Multiuser virtual reality environment for visualising neuroimaging data

• Main Author: David W. Shattuck
• Format: Article
• Language: English
• Published: Wiley 2018-10-01
• Series: Healthcare Technology Letters
• Subjects: brain; biomedical MRI; medical image processing; virtual reality; rendering (computer graphics); neurophysiology; data visualisation; neuroimaging data; high-performance consumer virtual reality systems; MRI volumes; neuroanatomical surface models; text-based annotations; OpenVR software development kit; virtual space; multiuser virtual reality environment; medical imaging; diffusion tensors; streamline tractography; HTC Vive; OpenGL; fibre track selection; automated brain MRI analysis packages; Vive controllers
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/htl.2018.5077

The recent advent of high-performance consumer virtual reality (VR) systems has opened new possibilities for immersive visualisation of numerous types of data. Medical imaging has long made use of advanced visualisation techniques, and VR offers exciting new opportunities for data exploration. The author presents a new framework for interacting with neuroimaging data, including MRI volumes, neuroanatomical surface models, diffusion tensors, and streamline tractography, as well as text-based annotations. The system was developed for the HTC Vive using C++, OpenGL, and the OpenVR software development kit. The author developed custom GLSL shaders for each type of data to provide high-performance real-time rendering suitable for use in a VR environment. These are integrated with an interface that enables the user to manipulate the scene through the Vive controllers and perform operations such as volume slicing, fibre track selection, and structural queries. The software can read data generated by existing automated brain MRI analysis packages, enabling the rapid development of subject-specific visualisations of multimodal data or annotated atlases. The system can also support multiple simultaneous users, placing them in the same virtual space to interact with each other while visualising the same datasets, opening new possibilities for teaching and for collaborative exploration of neuroimaging data.