BACKGROUND AND OBJECTIVES: Mixed reality (MxR) benefits neurosurgery by improving anatomic visualization, surgical planning and training. We aim to validate the usability of a dedicated certified system for this purpose. METHODS: All cases prepared with MxR in our center in 2022 were prospectively collected. Holographic rendering was achieved using an incorporated fully automatic algorithm in the MxR application, combined with contrast-based semiautomatic rendering and/or manual segmentation where necessary. Hologram segmentation times were documented. Visualization during surgical preparation (defined as the interval between finalized anesthesiological induction and sterile draping) was performed using MxR glasses and direct streaming to a side screen. Surgical preparation times were compared with a matched historical cohort of 2021. Modifications of the surgical approach after 3-dimensional (3D) visualization were noted. Usability was assessed by evaluating 7 neurosurgeons with more than 3 months of experience with the system using a Usefulness, Satisfaction and Ease of use (USE) questionnaire. RESULTS: One hundred-seven neurosurgical cases prepared with a 3D hologram were collected. Surgical indications were oncologic (63/107, 59%), cerebrovascular (27/107, 25%), and carotid endarterectomy (17/107, 16%). Mean hologram segmentation time was 39.4 ± 20.4 minutes. Average surgical preparation time was 48.0 ± 17.3 minutes for MxR cases vs 52 ± 17 minutes in the matched 2021 cohort without MxR (mean difference 4, 95% CI 1.7527-9.7527). Based on the 3D hologram, the surgical approach was modified in 3 cases. Good usability was found by 57% of the users. CONCLUSION: The perioperative use of 3D holograms improved direct anatomic visualization while not significantly increasing intraoperative surgical preparation time. Usability of the system was adequate. Further technological development is necessary to improve the automatic algorithms and reduce the preparation time by circumventing manual and semiautomatic segmentation. Future studies should focus on quantifying the potential benefits in teaching, training, and the impact on surgical and functional outcomes.

Background: Precise preoperative anatomical visualization and understanding of an intracranial aneurysm (IA) are fundamental for surgical planning and increased intraoperative confidence. Application of virtual reality (VR) and mixed reality (MR), thus three-dimensional (3D) visualization of IAs could be significant in surgical planning. Authors provide an up-to-date overview of VR and MR applied to IA surgery, with specific focus on tailoring of the surgical treatment.Methods: A systematic analysis of the literature was performed in accordance with the PRISMA guidelines. Pubmed, and Embase were searched to identify studies reporting use of MR and VR 3D visualization in IA surgery during the last 25 years. Type and number of IAs, category of input scan, visualization techniques (screen, glasses or head set), inclusion of haptic feedback, tested population (residents, fellows, attending neurosurgeons), and aim of the study (surgical planning/rehearsal, neurosurgical training, methodological validation) were noted.Results: Twenty-eight studies were included. Eighteen studies (64.3%) applied VR, and 10 (35.7%) used MR. A positive impact on surgical planning was documented by 19 studies (67.9%): 17 studies (60.7%) chose the tailoring of the surgical approach as primary outcome of the analysis. A more precise anatomical visualization and understanding with VR and MR was endorsed by all included studies (100%).Conclusion: Application of VR and MR to perioperative 3D visualization of IAs allowed an improved understanding of the patient-specific anatomy and surgical preparation. This review describes a tendency to utilize mostly VR-platforms, with the primary goals of a more accurate anatomical understanding, surgical planning and rehearsal.

Endovascular intervention for acute ischaemic stroke care is mostly performed in older patients, often with unfavourable aortic and supra-aortic anatomy, as well as cardiovascular comorbidities. A significant subset of them may benefit from transcervical access as the initial approach for mechanical thrombectomy. In fact, direct carotid artery puncture in these cases has the advantage to bypass the anatomical obstacles and achieve faster reperfusion. Caution is advised when common carotid artery access is pursued in order to avoid adverse events, including haematoma formation, iatrogenic arterial dissection and sheath kinking. In spite of potential complications, direct carotid puncture in acute ischaemic stroke intervention overcomes challenging angioarchitecture and may reduce the rate of poor clinical outcomes associated with delayed revascularisation in certain cases.

Background: Visualization, analysis and characterization of the angioarchitecture of a brain arteriovenous malformation (bAVM) present crucial steps for understanding and management of these complex lesions. Three-dimensional (3D) segmentation and 3D visualization of bAVMs play hereby a significant role. We performed a systematic review regarding currently available 3D segmentation and visualization techniques for bAVMs. Methods: PubMed, Embase and Google Scholar were searched to identify studies reporting 3D segmentation techniques applied to bAVM characterization. Category of input scan, segmentation (automatic, semiautomatic, manual), time needed for segmentation and 3D visualization techniques were noted. Results: Thirty-three studies were included. Thirteen (39%) used MRI as baseline imaging modality, 9 used DSA (27%), and 7 used CT (21%). Segmentation through automatic algorithms was used in 20 (61%), semiautomatic segmentation in 6 (18%), and manual segmentation in 7 (21%) studies. Median automatic segmentation time was 10 min (IQR 33), semiautomatic 25 min (IQR 73). Manual segmentation time was reported in only one study, with the mean of 5–10 min. Thirty-two (97%) studies used screens to visualize the 3D segmentations outcomes and 1 (3%) study utilized a heads-up display (HUD). Integration with mixed reality was used in 4 studies (12%). Conclusions: A golden standard for 3D visualization of bAVMs does not exist. This review describes a tendency over time to base segmentation on algorithms trained with machine learning. Unsupervised fuzzy-based algorithms thereby stand out as potential preferred strategy. Continued efforts will be necessary to improve algorithms, integrate complete hemodynamic assessment and find innovative tools for tridimensional visualization.

Revascularization surgery for the symptomatic hemisphere with hemodynamic impairment is effective for Moyamoya vasculopathy patients. However, careful patient selection is crucial and ideally supported by advanced quantitative hemodynamic imaging. Recently, blood oxygenation level-dependent cerebrovascular reactivity (BOLD-CVR) and quantitative magnetic resonance angiography with non-invasive optimal vessel analysis (qMRA-NOVA) have gained prominence in assessing these patients. This study aims to present the results of BOLD-CVR and qMRA-NOVA imaging along with the changes in cerebral hemodynamics and flow status following flow augmentation with superficial temporal artery–middle cerebral artery (STA-MCA) bypass in our Moyamoya vasculopathy patient cohort. Symptomatic patients with Moyamoya vasculopathy treated at the Clinical Neuroscience Center of the University Hospital Zurich who underwent hemodynamic and flow imaging (BOLD-CVR and qMRA-NOVA) before and after bypass were included in the analysis. Reduced hemispheric volume flow rates, as well as impaired BOLD-CVR, were measured in all 12 patients with Moyamoya vasculopathy before STA-MCA bypass surgery. Following the surgical procedure, post-operative BOLD-CVR demonstrated a non-significant increase in BOLD-CVR values within the revascularized, symptomatic middle cerebral artery territory and cerebral hemisphere. The results of the statistical tests should be viewed as indicative due to the small sample size. Additionally, post-operative qMRA-NOVA revealed a significant improvement in the hemispheric volume flow rate of the affected hemisphere due to the additional bypass flow rate. Our findings affirm the presence of hemodynamic and flow impairments in the symptomatic hemisphere of the Moyamoya vasculopathy patients. Bypass surgery proves effective in improving both BOLD-CVR impairment and the hemispheric volume flow rate in our patient cohort.

BACKGROUND AND PURPOSE: To develop a new machine-learning algorithm for fully automatic identification of cerebral arteries and intracranial aneurysms (IAs) based on a manually segmented magnetic resonance imaging with time-of-flight sequences (MRITOF) dataset. MATERIALS AND METHODS: In this retrospective single-center study, 62 MRI-TOF scans of a total of 73 untreated unruptured IAs were manually color-labelled in 21 classes. A nnUNet architecture was trained on MRI-TOF images. The performance of the automatic segmentation was compared with the manual segmentation using Dice Similarity Coefficient (DSC), Centerline Dice (ClDice) and 95th percentile Hausdorff Distance (HD95). Sensitivity was computed for aneurysm detection. RESULTS: Across all 21 classes, the median DSC was 0.86 [95CI: 0.81, 0.89], the median ClDice 0.91 [0.85, 0.94] and the median HD95 was 2.9 [1.0, 14.9] mm. Sensitivity of the model for aneurysms detection was 0.8. For this class specifically, a median DSC of 0.88 [0.13, 0.92], median ClDice of 0.89 [0.06, 1.0] and median HD95 of 1.8 [0.58, 81] mm was achieved. The volume of the labelled anatomical structure was the most relevant determinant of accuracy in this model. Median time to predict was 130.6 [60.9, 284.1] seconds. CONCLUSIONS: The nnUNet MRI-TOF based algorithm provided a fast and adequate automatic extraction of unruptured intracranial aneurysms, their parent vessels and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRI-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction. ABBREVIATIONS: IA = Intracranial Aneurysm; MRI-TOF＝ Magnetic Resonance Imaging – Time of Flight; DSC = Dice-Sørenson Coefficient; ClDice = Centerline Dice; HD95 = 95^th Percentile Hausdorff Distance.