BACKGROUND: The safe performance of regional anaesthesia (RA) requires theoretical knowledge and good manual skills. Virtual reality (VR)-based simulators may offer trainees a safe environment to learn and practice different techniques. However, currently available VR simulators do not consider individual anatomy, which limits their use for realistic training. We have developed a VR-based simulator that can be used for individual anatomy and for different anatomical regions. METHODS: Individual data were obtained from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) without contrast agent to represent morphology and the vascular system, respectively. For data handling, registration, and segmentation, an application based on the Medical Imaging Interaction Toolkit was developed. Suitable segmentation algorithms such as the fuzzy c-means clustering approach were integrated, and a hierarchical tree data structure was created to model the flexible anatomical structures of peripheral nerve cords. The simulator was implemented in the VR toolkit ViSTA using modules for collision detection, virtual humanoids, interaction, and visualization. A novel algorithm for electric impulse transmission is the core of the simulation. RESULTS: In a feasibility study, MRI morphology and MRA were acquired from five subjects for the inguinal region. From these sources, three-dimensional anatomical data sets were created and nerves modelled. The resolution obtained from both MRI and MRA was sufficient for realistic simulations. Our high-fidelity simulator application allows trainees to perform virtual peripheral nerve blocks based on these data sets and models. CONCLUSIONS: Subject-specific training of RA is supported in a virtual environment. We have adapted segmentation algorithms and developed a VR-based simulator for the inguinal region for use in training for different peripheral nerve blocks. In contrast to available VR-based simulators, our simulation offers anatomical variety.
BACKGROUND: The safe performance of regional anaesthesia (RA) requires theoretical knowledge and good manual skills. Virtual reality (VR)-based simulators may offer trainees a safe environment to learn and practice different techniques. However, currently available VR simulators do not consider individual anatomy, which limits their use for realistic training. We have developed a VR-based simulator that can be used for individual anatomy and for different anatomical regions. METHODS: Individual data were obtained from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) without contrast agent to represent morphology and the vascular system, respectively. For data handling, registration, and segmentation, an application based on the Medical Imaging Interaction Toolkit was developed. Suitable segmentation algorithms such as the fuzzy c-means clustering approach were integrated, and a hierarchical tree data structure was created to model the flexible anatomical structures of peripheral nerve cords. The simulator was implemented in the VR toolkit ViSTA using modules for collision detection, virtual humanoids, interaction, and visualization. A novel algorithm for electric impulse transmission is the core of the simulation. RESULTS: In a feasibility study, MRI morphology and MRA were acquired from five subjects for the inguinal region. From these sources, three-dimensional anatomical data sets were created and nerves modelled. The resolution obtained from both MRI and MRA was sufficient for realistic simulations. Our high-fidelity simulator application allows trainees to perform virtual peripheral nerve blocks based on these data sets and models. CONCLUSIONS: Subject-specific training of RA is supported in a virtual environment. We have adapted segmentation algorithms and developed a VR-based simulator for the inguinal region for use in training for different peripheral nerve blocks. In contrast to available VR-based simulators, our simulation offers anatomical variety.
Authors: Sebastian Ullrich; Oliver Grottke; Eduard Fried; Thorsten Frommen; Wei Liao; Rolf Rossaint; Torsten Kuhlen; Thomas M Deserno Journal: Int J Comput Assist Radiol Surg Date: 2009-06-13 Impact factor: 2.924