A New Hybrid Viscoelastic Soft Tissue Model based on Meshless Method for Haptic Surgical Simulation

A study of real-time simulation of liver of virtual surgical robot based on biologically position-based dynamic model

Virtual surgery robot can accurately modeling of surgical instruments and human organs, and realistic simulation of various surgical phenomena such as deformation of organic tissues, surgery simulation system can provide operators with reusable virtual training and simulation environment. To meet the requirement of virtual surgery robot for the authenticity and real-time of soft tissue deformation and surgical simulation in liver surgery, a new method is proposed to simulate the deformation of soft tissue. This method combines the spring force, the external force of the system, and the constraint force produced by the constraint function of the position-based dynamics. Based on the position-based dynamics, an improved three-parameter mass-spring model is added. In the calculation of the elastic force, the nonlinearity and viscoelasticity of the soft tissue are introduced, and the joint force of the constraint projection process and the constraint force of the position-based dynamics is used to modify mass points movement. The method of position-based dynamics based on biological characteristics, not only considers the biomechanical properties of biological soft tissue as an organic polymer such as viscoelasticity, nonlinearity, and incompressibility but also retains the rapidity and stability of the position based dynamic method. Through the simulation data, the optimal side length of tetrahedral mesh in the improved three-parameter model is obtained, and the physical properties of the model are proved. The real-time simulation of the liver and other organs is completed by using the Geomagic touch force feedback device, which proves the practicability and effectiveness of this method.

Softness-based adaptive mesh refinement algorithm for soft tissue deformation

Soft tissue and organ modeling is the most critical function of any virtual surgical system. This study proposes a softness-based adaptive mesh refinement algorithm to simultaneously ensure realistic and real-time soft tissue simulation. The algorithm was constructed to consider that in a virtual surgery scenario, the surgical sites involve large deformation and thus require high simulation precision, whereas the nonsurgical sites involve small deformations and thus require low simulation precision. This study used the stomach lining as an example, applying mesh refinement in the deformation sites of the stomach lining to enhance the accuracy of the simulations. In addition, low mesh models were adopted for nonsurgical sites to ensure computing efficiency.

Surface deformation and reaction force estimation of liver tissue based on a novel nonlinear mass-spring-damper viscoelastic model

Rheological soft tissue models play an important role in designing control methods for modern teleoperation systems. In the meanwhile, these models are also essential for creating a realistic virtual environment for surgical training. The implementation of model-based control in teleoperation has been a frequently discussed topic in the past decades, offering solutions for the loss of stability caused by time delay, which is one of the major issues in long-distance force control. In this paper, mass-spring-damper soft tissue models are investigated, showing that the widely used linear models do not represent realistic behavior under surgical manipulations. A novel, nonlinear model is proposed, where mechanical parameters are estimated using curve fitting methods. Theoretical reaction force curves are estimated using the proposed model, and the results are verified using measurement results from uniaxial indentation. The model is extended with force estimation by nonuniform surface deformation, where the surface deformation function is approximated according to visual data. Results show that using the proposed nonlinear model, a good estimation of reaction force can be achieved within the range of 0-4 mm, provided that the tissue deformation shape function is appropriately approximated.