A patient-specific, interactive, multiuser, online mixed-reality neurosurgical training and planning system.

OBJECTIVE: Mixed-reality simulation is an emerging tool for creating anatomical models for preoperative planning. Its use in neurosurgical training (NT) has been limited because of the difficulty in real-time interactive teaching. This study describes the development of a patient-specific, interactive mixed-reality NT system. The authors took cases of intracranial tumor resection or neurovascular compression (NVC) as examples to verify the technical feasibility and efficacy of the mixed-reality NT system for residents' training and preoperative planning. METHODS: This study prospectively enrolled 40 patients who suffered from trigeminal neuralgia, hemifacial spasms, or intracranial tumors. The authors used a series of software programs to process the multimodal imaging data, followed by uploading the holographic models online. They used a HoloLens or a standard iOS device to download and display the holographic models for training. Ten neurosurgical residents with different levels of surgical experience were trained with this mixed-reality NT system. Change in surgical strategy was recorded, and a questionnaire survey was conducted to evaluate the efficacy of the mixed-reality NT system. RESULTS: The system allows the trainer and trainee to view the mixed-reality model with either a HoloLens or an iPad/iPhone simultaneously online at different locations. Interactive manipulation and instant updates were able to be achieved during training. A clinical efficacy validation test was conducted. The surgeons changed their exploration strategy in 48.3% of the NVC cases. For residents with limited experience in surgery, the exploration strategy for 75.0% of all patients with NVC was changed after the residents were trained with the mixed-reality NT system. Of the 60 responses for intracranial tumors, the trainee changed the surgical posture in 19 (31.7%) cases. The change of the location (p = 0.0338) and size (p = 0.0056) of craniotomy are significantly related to the experience of the neurosurgeons. CONCLUSIONS: The mixed-reality NT system is available for local or real-time remote neurosurgical resident training. It may effectively help neurosurgeons in patient-specific training and planning of surgery for cases of NVC and intracranial tumor. The authors expect the system to have a broader application in neurosurgery in the near future.

The Feasibility and Accuracy of Holographic Navigation with Laser Crosshair Simulator Registration on a Mixed-Reality Display.

Addressing conventional neurosurgical navigation systems' high costs and complexity, this study explores the feasibility and accuracy of a simplified, cost-effective mixed reality navigation (MRN) system based on a laser crosshair simulator (LCS). A new automatic registration method was developed, featuring coplanar laser emitters and a recognizable target pattern. The workflow was integrated into Microsoft's HoloLens-2 for practical application. The study assessed the system's precision by utilizing life-sized 3D-printed head phantoms based on computed tomography (CT) or magnetic resonance imaging (MRI) data from 19 patients (female/male: 7/12, average age: 54.4 ± 18.5 years) with intracranial lesions. Six to seven CT/MRI-visible scalp markers were used as reference points per case. The LCS-MRN's accuracy was evaluated through landmark-based and lesion-based analyses, using metrics such as target registration error (TRE) and Dice similarity coefficient (DSC). The system demonstrated immersive capabilities for observing intracranial structures across all cases. Analysis of 124 landmarks showed a TRE of 3.0 ± 0.5 mm, consistent across various surgical positions. The DSC of 0.83 ± 0.12 correlated significantly with lesion volume (Spearman rho = 0.813, p < 0.001). Therefore, the LCS-MRN system is a viable tool for neurosurgical planning, highlighting its low user dependency, cost-efficiency, and accuracy, with prospects for future clinical application enhancements.

Head model dataset for mixed reality navigation in neurosurgical interventions for intracranial lesions.

Mixed reality navigation (MRN) technology is emerging as an increasingly significant and interesting topic in neurosurgery. MRN enables neurosurgeons to "see through" the head with an interactive, hybrid visualization environment that merges virtual- and physical-world elements. Offering immersive, intuitive, and reliable guidance for preoperative and intraoperative intervention of intracranial lesions, MRN showcases its potential as an economically efficient and user-friendly alternative to standard neuronavigation systems. However, the clinical research and development of MRN systems present challenges: recruiting a sufficient number of patients within a limited timeframe is difficult, and acquiring low-cost, commercially available, medically significant head phantoms is equally challenging. To accelerate the development of novel MRN systems and surmount these obstacles, the study presents a dataset designed for MRN system development and testing in neurosurgery. It includes CT and MRI data from 19 patients with intracranial lesions and derived 3D models of anatomical structures and validation references. The models are available in Wavefront object (OBJ) and Stereolithography (STL) formats, supporting the creation and assessment of neurosurgical MRN applications.