New Finite Element Algorithm for Surgical Simulation (original) (raw)
Total Lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation
Communications in Numerical Methods in Engineering, 2007
We propose an efficient numerical algorithm for computing deformations of 'very' soft tissues (such as the brain, liver, kidney etc.), with applications to real-time surgical simulation. The algorithm is based on the finite element method using the total Lagrangian formulation, where stresses and strains are measured with respect to the original configuration. This choice allows for pre-computing of most spatial derivatives before the commencement of the time-stepping procedure.
Medical image analysis, 2009
Real time computation of soft tissue deformation is important for the use of augmented reality devices and for providing haptic feedback during operation or surgeon training. This requires algorithms that are fast, accurate and can handle material nonlinearities and large deformations. A set of such algorithms is presented in this paper, starting with the finite element formulation and the integration scheme used and addressing common problems such as hourglass control and locking. The computation examples presented prove that by using these algorithms, real time computations become possible without sacrificing the accuracy of the results. For a brain model having more than 7,000 degrees of freedom, we computed the reaction forces due to indentation with frequency of around 1,000 Hz using a standard dual core PC. Similarly, we conducted simulation of brain shift using a model with more than 50,000 degrees of freedom in less than one minute. The speed benefits of our models result fr...
Real-time elastic deformations of soft tissues for surgery simulation
IEEE Transactions on Visualization and Computer Graphics, 1999
In this paper, we describe a new method for surgery simulation including a volumetric model built from medical images and an elastic modeling of the deformations. The physical model is based on elasticity theory which suitably links the shape of deformable bodies and the forces associated with the deformation. A real-time computation of the deformation is possible thanks to a pre-processing of elementary deformations derived from a nite element method. This method has been implemented in a system including a force feedback device and a collision detection algorithm. The simulator works in real-time with a high resolution liver model.
Efficient linear elastic models of soft tissues for real-time surgery simulation
Studies in health technology and informatics, 1999
In this paper, we describe the basic components of a surgery simulator prototype developed at INRIA. We present two physical models which are well suited for surgery simulation. These models are based on linear elasticity theory and finite elements modeling. The former model can deforme large tetrahedral meshes in real-time but does not allow any topological changes. On the contrary, the latter biomechanical model can simulate the cutting and tearing of soft tissue but must have a limited number of vertices to run in real-time. We propose a method for combining these two approaches into a hybrid model which may allow real time deformations and cuttings of large enough anatomical structures.
Medical Image Analysis, 2019
The ability to predict patient-specific soft tissue deformations is key for computer-integrated surgery systems and the core enabling technology for a new era of personalized medicine. Element-Free Galerkin (EFG) methods are better suited for solving soft tissue deformation problems than the finite element method (FEM) due to their capability of handling large deformation while also eliminating the necessity of creating a complex predefined mesh. Nevertheless, meshless methods based on EFG formulation, exhibit three major limitations: i) meshless shape functions using higher order basis cannot always be computed for arbitrarily distributed nodes (irregular node placement is crucial for facilitating automated discretization of complex geometries); ii) imposition of the Essential Boundary Conditions (EBC) is not straightforward; and, iii) numerical (Gauss) integration in space is not exact as meshless shape functions are not polynomial. This paper presents a suite of Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms incorporating a Modified Moving Least Squares (MMLS) method for interpolating scattered data both for visualization and for numerical computations of soft tissue deformation, a novel way of imposing EBC for explicit time integration, and an adaptive numerical integration procedure within the Meshless Total Lagrangian Explicit Dynamics algorithm. The appropriateness and effectiveness of the proposed methods is demonstrated using comparisons with the established non-linear procedures from commercial finite element software ABAQUS and experiments with very large deformations. To demonstrate the translational benefits of MTLED we also present a realistic brain-shift computation.
Three-dimensional finite element modelling for soft tissues surgery
International Congress Series, 2003
Laparoscopy is a surgical technique that requires fine control from the surgeon point of view. Up to this day, this experience can only be obtained by intensive training. That is why a lot of training simulators have been developed in the medical area. We present here a new approach based on a three-dimensional finite element software and an elastic constitutive equation, able to predict realistic results. This software has been applied to soft tissues deformation, namely lamb kidney and human uterus, and the numerical results are compared to experimental ones. D
Simulation of real-time deformable soft tissues for computer assisted surgery
2004
The simulation of realistic surgical procedures requires specialized optimized algorithms for the models of organs and tissues, which should comply both with accuracy of results and run-time computation. This paper provides a general survey of methods and approaches used for the simulation of soft tissues in Computer Assisted Surgery, discussing the technological challenges to achieve realistic simulation of deformation. An application example is presented, referring to the simulation of a gastroenterology procedure, the abdominal paracentesis for the treatment of ascites.
Face-based smoothed finite element method for real-time simulation of soft tissue
SPIE Proceedings, 2017
In soft tissue surgery, a tumor and other anatomical structures are usually located using the preoperative CT or MR images. However, due to the deformation of the concerned tissues, this information suffers from inaccuracy when employed directly during the surgery. In order to account for these deformations in the planning process, the use of a bio-mechanical model of the tissues is needed. Such models are often designed using the finite element method (FEM), which is, however, computationally expensive, in particular when a high accuracy of the simulation is required. In our work, we propose to use a smoothed finite element method (S-FEM) in the context of modeling of the soft tissue deformation. This numerical technique has been introduced recently to overcome the overly stiff behavior of the standard FEM and to improve the solution accuracy and the convergence rate in solid mechanics problems. In this paper, a face-based smoothed finite element method (FS-FEM) using 4-node tetrahedral elements is presented. We show that in some cases, the method allows for reducing the number of degrees of freedom, while preserving the accuracy of the discretization. The method is evaluated on a simulation of a cantilever beam loaded at the free end and on a simulation of a 3D cube under traction and compression forces. Further, it is applied to the simulation of the brain shift and of the kidney's deformation. The results demonstrate that the method outperforms the standard FEM in a bending scenario and that has similar accuracy as the standard FEM in the simulations of the brain-shift and of the kidney's deformation.
A New Deformation Model of Brain Tissues for Neurosurgical Simulation
IEEE Transactions on Instrumentation and Measurement, 2020
An accurate and realistic brain tissue deformation model with real-time performance is very important for virtual neurosurgical simulation. In this paper, a new Finite Element Method (FEM) brain tissue deformation model, which is based on the optimization implicit Euler method, is introduced. Biomechanical properties of brain tissue such as anisotropy and viscoelasticity are incorporated into the model, which provides more accurate and realistic imitation of the deformation of brain tissue. A descent method with GPU-based implementation is used to solve the optimization problem, which makes it possible to achieve a high degree of computational efficiency. Simulation results show that both the anisotropic and viscoelastic behaviors are presented in the deformation model. The GPU-based implementation of the proposed model improves significantly the computational efficiency over CPU-based FEM models with the implicit integration scheme. Moreover, the result of the proposed model converges to the exact solution of implicit Euler integration after 96 iterations. The proposed model was implemented on the development of a neurosurgical simulator. A relative high degree of realistic brain tissue deformation was rendered at a refreshment rate of 32.5 frames per second on a regular PC.
The determination of biological material behaviors and numerical tools for medical application have been largely ongoing in the last hundred years and numerous models were developed at different scales and for different physics. We present here some late development of numerical models regarding direct medical applications for the use by clinicians, surgeons and repair surgery. These models are mostly concerned with multi-scale and multi-physics approaches with real time applications and priority is given to direct clinical transfer for everyday use. More specifically, examples are presented for bone remodeling, maxillo-facial surgery, and soft-tissue behavior for augmented reality surgery.
Towards Patient-Specific Anatomical Model Generation for Finite Element-Based Surgical Simulation
Lecture Notes in Computer Science, 2003
This paper presents ongoing research on a semi-automatic method for computing, from CT and MR data, patient-specific anatomical models used in surgical simulation. Surgical simulation is a software implementation enabling a user to interact, through virtual surgical tools, with an anatomical model representative of relevant tissues and endowed with realistic constitutive properties. Up to now, surgical simulators have generally been characterized by their reliance on a generic anatomical model, typically obtained at the cost of extensive user interaction, and by biomechanical computations based on mass-spring networks. We propose a minimally supervised procedure for extracting from a set of CT and MR scans a highly descriptive tissue classification, a set of triangulated surfaces coinciding with relevant tissue boundaries, and volumetric meshes bounded by these surfaces and comprised of tetrahedral elements of homogeneous tissue. In this manner, a series of models could be obtained with little user interaction, allowing surgeons to be trained on a large set of pathologies which are clinically representative of those they are likely to encounter. The application of this procedure to the simulation of pituitary surgery is described. Furthermore, the resolution of the surface and tissue meshes is explicitly controllable with a few simple parameters. In turn, the target mesh resolution can be expressed as a radially varying function from a central point, in this case coinciding with a point on the pituitary gland. A further objective is to produce anatomical models which can interact with a published finite element-based biomechanical simulation technique which partitions the volume into separate parent and child meshes: the former sparse and linearly elastic; the latter dense, centered on the region of clinical interest and possibly nonlinearly elastic.
Constraint-Based Soft Tissue Simulation for Virtual Surgical Training
IEEE Transactions on Biomedical Engineering, 2014
Most of surgical simulators employ a linear elastic model to simulate soft tissue material properties due to its computational efficiency and the simplicity. However, soft tissues often have elaborate nonlinear material characteristics. Most prominently soft tissues are soft and compliant to small strains, but after initial deformations they are very resistant to further deformations even under large forces. Such material characteristic is referred as the nonlinear material incompliant which is computationally expensive and numerically difficult to simulate. This paper presents a constraint-based finite element algorithm to simulate the nonlinear incompliant tissue materials efficiently for interactive simulation applications such as virtual surgery. Firstly, the proposed algorithm models the material stiffness behaviour of soft tissues with a set of three-dimensional strain limit constraints on deformation strain tensors. By enforcing a large number of geometric constraints to achieve the material stiffness, the algorithm reduces the task of solving stiff equations of motion with a general numerical solver to iteratively resolving a set of constraints with a nonlinear Gauss-Seidel iterative process. Secondly, as a Gauss-Seidel method processing constraints individually, in order to speed up the global convergence of the large constrained system a multi-resolution hierarchy structure is also used to accelerate the computation significantly, making interactive simulations possible at a high level of details. Finally, this paper also presents a simple-to-build data acquisition system to validate simulation results with ex vivo tissue measurements. An interactive virtual reality-based simulation system is also demonstrated.
Efficient soft tissue characterization under large deformations in medical simulations
International Journal of Precision Engineering and Manufacturing, 2009
The modeling of soft tissue behavior is essential for virtual reality (VR)-based medical simulation, providing a safe and objective medium for training of the medical personnel. This paper presents a soft tissue modeling framework including instrumentation design, in vitro organ experiments and material property characterization. As observed from the force responses measured by a force transducer, the tissue was assumed as a nonlinear, continuous, incompressible, homogeneous and isotropic material for modeling. An electromechanical indentation system to measure the mechanical behavior of soft tissues was designed, and a series harvested organ in vitro experiments were performed. The non-linear soft tissue model parameters were then extracted by matching finite element model predictions with the empirical data. The soft tissue characterization algorithm could become computationally efficient by reducing the number of parameters. The developed tissue models are suitable for computing accurate reaction forces on surgical instruments and for computing deformations of organ surfaces for the VR based medical simulation.
A meshless Total Lagrangian explicit dynamics algorithm for surgical simulation
International Journal for Numerical Methods in Biomedical Engineering, 2010
A method is presented for computing deformation of very soft tissue. The method is motivated by the need for simple, automatic model creation for real-time simulation. The method is meshless in the sense that deformation is calculated at nodes that are not part of an element mesh. Node placement is almost arbitrary. Fully geometrically nonlinear Total Lagrangian formulation is used. Geometric integration is performed over a regular background grid that does not conform to the simulation geometry. Explicit time integration is used via the central difference method. As an example the simple but fully nonlinear Neo-Hookean material model is employed. The results are compared with a finite element simulation to verify the usefulness of the method.
Geometrically and physically non-linear models for soft tissue simulation
We describe extensions of the tensor-mass algorithm allowing fast computation of nonlinear and visco-elastic mechanical forces and deformations for the simulation of biological soft tissue. This work is part of a broader project aiming at the development of a simulation tool for the planning of cryogenic surgical treatment of liver cancer. Real-time deformation algorithms are usually based on linear elasticity, but the simulation of percutaneous surgery requires more accurate modelling of soft tissue. Two types of non-linear extensions of the tensor-mass model are discussed here: a physically non-linear model, involving non-linear stress-strain relationships, and a geometrically non-linear or large displacement model. Both simulation models are compared to experimental data obtained under perforation of a deer liver sample by a biopsy needle.
Simulation of soft tissue deformation: A new approach
Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, 2013
An approach is presented in this paper, combining methods and models that are efficient enough to simulate elastic deformation, obtaining equilibrium between visual and haptic realism. Many medical training computational applications manipulate 3D objects that represent organs and human tissues. These representations, in function of the training requirements for which they are meant, may include parameter such as shape, topology, color, volume texture and, in certain cases, physical properties such as elasticity and stiffness. Based on this model, visual and/or haptic outputs are generated for users, and need to be realistic. In other words, they need to provide the learner with sensations close enough to those they would have if the training were provided with real life objects. However, its computational cost is too high to simultaneously provide visual and haptic realism in real-time. The results from deformation response time are compatible with those required for haptic interaction and the visual results from using meshes composed of a large number of polygons.
Physically Realistic Interactive Simulation for Biological Soft Tissues
Many applications in biomedical engineering and surgical simulators require effective modeling methods for dynamic interactive simulations. Due to its high computation time, the standard Finite Element Method (FEM) cannot be used in such cases. A FEM-based method is first presented, which rely on the decomposition of the deformation of each element into a rigid motion and a pure deformation, and a fast implicit dynamic integration without assembling a global stiffness matrix. A second physically-based discrete method is also proposed, derived from computer graphics modeling. These methods are finally compared, in terms of accuracy and speed, to theoretical problems, FEM results and experimental data.
Interactive soft tissue modelling for virtual reality surgery simulation and planning
International Journal of Computer Aided Engineering and Technology, 2017
While most existing virtual reality-based surgical simulators in the literature use linear deformation models, soft-tissues exhibit geometric and material nonlinearities that should be taken into account for realistic modelling of the deformations. In this paper, an interactive soft tissue model (ISTM) which enables flexible, accurate and robust simulation of surgical interventions on virtual patients is proposed. In ISTM, simulating the tool-tissue interactions using nonlinear dynamic analysis is formulated within a total Lagrangian framework, and the energy function is modified by adding a term in order to achieve material incompressibility. The simulation results show that ISTM increases the stability and eliminates integration errors in the dynamic solution, decreases calculation costs by a factor of 5-7, and leads to very stable and sufficiently accurate results. From the simulation results it can be concluded that the proposed model can successfully create acceptable soft tissue models and generate realistically visual effects of surgical simulation.