A new diamond-based variable spring-mass model has been proposed in this study. It can realize the deformation simulation for different organs by changing the length of the springs, spring coefficient and initial angle. The virtual spring joined in the model is used to provide constraint and to avoid hyperelastic phenomenon when excessive force appears. It is also used for the calculation of force feedback in the deformation process. With the deformation force feedback algorithm, we calculated the deformation area of each layer through screening effective particles, and contacted the deformation area with the force. This simplified the force feedback algorithm of traditional spring-particle model. The deformation simulation was realized by the PHANTOM haptic interaction devices based on this model. The experimental results showed that the model had the advantage of simple structure and of being easy to implement. The deformation force feedback algorithm reduces the number of the deformation calculation, improves the real-time deformation and has a more realistic deformation effect.
Mass point-spring model is one of the commonly used models in virtual surgery. However, its model parameters have no clear physical meaning, and it is hard to set the parameter conveniently. We, therefore, proposed a method based on genetic algorithm to determine the mass-spring model parameters. Computer-aided tomography (CAT) data were used to determine the mass value of the particle, and stiffness and damping coefficient were obtained by genetic algorithm. We used the difference between the reference deformation and virtual deformation as the fitness function to get the approximate optimal solution of the model parameters. Experimental results showed that this method could obtain an approximate optimal solution of spring parameters with lower cost, and could accurately reproduce the effect of the actual deformation model as well.
Polyvinyl alcohol (PVA) hydrogel was made for simulating human's soft tissue in our experiment. The image acquisition device is composed of an optical platform, a camera and its bracket and a light source. In order to study the law of soft tissue deformation under flexible needle insertion, markers were embedded into the soft tissue and their displacements were recorded. Based on the analysis of displacements of markers in X direction and Y direction, back propagation (BP) neural network was employed to model the displacement of Y direction for the markers. Compared to the experimental data, fitting degree of the neural network model was above 95%, the maximum relative error for valid data was limited to 30%, and the maximum absolute error was 0.8 mm. The BP neural network model was beneficial for predicting soft tissue deformation quantitatively. The results showed that the model could effectively improve the accuracy of flexible needle insertion into soft tissue.