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.
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.
The use of pre-operative CT and MR images for navigation during endo-nasal skull-base endoscopic surgery is a well-established procedure in clinical practice. Fusion of CT and MR images on the endoscopic view can offer an additional advantage by directly overlaying surgical-planning information in the surgical view. Fusion of intraoperative images, such as cone beam computed tomography (CBCT), represents a step forward since these images can also account for intra-operative anatomical changes. In this work, we present a method for intra-operative CBCT image fusion on the endoscopic view for endo-nasal skull-base surgery, implemented on the Philips surgical navigation system. This is the first study which utilizes an optical tracking system (OTS) embedded in the flat-panel detector of the C-arm for endoscopic-image augmentation. In our method the OTS, co-registered in the same CBCT coordinate system, is used for tracking the endoscope. Accuracy in CBCT image registration in the endoscopic view is studied using a calibration board. Image fusion is tested in a realistic surgical scenario by using a skull phantom and inserts that mimic critical structures at the skull base. Overall performances tested on the skull phantom show a high accuracy in tracking the endoscope and registration of CBCT on endoscopic view. It can be concluded that the implemented system show potential for usage in endo-nasal skull-base surgery.