diverse MR applications are recommended.