ABSTRACT
OBJECTIVE: Learning surgical skills is an essential part of neurosurgical training. Ideally, these skills are acquired to a sufficient extent in an ex vivo setting. The authors previously described an in vitro brain tumor model, consisting of a cadaveric animal brain injected with fluorescent agar-agar, for acquiring a wide range of basic neuro-oncological skills. This model focused on haptic skills such as safe tissue ablation technique and the training of fluorescence-based resection. As important didactical technologies such as mixed reality and 3D printing become more readily available, the authors developed a readily available training model that integrates the haptic aspects into a mixed reality setup. METHODS: The anatomical structures of a brain tumor patient were segmented from medical imaging data to create a digital twin of the case. Bony structures were 3D printed and combined with the in vitro brain tumor model. The segmented structures were visualized in mixed reality headsets, and the congruence of the printed and the virtual objects allowed them to be spatially superimposed. In this way, users of the system were able to train on the entire treatment process from surgery planning to instrument preparation and execution of the surgery. RESULTS: Mixed reality visualization in the joint model facilitated model (patient) positioning as well as craniotomy and the extent of resection planning respecting case-dependent specifications. The advanced physical model allowed brain tumor surgery training including skin incision; craniotomy; dural opening; fluorescence-guided tumor resection; and dura, bone, and skin closure. CONCLUSIONS: Combining mixed reality visualization with the corresponding 3D printed physical hands-on model allowed advanced training of sequential brain tumor resection skills. Three-dimensional printing technology facilitates the production of a precise, reproducible, and worldwide accessible brain tumor surgery model. The described model for brain tumor resection advanced regarding important aspects of skills training for neurosurgical residents (e.g., locating the lesion, head position planning, skull trepanation, dura opening, tissue ablation techniques, fluorescence-guided resection, and closure). Mixed reality enriches the model with important structures that are difficult to model (e.g., vessels and fiber tracts) and advanced interaction concepts (e.g., craniotomy simulations). Finally, this concept demonstrates a bridging technology toward intraoperative application of mixed reality.
