Tetrahedral Image-To-Mesh Conversion for Anatomical Modeling and Surgical Simulations (original) (raw)
Related papers
Tetrahedral Image-To-Mesh Conversion Approaches For Surgery Simulation and Navigation
In this paper we evaluate three different mesh generation approaches with respect to their fitness for use in a surgery simulation and navigation system. The behavior of such a system can be thought of as a trade-off between material fidelity and computation time. We focus on one critical component of this system, namely non-rigid registration, and conduct an experimental study of the selected mesh generation approaches with respect to material fidelity of the resulting meshes, shape of mesh elements, condition number of the resulting stiffness matrix, and the registration error. We concluded that meshes with very bad fidelity do not affect the accuracy drastically. On the contrary, meshes with very good fidelity hurt the speed of the mesher due to the poor quality they exhibit. We also observed that the speed of the solver is very sensitive to mesh quality rather than to fidelity. For these reasons, we think that mesh generation should first try to produce high quality meshes, possibly sacrificing fidelity.
Tetrahedral Mesh Generation for Medical Imaging
Medical Image Computing and Computer-Assisted Intervention, 2000
We describe the open source implementation of an adap- tive tetrahedral mesh generator particularly targeted for non-rigid FEM registration of MR images. While many medical imaging applications re- quire robust mesh generation, there are few codes available. Moreover, most of the practical implementations are commercial. The algorithm we have implemented has been previously evaluated for simulations of highly deformable objects,
Tetrahedral Image-to-Mesh Conversion Software for Anatomic Modeling of Arteriovenous Malformations
Procedia Engineering, 2015
We describe a new implementation of an adaptive multi-tissue tetrahedral mesh generator targeting anatomic modeling of Arteriovenous Malformation (AVM) for surgical simulations. Our method, initially constructs an adaptive Body-Centered Cubic (BCC) mesh of high quality elements. Then, it deforms the mesh surfaces to their corresponding physical image boundaries, hence, improving the mesh fidelity and smoothness. Our deformation scheme, which builds upon the ITK toolkit, is based on the concept of energy minimization, and relies on a multi-material point-based registration. It uses non-connectivity patterns to implicitly control the number of the extracted feature points needed for the registration, and thus, adjusts the trade-off between the achieved mesh fidelity and the deformation speed. While many medical imaging applications require robust mesh generation, there are few codes available to the public. We compare our implementation with two similar open-source image-to-mesh conversion codes: (1) Cleaver from US, and (2) CGAL from EU. Our evaluation is based on five isotropic/anisotropic segmented images, and relies on metrics like geometric & topologic fidelity, mesh quality, gradation and smoothness. The implementation we describe is opensource and it will be available within: (i) the 3D Slicer package for visualization and image analysis from Harvard Medical School, and (ii) an interactive simulator for neurosurgical procedures involving vasculature using SOFA, a framework for real-time medical simulation developed by INRIA.
Generation and adaptation of computational surface meshes from discrete anatomical data
International Journal for Numerical Methods in Engineering, 2004
Fast and accurate scanning devices are nowadays widely used in many engineering and biomedical fields. The resulting discrete data is usually directly converted into polygonal surface meshes, using 'brute-force' algorithms, often resulting in meshes that may contain several millions of polygons. Simplification is therefore required in order to make storage, computation and display possible if not efficient. In this paper, we present a general scheme for mesh simplification and optimization that allows to control the geometric approximation as well as the element shape and size quality (required for numerical simulations). Several examples ranging from academic to complex biomedical geometries (organs) are presented to illustrate the efficiency and the utility of the proposed approach.
Automated subject-specific, hexahedral mesh generation via image registration
Finite Elements in Analysis and Design, 2011
Generating subject-specific, all-hexahedral meshes for finite element analysis continues to be of significant interest in biomechanical research communities. To date, most automated methods "morph" an existing atlas mesh to match with a subject anatomy, which usually result in degradation in mesh quality because of mesh distortion. We present an automated meshing technique that produces satisfactory mesh quality and accuracy without mesh repair. An atlas mesh is first developed using a script. A subject-specific mesh is generated with the same script after transforming the geometry into the atlas space following rigid image registration, and is transformed back into the subject space. By meshing the brain in 11 subjects, we demonstrate that the technique's performance is satisfactory in terms of both mesh quality (99.5% of elements had a scaled Jacobian >0.6 while <0.01% were between 0 and 0.2) and accuracy (average distance between mesh boundary and geometrical surface was 0.07 mm while <1% greater than 0.5mm). The combined computational cost for image registration and meshing was <4 min. Our results suggest that the technique is effective for generating subject-specific, all-hexahedral meshes and that it may be useful for meshing a variety of anatomical structures across different biomechanical research fields.
High-Quality Multi-tissue Mesh Generation for Finite Element Analysis
Springer eBooks, 2013
Mesh generation on 3D segmented images is a fundamental step for the construction of realistic biomechanical models. Mesh elements with low or large dihedral angles are undesirable, since they are known to underpin the speed and accuracy of the subsequent finite element analysis. In this paper, we present an algorithm for meshing 3D multi-label images. A notable feature of our method is its ability to produce tetrahedra with very good dihedral angles respecting, at the same time, the interfaces created by two or more adjoining tissues. Our method employs a Delaunay refinement scheme orchestrated by special point rejection strategies which remove poorly shaped elements without deteriorating the representation of the objects' anatomical boundaries. Experimental evaluation on CT and MRI atlases have shown that our algorithm produces watertight meshes consisting of elements of very good quality (all the dihedral angles were between 19 and 150 degrees) which makes our method suitable for finite element simulations.
Tetrahedral mesh generation for medical images with multiple regions using active surfaces
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010
In this paper, we present a method for automatically generating tetrahedral meshes from 3D images with multiple region labels. The first step consists of constructing an adaptively sized tetrahedral mesh that conforms exactly to the voxelized regions in the image. Active surfaces (active contours in 2D) are then employed to smooth the region boundaries and remove the voxelization. Specifically, an energy with three terms is minimized: a smoothing term to remove the voxelization, a fidelity term to keep the mesh from moving too far away from the image data, and an elasticity term to keep the tetrahedra from becoming flattened or inverted as the mesh deforms. The algorithm for tetrahedral mesh generation is applied to an MRI image that has been automatically segmented using an existing method. The resulting mesh has a number of desirable properties such as tetrahedra with all dihedral angles away from 0 and 180 degrees, smoothness, and a high level of detail for the number of tetrahedra used.
From medical images to computational meshes
Numerical simulations take a bigger importance in medical activity as they allow to obtain information non-invasively. To take into account individual variability, we propose numerical simulations in geometries reconstructed from medical images. We present the necessary treatment of images of diseased vessels, to provide a computational mesh of an anatomical model of the subject. First a faceted surface is extracted from the images. Then this surface is transformed into a geometrical model to be finally remeshed for a finite element use.
Medical & Biological Engineering & Computing, 2010
In this article, we present a new method for the generation of surface meshes of biological soft tissue. The method is based on the deformable surface model technique and is extended to histological data sets. It relies on an iterative adjustment towards polygonal segments describing the histological structures of the soft tissue. The generated surface meshes allow for the construction of volumetric meshes through a standard constrained Delaunay approach and, thus, for the application in finite element methods. The geometric properties of volumetric meshes have an immediate influence on the numerical conditioning and, therewith, on the stability of the finite element method and the convergence of iterative solvers. In this article, the influence of the surface meshes on the quality of the volumetric meshes is analysed in terms of the spectral condition number of the stiffness matrices, which are assembled within Newton's method. The non-linear material behavior of biological soft tissue is modeled by the Mooney-Rivlin material law. The subject is motivated by the requirements of virtual surgery.
From medical images to anatomically accurate finite element grids
International Journal for Numerical Methods in Engineering, 2001
The successful application of computational modelling of blood ow for the planning of surgical and interventional procedures to treat cardiovascular diseases strongly depends on the rapid construction of anatomical models. The large individual variability of the human vasculature and the strong dependence of blood ow characteristics on the vessel geometry require modelling on a patient-specic basis. Various image processing and geometrical modelling techniques are integrated for the rapid construction of geometrical surface models of arteries starting from medical images. These discretely dened surfaces are then used to generate anatomically accurate nite element grids for hemodynamic simulations. The proposed methodology operates directly in 3D and consists of three stages. In the rst stage, the images are ltered to reduce noise and segmented using a region-growing algorithm in order to obtain a properly dened boundary of the arterial lumen walls. In the second stage, a surface triangulation representing the vessel walls is generated using a direct tessellation of the boundary voxels. This surface is then smoothed and the quality of the resulting triangulation is improved. Finally, in the third stage, the triangulation is subdivided into so-called discrete surface patches for surface gridding, the desired element size distribution is dened and the nite element grid generated. Copyright ? 2001 John Wiley & Sons, Ltd.