Kalman Filter Finite Element Method for Real-time Soft Tissue Modelling

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Xie, Hujin, Song, Jialu, Zhong, Yongmin, Gu, Chengfan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Discrete systems; Discretization; Elastic deformation; Equations of state; Equilibrium equations; Finite element analysis; Finite element method; Kalman filter; Kalman filters; local measurement and soft tissue deformation; Mathematical models; Real time; Realism; Simulation; Soft tissues; Surgery
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2981400

Soft tissue modelling plays a significant role in surgery simulation as well as surgical procedure planning and training. However, it is a challenging research task to satisfy both physical realism and real-time simulation for soft tissue deformation. The finite element method (FEM) is a representative strategy for modelling of soft tissue deformation with highly physical realism. However, it suffers from expensive computations, unable to meet the requirement of real-time simulation. This paper proposes a novel method by combining FEM with the Kalman filter for real-time and accurate modelling of soft tissue deformation. The novelty of this method is that soft tissue deformation is formulated as a filtering identification process to online estimate soft tissue deformation from local measurement of displacement. To construct the discrete system state equation for filtering estimation, soft tissue deformation is discretised based on elastic theory in the space domain by FEM and is further discretised in the time domain by using the Wilson-h implicit integration to solve the dynamic equilibrium equation of FEM deformation modelling. Subsequently, a Kalman filter is developed for online estimation and analysis of soft tissue deformation according to local measurement of displacement. Interactive tool-tissue interaction with haptic feedback is also achieved for surgery simulation. The presented method significantly improves the computational performance of the traditional FEM, but still maintains a similar level of accuracy. It not only achieves the real-time performance, but also exhibits the similar deformation behaviours as the traditional FEM and enables the use of large time steps to improve the simulation efficiency.