GPU Accelerated Needle Insertion Simulation using Meshfree Methods

Abstract

Needle insertion is a common practice used in many different medical procedures. Therefore, simulation of needle insertion is of great importance for multiple purposes such as training, planning and robotic assisted interventions. Modeling of soft tissue plays an important role in the needle insertion simulation, but the use of mesh based methods such as the Finite Element Method is frustrated by the need for remeshing in the neighbourhood of the needle tip. We have developed a novel method that uses meshfree methods for the tissue deformation model. In this method new tissue nodes are added on the needle shaft as the needle is inserted into the tissue. We have used a stack based approach to keep the state of the model; therefore, we have avoided over-sampling the model due to continuous needle insertion and extraction. Using this approach we have simulated the insertion of a straight rigid needle into soft tissue. In addition, we have utilized Nvidia's CUDA technology to accelerate the methods used in our framework. Our framework allows dynamic resampling and addition of new nodes while using CUDA. Our results show the usability and flexibility of the new method. By using the CUDA technology we were able to achieve up to 20 times speed for large meshes.