Fast computation of soft tissue deformations in real-time simulation with Hyper-Elastic Mass Links

Abstract

International audienceVirtual surgery simulators show a lot of advantages in the world of surgery training, where they allow to improve the quality of surgeons' gesture. One of the current major technical difficulties for the development of surgery simulation is the possibility to perform a real-time computation of soft tissue deformation by considering the accurate modeling of their mechanical properties. However today, few models are available, they are still time consuming and limited in number of elements by algorithm complexity. We present in this paper a new method and framework that we call 'HEML' (Hyper-Elastic Mass Links), which is particularly fast. It is derived from the finite element method, can handle visco-hyperelastic and large deformation modeling. Although developed initially for medical applications, the HEML method can be used for any numerical computation of hyperelastic material deformations based on a tetrahedral mesh. A comparison with existing methods shows a much faster speed. A comparison with Mass-Spring methods, that are particularly fast but not realistic, shows that they can be considered as a degenerate case of the HEML framework

Non linear position and closed loop stiffness control for a pneumatic actuated haptic interface: the BirthSIM

“…Obtaining this data by in vivo measurement is difficult because of ethical issues. So to overcome this issue some partners of the SAGA project (French acronym for simulator to learn delivery gestures) work on a biomechanical model of childbirth delivery which will compute fetal head trajectories and will also allow the stiffness to be estimated [16][17] [18]. Of course, all this data will be validated by expert practitioners.…”

A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery

“…Out of the currently available deformable models, nonphysical or geometry-based models are by far the fastest and can be computed in real time, since they are based on a mathematical representation of the object's surface [19,20]; however, they lack the accuracy required reliably for medical applications [16,21]. On the contrary, physics-based models can approximate the non-linear behaviour of soft tissue much more accurately [20], but their high computational load and storage requirements do not allow them to be computed in real time [22].…”

“…On the contrary, physics-based models can approximate the non-linear behaviour of soft tissue much more accurately [20], but their high computational load and storage requirements do not allow them to be computed in real time [22]. These include mass-spring models (MSM) [13,16,21,22], the boundary element method (BEM) [20,23], the meshless method [18] and the widely used Finite Element Method (FEM) [8,24,25]. Table 1 summarises the capabilities of the aforementioned approaches in terms of accuracy, real-time computation, and adaptability to topological changes -meaning how easily the model can be updated intra-operatively when the anatomy is altered.…”

“…However, correctly predicting the deformation of an anatomical target in real time is particularly challenging. While numerous methods exist to compute the deformation of soft tissue, they are generally characterised by a compromise between speed and accuracy, due to the complex mechanical behaviour of biological tissue [16]. In fact, the high processing time required for the computation of non-linear biomechanical models is thought to be one of the main factors currently preventing accurate real-time prediction of organ deformation in image-guided surgery [17].…”

Cellular neural network modelling of soft tissue dynamics for surgical simulation

“…Explicit time integration [6,7] and implicit time integration [8,9] are the two popular numerical methods to evolve model dynamics in the temporal domain. In the explicit integration, variables in the next state are determined from their current state of known values via time steps.…”