Subject-Specific Biomechanical Simulation of Brain Indentation Using a Meshless Method

Numer Methods Biomed Eng

Wittek

et al. 2012

Self Cite

Motivated by patient-specific computational modelling in the context of image-guided brain surgery, we propose a new fuzzy mesh-free modelling framework. The method works directly on an unstructured cloud of points that do not form elements so that mesh generation is not required. Mechanical properties are assigned directly to each integration point based on fuzzy tissue classification membership functions without the need for image segmentation. Geometric integration is performed over an underlying uniform background grid. The verification example shows that, while requiring no hard segmentation and meshing, the proposed model gives, for all practical purposes, equivalent results to a finite element model.

Section: Mesh‐free Total Lagrangian Explicit Dynamic Algorithmsupporting

confidence: 83%

Patient‐specific computational biomechanics of the brain without segmentation and meshing

Zhang

Numer Methods Biomed Eng

Wittek

et al. 2012

Self Cite

“…For this simulation, a regular Cartesian grid of 53,672 integration points was created within the problem domain with 3 mm spacing between points. Such a grid can contain a small volume error (at most half of a volume of a hexahedron intersected by a domain boundary) because the grid is not volume-conforming but this has proven to be too small to have any significant effect on the results in all simulations to date (Horton et al, 2010; Horton et al, 2007). The freedom and simplicity offered by such a background grid outweigh the small inaccuracy.…”

Section: Methodsmentioning

confidence: 99%

“…These have primarily been motivated by the study of fracture mechanics and large deformations. Efforts to apply these methods in surgical simulation have been very limited (Doblare et al, 2005; Horton et al, 2007) and, to date, inadequate for clinical use.…”

Section: Introductionmentioning

confidence: 99%

Beyond finite elements: A comprehensive, patient-specific neurosurgical simulation utilizing a meshless method

Miller

Horton

Journal of Biomechanics

et al. 2012

To be useful in clinical (surgical) simulations, a method must use fully nonlinear (both geometric and material) formulations to deal with large (finite) deformations of tissues. The method must produce meaningful results in a short time on consumer hardware and not require significant manual work while discretizing the problem domain. In this paper, we showcase the Meshless Total Lagrangian Explicit Dynamics Method (MTLED) which meets these requirements, and use it for computing brain deformations during surgery. The problem geometry is based on patient-specific MRI data and includes the parenchyma, tumor, ventricles and skull. Nodes are distributed automatically through the domain rendering the normally difficult problem of creating a patient-specific computational grid a trivial exercise. Integration is performed over a simple, regular background grid which does not need to conform to the geometry boundaries. Appropriate nonlinear material formulation is used. Loading is performed by displacing the parenchyma surface nodes near the craniotomy and a finite frictionless sliding contact is enforced between the skull (rigid) and parenchyma. The meshless simulation results are compared to both intraoperative MRIs and Finite Element Analysis results for multiple 2D sections. We also calculate Hausdorff distances between the computed deformed surfaces of the ventricles and those observed intraoperatively. The difference between previously validated Finite Element results and the meshless results presented here is less than 0.2 mm. The results are within the limits of neurosurgical and imaging equipment accuracy (~1mm) and demonstrate the method’s ability to fulfill all of the important requirements for surgical simulation.

“…We use a modified element-free Galerkin method [24] that is meshless in the sense that deformation is calculated at nodes that are not part of an element mesh. Node placement is almost arbitrary.…”

Section: Meshless Computational Gridmentioning

confidence: 99%

“…The problem of computational grid generation disappears as one needs only to drop a cloud of points into the volume defined by a 3D medical image [22][23][24][25][26][27], see Figure 4. …”

Section: Geometrymentioning

confidence: 99%

Modelling brain deformations for computer‐integrated neurosurgery

Miller

Wittek

Numer Methods Biomed Eng

et al. 2009

SUMMARYIn this review paper we discuss Intelligent Systems for Medicine Laboratory's contributions to mathematical and numerical modelling of brain deformation behaviour for neurosurgical simulation and brain image registration. These processes can be reasonably described in purely mechanical terms, such as displacements, strains and stresses and therefore can be analysed using established methods of continuum mechanics. We advocate the use of fully non-linear theory of continuum mechanics. We discuss in some detail modelling geometry, boundary conditions, loading and material properties. We consider numerical problems such as the use of hexahedral and mixed hexahedral-tetrahedral meshes as well as meshless spatial discretization schemes. We advocate the use of total Lagrangian formulation of both finite element and meshless methods together with explicit time-stepping procedures. We support our recommendations and conclusions with two examples: computation of the reaction force acting on a biopsy needle, and computation of the brain shift for image registration.