Finite-element kalman filter with state constraint for dynamic soft tissue modelling

A high-fidelity virtual liver model incorporating biological characteristics

“…Relevant parameters of human liver tissue were specified to the model, and the mass density was set as 1060 kg/m 3 , Young's modular as 3500 Pa, and Poisson's ratio as 0.49 [ 24 , 25 ]. Four models were used to model liver tissue: a new particle spring model that effectively simulates dynamic soft tissue deformation [ 2 ], constrained Kalman filter modeling based on finite element method [ 7 ], extended Kalman filter nonlinear finite element method for nonlinear soft tissue deformation [ 12 ], and proposed model. Take a mark point at the same position of these models, and apply 0–0.63 N vertical axial tension to this point.…”

“…In the past decades, physical modeling methods have been widely studied and applied to modeling and simulation of biological tissues. Among various modeling methods to enhance the immersion of virtual surgery, scholars from many countries are committed to balancing the real-time and authenticity of the model, and most of them believe that biological tissues are purely elastic [ [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] ]. Abbass Ballit et al [ 2 ] proposed a new mass spring model for fast, accurate, and stable simulation of dynamic soft tissue deformation.…”

“…Jon S. Heiselman et al [ 6 ] used linear iterative boundary reconstruction of sparse surface and vascular features to correct liver deformation intraoperatively to avoid large errors caused by soft tissue deformation. Hujin Xie et al [ 7 ] proposed a new method for real and real-time modeling of deformable biological tissues by combining the traditional finite element method with the constrained Kalman filter, which was utilized to address the deficiencies of the finite element method with large computation amount and inherit the advantages of physical fidelity at the same time. Sirui He et al [ 8 ] introduced the virtual cutting simulation algorithm based on the energy-based cutting fracture evolution model to conduct real-time and real cutting simulation for the multifunctional cutting fracture evolution modeling of deformable objects.…”

The Kalman filter's role in optimizing fluorescence analysis

“…This property of adaptability and predictive precision of the Kalman filter [36][37][38][39][40][41][42] in dealing with noise and uncertainties of the fluorescence signals convert it into an invaluable resource, that in its turn raises the signal-to-noise ratio (SNR) and allows us to extract meaningful data from serious biological problems. This not only helps in gaining a better insight into biological processes at cellular and molecular levels but also results in a more accurate generation of diagnostic tools and therapies [43][44][45][46][47][48][49][50][51][52].…”