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

This study aimed to rigorously assess the accuracy of mixed-reality neuronavigation (MRN) in comparison with magnetic neuronavigation (MN) through a comprehensive phantom-based experiment. It introduces a novel dimension by examining the influence of blue-green light (BGL) on MRN accuracy, a previously unexplored avenue in this domain. Twenty-nine phantoms, each meticulously marked with 5-6 fiducials, underwent CT scans as part of the navigation protocol. A 3D model was then superimposed onto a 3D-printed plaster skull using a semiautomatic registration process. The study meticulously evaluated the accuracy of both navigation techniques by pinpointing specific markers on the plaster surface. Precise measurements were then taken using digital calipers, with navigation conducted under three distinct lighting conditions: indirect white light (referred to as no light [NL]), direct white light (WL), and BGL. The research enlisted two operators with distinct levels of experience, one senior and one junior, to ensure a comprehensive analysis. The study was structured into two distinct experiments (experiment 1 [MN] and experiment 2 [MRN]) conducted by the two operators. Data analysis focused on calculating average and median values within subgroups, considering variables such as the type of lighting, precision, and recording time. In experiment 1, no statistically significant differences emerged between the two operators. However, in experiment 2, notable disparities became apparent, with the senior operator recording longer times but achieving higher precision. Most significantly, BGL consistently demonstrated a capacity to enhance accuracy in MRN across both experiments. This study demonstrated the substantial positive influence of BGL on MRN accuracy, providing profound implications for the design and implementation of mixed-reality systems. It also emphasized that integrating BGL into mixed-reality environments could profoundly improve user experience and performance. Further research is essential to validate these findings in real-world settings and explore the broader potential of BGL in a variety of mixed-reality applications.