Cellular neural network modelling of soft tissue dynamics for surgical simulation

Background: Currently, the mechanical dynamics of soft tissue deformation is achieved by numerical time integrations such as the explicit or implicit integration; however, the explicit integration is stable only under a small time step, whereas the implicit integration is computa-tionally expensive in spite of the accommodation of a large time step. Objective: This paper presents a cellular neural network method for stable simulation of soft tissue deformation dynamics. Method: The non-rigid motion equation is formulated as a cellular neural network with lo-cal connectivity of cells, and thus the dynamics of soft tissue deformation is transformed into the neural dynamics of the cellular neural network. Results: Results show that the proposed method can achieve good accuracy at a small time step. It still remains stable at a large time step, while maintaining the computational effi-ciency of the explicit integration. Conclusion: The proposed method can achieve stable soft tissue deformation with effi-ciency of explicit integration for surgical simulation.