Image-based meshing is the automated process of creating computer models for computational fluid dynamics (CFD) and finite element analysis (FEA) from 3D image data (such as magnetic resonance imaging (MRI), computed tomography (CT) or microtomography). Although a wide range of mesh generation techniques are currently available, these were usually developed to generate models from computer-aided design (CAD), and therefore have difficulties meshing from 3D imaging data.
Mesh generation from 3D imaging data
Meshing from 3D imaging data presents a number of challenges but also unique opportunities for presenting a more realistic and accurate geometrical description of the computational domain. There are generally two ways of meshing from 3D imaging data:
CAD-based approach
The majority of approaches used to date still follow the traditional CAD route by using an intermediary step of surface reconstruction which is then followed by a traditional CAD-based meshing algorithm. CAD-based approaches use the scan data to define the surface of the domain and then create elements within this defined boundary. Although reasonably robust algorithms are now available, these techniques are often time consuming, and virtually intractable for the complex topologies typical of image data. They also do not easily allow for more than one domain to be meshed, as multiple surfaces are often non-conforming with gaps or overlaps at interfaces where one or more structures meet.
Image-based approach
This approach is the more direct way as it combines the geometric detection and mesh creation stages in one process which offers a more robust and accurate result than meshing from surface data. Voxel conversion technique providing meshes with brick elements and with tetrahedral elements have been proposed. Another approach generates 3D tetrahedral or tetrahedral elements throughout the volume of the domain, thus creating the mesh directly with conforming multipart surfaces.
Generating a model
The steps involved in the generation of models based on 3D imaging data are:
Scan and image processing
An extensive range of image processing tools can be used to generate highly accurate models based on data from 3D imaging modalities, e.g. MRI, CT, MicroCT (XMT), and Ultrasound. Features of particular interest include: Segmentation tools (e.g. thresholding, floodfill, level set methods, etc.) Filters and smoothing tools (e.g. volume- and topology-preserving smoothing and noise reduction/artefact removing).
Volume and surface mesh generation
The image-based meshing technique allows the straightforward generation of meshes out of segmented 3D data. Features of particular interest include: Multi-part meshing (mesh any number of structures simultaneously) Mapping functions to apply material properties based on signal strength (e.g. Young's modulus to Hounsfield scale) Smoothing of meshes (e.g. topological preservation of data to ensure preservation of connectivity, and volume neutral smoothing to prevent shrinkage of convex hulls) Export to FEA and CFD codes for analysis (e.g. node sets, shell elements, material properties, contact surfaces, boundary layers, inlets/outlets)
Typical use
Biomechanics and design of medical and dental implants
Food science
Forensic science
Materials science (composites and foams) Nondestructive testing (NDT) Paleontology and functional morphology Reverse engineering Soil science Petrophysics
See also
Image segmentation
References
External links
Computing-Objects commercial C++ libraries for mesh generation & FEM computation
ScanIP commercial image-based meshing software: www.simpleware.com
Mimics 3D image-based engineering software for FEA and CFD on anatomical data: Mimics website Google group on image-based modelling: Avizo Software's 3D image-based meshing tools for CFD and FEA iso2mesh: a free 3D surface and volumetric mesh generator for matlab/octave OOF3D, object oriented finite element analysis from the NIST VGSTUDIO MAX, Commercial CT analysis software for industry. They offer an add-on module for FEM meshing.