Xu, W., Wang, Y., Huang, W., & Duan, Y. (2022). An Efficient Nonlinear Mass-Spring Model for Anatomical Virtual Reality. IEEE Transactions on Instrumentation and Measurement, 71, 1–10. https://doi.org/10.1109/tim.2022.3164132
Abstract:
The visuo-haptic surgical simulator providing both visual feedback and haptic interaction is very important for various applications such as surgical simulation, training, and planning. In this paper, we develop a nonlinear mass-spring model by introducing the elastica springs, which measure the soft tissue deformation based on both spring length and curvature. As a result, our model works well on the triangular surface
meshes by producing more realistic simulations with smoother and plumper surfaces. Numerical experiments are conducted on both synthetic sphere and human liver models to demonstrate the superior performance of our method with both position based and force-based interaction. Compared to the traditional and constrained mass-spring models, our model can well balance the accuracy and efficiency, providing simulation results with biomechanical properties such as nonlinearity and incompressibility.
Furthermore, we implement the proposed model as the physical engine for a prototype of anatomical virtual reality, where realistic deformation is rendered at a refresh rate of 33 frames/s on a regular personal computer.
License type:
Publisher Copyright
Funding Info:
The work was partially supported by the Major Science and Technology
Project of Tianjin 18ZXRHSY00160, National Natural Science Foundation of China (NSFC 12071345, 11701418), and the Recruitment Program of Global Young Expert.