Research on Real-time Simulation Method of Vascular Interventional Surgery Based on Model Order Reduction

In the vascular interventional surgery virtual simulation training system, there is a high demand for the real-time nature of the simulation. However, the amount of storage and calculation required by the current system is very large. We need some methods that can increase the calculation rate and speed up the system simulation rate. In this study, for the existing simulation training system, we improved the two parts of the blood vessel local simulation and the entire system simulation. First, the blood vessel is simulated and modeled using a model-based order reduction method. After a set of offline simulations based on the entire model, eigen-orthogonal decomposition is applied to greatly reduce the number of states of the robot model, and super-reduction is used to perform integration on the simplified domain. Then, a multi-threaded parallel operation method is adopted. The movement between the blood vessel and the guide wire and between the guide wire and the tactile force feedback device are calculated in parallel to increase the calculation rate. Finally, CUDA is used for GPU computing to accelerate the simulation modeling speed of the system. The experimental results show that the method proposed in this paper can speed up the system simulation rate and improve the real-time performance of the system simulation.