An adaptive deviation-feedback approach for simulating multiple devices interaction in virtual interventional radiology. (December 2019) - Wang, Haoyu; Wu, Jianhuang; Cai, Yiyu
Abstract: Endovascular intervention is widely used in the treatment of cardiovascular and cerebrovascular diseases. This procedure requires physicians to be highly skilled at manipulating surgical devices such as catheters and guidewires. Virtual reality-based interventional simulators for surgical skills training have many advantages over traditional training methods. Simulation of the behavior of interventional devices is a very challenging task when developing such a simulator. In this paper, we proposed a shared centerline model incorporated with weighted material properties to represent the interventional devices. To efficiently and stably simulate the interactive behavior of these devices, we proposed an adaptive deviation-feedback approach to accelerate the convergence of the iterations. Additionally we proposed a track based control strategy to predict the behavior of the devices based on their past track to further decrease the amount of computation needed for the achievement of new equilibrium. The performance of our approach is experimentally validated via two endovascular interventional applications.