P07.02.B Neuro-oncological augmented reality planning (NOA-p). (5th September 2022) - Van Gestel, F; Frantz, T; Neuville, Q; Klein, S; Bruneau, M; Jansen, B; Scheerlinck, T; Vandemeulebroucke, J; Duerinck, J
Abstract: Background: When preparing for the resection of an intracranial lesion, neuronavigation with a tracked pointer is most often used to determine lesion borders and the optimal approach. This can sometimes prove challenging, especially for deep-seated lesions. Augmented Reality (AR), directly displaying the lesion on the patient's skin, can simplify and improve this step. Material and Methods: We developed a system for inside-out infrared tracking that does not require an external tracking camera or external computer and allows for heads-up displaying an AR scene on the Microsoft HoloLens II. Twenty patients planned for the resection of an intracerebral lesion were included in our study. After patient registration, the lesion outlines were marked on the patient's skin by different participants, consecutively using the Brainlab neuronavigation system and the HoloLens. Each registration on both systems provided a registration transform that was compared for accuracy and consistency. The performance of the participants was measured in terms of duration and accuracy and compared to expert registration and delineation. Results: Both registration and delineation were significantly faster with AR (p=0.02 and p<0.001, respectively, and p<0.001 for the total duration), taking 79.23±17.48 and 39.58±39.10 seconds while neuronavigation required 96.61±24.54 and 90.80±44.09 seconds. AR had a registration offset of 3.3mm and 3.4°, and was more consistent compared to neuronavigation. AR facilitated more accurate and detailed lesion delineation, while neuronavigation often overestimated lesion size. Conclusion: Augmented reality provides a faster and more accurate alternative for resection planning. Lesion delineation is more intuitive while remaining high in accuracy. Future research should focus on further intraoperative implementations.