BACKGROUND As current augmented-reality (AR) smart glasses are self-contained, powerful computers that project 3-dimensional holograms that can maintain their position in physical space, they could theoretically be used as a low-cost, stand-alone neuronavigation system. OBJECTIVE To determine feasibility and accuracy of holographic neuronavigation (HN) using AR smart glasses. METHODS We programmed a fully functioning neuronavigation system on commercially available smart glasses (HoloLens®, Microsoft, Redmond, Washington) and tested its accuracy and feasibility in the operating room. The fiducial registration error (FRE) was measured for both HN and conventional neuronavigation (CN) (Brainlab, Munich, Germany) by using point-based registration on a plastic head model. Subsequently, we measured HN and CN FRE on 3 patients. RESULTS A stereoscopic view of the holograms was successfully achieved in all experiments. In plastic head measurements, the mean HN FRE was 7.2 ± 1.8 mm compared to the mean CN FRE of 1.9 ± 0.45 (mean difference: –5.3 mm; 95% confidence interval [CI]: –6.7 to –3.9). In the 3 patients, the mean HN FRE was 4.4 ± 2.5 mm compared to the mean CN FRE of 3.6 ± 0.5 (mean difference: –0.8 mm; 95% CI: –3.0 to 4.6). CONCLUSION Owing to the potential benefits and promising results, we believe that HN could eventually find application in operating rooms. However, several improvements will have to be made before the device can be used in clinical practice.
OBJECTIVE For currently available augmented reality workflows, 3D models need to be created with manual or semiautomatic segmentation, which is a time-consuming process. The authors created an automatic segmentation algorithm that generates 3D models of skin, brain, ventricles, and contrast-enhancing tumor from a single T1-weighted MR sequence and embedded this model into an automatic workflow for 3D evaluation of anatomical structures with augmented reality in a cloud environment. In this study, the authors validate the accuracy and efficiency of this automatic segmentation algorithm for brain tumors and compared it with a manually segmented ground truth set. METHODS Fifty contrast-enhanced T1-weighted sequences of patients with contrast-enhancing lesions measuring at least 5 cm3 were included. All slices of the ground truth set were manually segmented. The same scans were subsequently run in the cloud environment for automatic segmentation. Segmentation times were recorded. The accuracy of the algorithm was compared with that of manual segmentation and evaluated in terms of Sørensen-Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and 95th percentile of Hausdorff distance (HD95). RESULTS The mean ± SD computation time of the automatic segmentation algorithm was 753 ± 128 seconds. The mean ± SD DSC was 0.868 ± 0.07, ASSD was 1.31 ± 0.63 mm, and HD95 was 4.80 ± 3.18 mm. Meningioma (mean 0.89 and median 0.92) showed greater DSC than metastasis (mean 0.84 and median 0.85). Automatic segmentation had greater accuracy for measuring DSC (mean 0.86 and median 0.87) and HD95 (mean 3.62 mm and median 3.11 mm) of supratentorial metastasis than those of infratentorial metastasis (mean 0.82 and median 0.81 for DSC; mean 5.26 mm and median 4.72 mm for HD95). CONCLUSIONS The automatic cloud-based segmentation algorithm is reliable, accurate, and fast enough to aid neurosurgeons in everyday clinical practice by providing 3D augmented reality visualization of contrast-enhancing intracranial lesions measuring at least 5 cm3. The next steps involve incorporation of other sequences and improving accuracy with 3D fine-tuning in order to expand the scope of augmented reality workflow.
Background Holographic neuronavigation has several potential advantages compared to conventional neuronavigation systems. We present the first report of a holographic neuronavigation system with patient-to-image registration and patient tracking with a reference array using an augmented reality head-mounted display (AR-HMD). Methods Three patients undergoing an intracranial neurosurgical procedure were included in this pilot study. The relevant anatomy was first segmented in 3D and then uploaded as holographic scene in our custom neuronavigation software. Registration was performed using point-based matching using anatomical landmarks. We measured the fiducial registration error (FRE) as the outcome measure for registration accuracy. A custom-made reference array with QR codes was integrated in the neurosurgical setup and used for patient tracking after bed movement. Results Six registrations were performed with a mean FRE of 8.5 mm. Patient tracking was achieved with no visual difference between the registration before and after movement. Conclusions This first report shows a proof of principle of intraoperative patient tracking using a standalone holographic neuronavigation system. The navigation accuracy should be further optimized to be clinically applicable. However, it is likely that this technology will be incorporated in future neurosurgical workflows because the system improves spatial anatomical understanding for the surgeon.
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