Real-time surgical simulation is becoming an important component of surgical training. To meet the realtime
requirement, however, the accuracy of the biomechancial modeling of soft tissue is often compromised due
to computing resource constraints. Furthermore, haptic integration presents an additional challenge with its
requirement for a high update rate. As a result, most real-time surgical simulation systems employ a linear
elasticity model, simplified numerical methods such as the boundary element method or spring-particle systems,
and coarse volumetric meshes. However, these systems are not clinically realistic. We present here an ongoing
work aimed at developing an efficient and physically realistic neurosurgery simulator using a non-linear
finite element method (FEM) with haptic interaction. Real-time finite element analysis is achieved by utilizing
the total Lagrangian explicit dynamic (TLED) formulation and GPU acceleration of per-node and per-element
operations. We employ a virtual coupling method for separating deformable body simulation and collision
detection from haptic rendering, which needs to be updated at a much higher rate than the visual simulation.
The system provides accurate biomechancial modeling of soft tissue while retaining a real-time performance with
haptic interaction. However, our experiments showed that the stability of the simulator depends heavily on the
material property of the tissue and the speed of colliding objects. Hence, additional efforts including dynamic
relaxation are required to improve the stability of the system.