| Summary: | This paper presents a new methodology for soft tissue deformation by using neural dynamics. This methodology combines neural propagation of mechanical load and non-rigid mechanics of motion to govern the dynamics of soft tissue deformation. The mechanical load applied to a soft tissue to cause a deformation is treated as the input of neural network and distributed among mass points of the soft tissue according to neural dynamics. A Hopfield neural network model is developed to describe the distribution of the mechanical load in the tissue. Methods are established for construction of the neural network model on a 3D tissue surface and for derivation of internal forces from the distribution of the mechanical load. Experiments have been conducted, demonstrating that the proposed methodology cannot only deal with large-range deformation, but it can also accommodate isotropic, anisotropic and inhomogeneous materials by simply modifying the control coefficient.
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