Purpose: The main purpose is to provide an intuitive VR-based training environment for regional anesthesia (RA). The research question is how to process subject-specific datasets, organize them in a meaningful way and how to perform the simulation for peripheral regions.
Methods: We propose a flexible virtual patient architecture and methods to process datasets. Image acquisition, image processing (especially segmentation), interactive nerve modeling and permutations (nerve instantiation) are described in detail. The simulation of electric impulse stimulation and according responses are essential for the training of peripheral RA and solved by an approach based on the electric distance.
Results: We have created an XML-based virtual patient database with several subjects. Prototypes of the simulation are implemented and run on multimodal VR hardware (e.g., stereoscopic display and haptic device). A first user pilot study has confirmed our approach.
Conclusion: The virtual patient architecture enables support for arbitrary scenarios on different subjects. This concept can also be used for other simulators. In future work, we plan to extend the simulation and conduct further evaluations in order to provide a tool for routine training for RA.