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The traditional freehand placement of an external ventricular drain (EVD) relies on empirical craniometric landmarks to guide the craniostomy and subsequent passage of the EVD catheter. The diameter and trajectory of the craniostomy physically limit the possible trajectories that can be achieved during the passage of the catheter. In this study, the authors implemented a mixed reality–guided craniostomy procedure to evaluate the benefit of an optimally drilled craniostomy to the accurate placement of the catheter. Optical marker–based tracking using an OptiTrack system was used to register the brain ventricular hologram and drilling guidance for craniostomy using a HoloLens 2 mixed reality headset. A patient-specific 3D-printed skull phantom embedded with intracranial camera sensors was developed to automatically calculate the EVD accuracy for evaluation. User trials consisted of one blind and one mixed reality–assisted craniostomy followed by a routine, unguided EVD catheter placement for each of two different drill bit sizes. A total of 49 participants were included in the study (mean age 23.4 years, 59.2% female). The mean distance from the catheter target improved from 18.6 ± 12.5 mm to 12.7 ± 11.3 mm (p = 0.0008) using mixed reality guidance for trials with a large drill bit and from 19.3 ± 12.7 mm to 10.1 ± 8.4 mm with a small drill bit (p < 0.0001). Accuracy using mixed reality was improved using a smaller diameter drill bit compared with a larger bit (p = 0.039). Overall, the majority of the participants were positive about the helpfulness of mixed reality guidance and the overall mixed reality experience. Appropriate indications and use cases for the application of mixed reality guidance to neurosurgical procedures remain an area of active inquiry. While prior studies have demonstrated the benefit of mixed reality–guided catheter placement using predrilled craniostomies, the authors demonstrate that real-time quantitative and visual feedback of a mixed reality–guided craniostomy procedure can independently improve procedural accuracy and represents an important tool for trainee education and eventual clinical implementation. 

External ventricular drain (EVD) is a common, yet challenging neurosurgical procedure of placing a catheter into the brain ventricular system that requires prolonged training for surgeons to improve the catheter placement accuracy. In this paper, we introduce NeuroLens, an Augmented Reality (AR) system that provides neurosurgeons with guidance that aides them in completing an EVD catheter placement. NeuroLens builds on prior work in AR-assisted EVD to present a registered hologram of a patient’s ventricles to the surgeons, and uniquely incorporates guidance on the EVD catheter’s trajectory, angle of insertion, and distance to the target. The guidance is enabled by tracking the EVD catheter. We evaluate NeuroLens via a study with 33 medical students, in which we analyzed students’ EVD catheter insertion accuracy and completion time, eye gaze patterns, and qualitative responses. Our study, in which NeuroLens was used to aid students in inserting an EVD catheter into a realistic phantom model of a human head, demonstrated the potential of NeuroLens as a tool that will aid and educate novice neurosurgeons. On average, the use of NeuroLens improved the EVD placement accuracy of year 1 students by 39.4% and of the year 2−4 students by 45.7%. Furthermore, students who focused more on NeuroLens-provided contextual guidance achieved better results. 

We prove that for every n ≥ 3 the sharp upper bound for the dimension of the symmetry groups of homogeneous, 2-nondegenerate, (2n+1)-dimensional CR manifolds of hypersurface type with a 1-dimensional Levi kernel is equal to n^2+7, and simultaneously establish the same result for a more general class of structures characterized by weakening the homogeneity condition. This supports Beloshapka’s conjecture stating that hypersurface models with a maximal finite-dimensional group of symmetries for a given dimension of the underlying manifold are Levi nondegenerate.