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 computationally 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 local 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 efficiency of the explicit integration.

CONCLUSION: The proposed method can achieve stable soft tissue deformation with efficiency of explicit integration for surgical simulation.