Background: Neurosurgical procedures are complex and require years of training and experience. Traditional training on human cadavers is expensive, requires facilities and planning, and raises ethical concerns. Therefore, the use of anthropomorphic phantoms could be an excellent substitute. The aim of the study was to design and develop a patient-specific 3D-skull and brain model with realistic CT-attenuation suitable for conventional and augmented reality (AR)-navigated neurosurgical simulations. Methods: The radiodensity of materials considered for the skull and brain phantoms were investigated using cone beam CT (CBCT) and compared to the radiodensities of the human skull and brain. The mechanical properties of the materials considered were tested in the laboratory and subsequently evaluated by clinically active neurosurgeons. Optimization of the phantom for the intended purposes was performed in a feedback cycle of tests and improvements. Results: The skull, including a complete representation of the nasal cavity and skull base, was 3D printed using polylactic acid with calcium carbonate. The brain was cast using a mixture of water and coolant, with 4 wt% polyvinyl alcohol and 0.1 wt% barium sulfate, in a mold obtained from segmentation of CBCT and T1 weighted MR images from a cadaver. The experiments revealed that the radiodensities of the skull and brain phantoms were 547 and 38 Hounsfield units (HU), as compared to real skull bone and brain tissues with values of around 1300 and 30 HU, respectively. As for the mechanical properties testing, the brain phantom exhibited a similar elasticity to real brain tissue. The phantom was subsequently evaluated by neurosurgeons in simulations of endonasal skull-base surgery, brain biopsies, and external ventricular drain (EVD) placement and found to fulfill the requirements of a surgical phantom. Conclusions: A realistic and CT-compatible anthropomorphic head phantom was designed and successfully used for simulated augmented reality-led neurosurgical procedures. The anatomic details of the skull base and brain were realistically reproduced. This phantom can easily be manufactured and used for surgical training at a low cost. @en

Glial tumors grow diffusely in the brain. Survival is correlated to the extent of tumor removal, but tumor borders are often invisible. Resection beyond the borders as defined by conventional methods may further improve prognosis. In this proof-of-concept study, we evaluate diffuse reflectance spectroscopy (DRS) for discrimination between glial tumors and normal brain ex vivo. DRS spectra and histology were acquired from 22 tumor samples and nine brain tissue samples retrieved from 30 patients. The content of biological chromophores and scattering features were estimated by fitting a model derived from diffusion theory to the DRS spectra. DRS parameters differed significantly between tumor and normal brain tissue. Classification using random forest yielded a sensitivity and specificity for the detection of low-grade gliomas of 82.0% and 82.7%, respectively, and the area under curve (AUC) was 0.91. Applied in a hand-held probe or biopsy needle, DRS has the potential to provide intra-operative tissue analysis.

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OBJECTIVE The aim of this study was to evaluate the accuracy (deviation from the target or intended path) and efficacy (insertion time) of an augmented reality surgical navigation (ARSN) system for insertion of biopsy needles and external ventricular drains (EVDs), two common neurosurgical procedures that require high precision. METHODS The hybrid operating room-based ARSN system, comprising a robotic C-arm with intraoperative conebeam CT (CBCT) and integrated video tracking of the patient and instruments using nonobtrusive adhesive optical markers, was used. A 3D-printed skull phantom with a realistic gelatinous brain model containing air-filled ventricles and 2-mm spherical biopsy targets was obtained. After initial CBCT acquisition for target registration and planning, ARSN was used for 30 cranial biopsies and 10 EVD insertions. Needle positions were verified by CBCT. RESULTS The mean accuracy of the biopsy needle insertions (n = 30) was 0.8 mm ± 0.43 mm. The median path length was 39 mm (range 16-104 mm) and did not correlate to accuracy (p = 0.15). The median device insertion time was 149 seconds (range 87-233 seconds). The mean accuracy for the EVD insertions (n = 10) was 2.9 mm ± 0.8 mm at the tip with a 0.7° ± 0.5° angular deviation compared with the planned path, and the median insertion time was 188 seconds (range 135-400 seconds). CONCLUSIONS This study demonstrated that ARSN can be used for navigation of percutaneous cranial biopsies and EVDs with high accuracy and efficacy.

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Neurosurgical training is performed on human cadavers and simulation models, such as VR platforms, which have several drawbacks. Head phantoms could solve most of the issues related to these trainings. The aim of this study was to design a realistic and CT-compatible head phantom, with a specific focus on endo-nasal skull-base surgery and brain biopsy. A head phantom was created by segmenting an image dataset from a cadaver. The skull, which includes a complete structure of the nasal cavity and detailed skull-base anatomy, is 3D printed using PLA with calcium, while the brain is produced using a PVA mixture. The radiodensity and mechanical properties of the phantom were tested and adjusted in material choice to mimic real-life conditions. Surgeons find the skull, the structures at the skull-base and the brain realistically reproduced. The head phantom can be employed for neurosurgical education, training and surgical planning, and can be successfully used for simulating surgeries.

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OBJECTIVE: Surgical navigation is a well-established tool in endoscopic skull base surgery. However, navigational and endoscopic views are usually displayed on separate monitors, forcing the surgeon to focus on one or the other. Aiming to provide real-time integration of endoscopic and diagnostic imaging information, we present a new navigation technique based on augmented reality with fusion of intraoperative cone beam computed tomography (CBCT) on the endoscopic view. The aim of this study was to evaluate the accuracy of the method. MATERIAL AND METHODS: An augmented reality surgical navigation system (ARSN) with 3D CBCT capability was used. The navigation system incorporates an optical tracking system (OTS) with four video cameras embedded in the flat detector of the motorized C-arm. Intra-operative CBCT images were fused with the view of the surgical field obtained by the endoscope's camera. Accuracy of CBCT image co-registration was tested using a custom-made grid with incorporated 3D spheres. RESULTS: Co-registration of the CBCT image on the endoscopic view was performed. Accuracy of the overlay, measured as mean target registration error (TRE), was 0.55 mm with a standard deviation of 0.24 mm and with a median value of 0.51mm and interquartile range of 0.39--0.68 mm. CONCLUSION: We present a novel augmented reality surgical navigation system, with fusion of intraoperative CBCT on the endoscopic view. The system shows sub-millimeter accuracy.

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In neurosurgery, technical solutions for visualizing the border between healthy brain and tumor tissue is of great value, since they enable the surgeon to achieve gross total resection while minimizing the risk of damage to eloquent areas. By using real-time non-ionizing imaging techniques, such as hyperspectral imaging (HSI), the spectral signature of the tissue is analyzed allowing tissue classification, thereby improving tumor boundary discrimination during surgery. More particularly, since infrared penetrates deeper in the tissue than visible light, the use of an imaging sensor sensitive to the near-infrared wavelength range would also allow the visualization of structures slightly beneath the tissue surface. This enables the visualization of tumors and vessel boundaries prior to surgery, thereby preventing the damaging of tissue structures. In this study, we investigate the use of Diffuse Reflectance Spectroscopy (DRS) and HSI for brain tissue classification, by extracting spectral features from the near infra-red range. The applied method for classification is the linear Support Vector Machine (SVM). The study is conducted on ex-vivo porcine brain tissue, which is analyzed and classified as either white or gray matter. The DRS combined with the proposed classification reaches a sensitivity and specificity of 96%, while HSI reaches a sensitivity of 95% and specificity of 93%. This feasibility study shows the potential of DRS and HSI for automated tissue classification, and serves as a fjrst step towards clinical use for tumor detection deeper inside the tissue.

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