New Strategies in Biomedical Mesh Generation
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| A cross-section of a 3-dimensional, tetrahedral mesh of a torso. Each separate organ type is shown using a different color. |
High Quality Meshing
The problem of mesh generation has been widely studied, as a hybrid field of interest to the scientific, engineering, and computer science communities. In each of these fields, meshes are used to compute numerical approximations to solutions of partial differential equations. To do so, continuous mathematics are replaced with a discrete analogue, most commonly to facilitate the finite element method (FEM).The FEM works by decomposing a domain of interest into discrete entities of various dimensions, such as points (0-dimensional), edges (1-dimensional), and cells of higher dimension (frequently triangles and quadrilaterals are used for 2-dimensionl elements, tetrahedra and hexahedra for 3-dimensional). Together, these elements form what is commonly called a mesh (see figure)Solutions to the complex system are solved piecewise on each element, and then aggregated together to form the final solution. The FEM has become an important tool in medical imaging as well. For example, CT scans of legs can be meshed so that orthopedic modeling can accurately simulate gait, MRI scans of the torso are frequently used in cardiac electrophysical modeling, and images of the skull can identify structures of the brain.
Because the FEM is a computational tool that processes individual elements to approximate a whole solution, it is deeply impacted by the mesh elements used to represent the space. Two principle concerns stand out in the meshing problem for medical images:
Meshing for Multimaterial Biological Volumes: BioMesh3D
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| Figure from R.S. MacLeod, et al., Subjectspecific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples. Example A particle system provides adaptive sampling the various material boundaries of a segmented CT volume from a human torso. |
With the widespread use of medical imaging, there is a growing need for better analysis of datasets. One method for improving analysis is to simulate biological processes and medical interventions in silico, in order to render better predictions. For example, the CIBC center is currently collaborating with Dr. Triedman at Children's Hospital in Boston to develop a computer model that will help guide the implantation of Implantable Cardiac Defibrillators (ICDs). This model uses pediatric imaging to select placement of electrode leads to generate the optimal field for defibrillation. One of the critical pieces in the development of the model is the generation of quality meshes for electric field simulation. Because the project is entering the validation phase where many cases need to be reviewed, a robust and automated Meshing Pipeline is required.
Atrial Fibrillation
Uncertainty Visualization
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| An Isosurface visualization of a magnetic resonance imaging data set (in orange) surrounded by a volume rendered region of low opacity (in green) to indicate uncertainty in surface position. |
Imaging Meets Electrophysiology
In atrial fibrillation, the upper two chambers (the left and right atria) of the heart lose their synchronization and beat erratically and inefficiently. The same condition in the lower chambers (ventricles) of the heart is fatal within minutes and defibrillators are necessary to restore coordination. In the atria, death is by stealth and occurs over years, which is both good news and bad.
Because it is not immediately fatal, there is time to treat atrial fibrillation–but also time to ignore it. While it is not immediately life-threatening, AF does immediately reduce the pumping capacity of the heart and elevates the heart rate of the entire organ. Patients cannot be as physically active as they often wish but many adjust to the symptoms and live with the disease untreated for many years.
Visualization
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| Figure from T. Fogal and J. Krüger, a Clearview rendering of the visible human male dataset |
Simulation of Electric Stimulation for Bone Growth
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| Figure from B.M. Issacson, et al., A unilateral hierarchical model was assembled as a representative image consisting of skin (purple) adipose tissue (yellow), musculature (pink), bone (blue), bone marrow (orange), and internal organs (green) (a). Each tissue type was assigned a specific conductivity using SCIRun. A large serpentine-like mass of HO was identified in the medial aspect of the residual limb, and was demonstrated in more detail in an axial cross section of the affected limb (b). |
Subject Specific, Multiscale Simulation of Electrophysiology
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A "typical" workflow that applies to many problems in biomedical simulation contains the following elements: (i) Image acquisition and processing for a tissue, organ or region of interest (imaging and image processing), (ii) Identification of structures, tissues, cells or organelles within the images(image processing and segmentation), (iii) Fitting of geometric surfaces to the boundaries between structures and regions (geometric modelling), (iv) Generation of three-dimensional volume mesh from hexahedra or tetrahedra (meshing), and (v) Application of tissue parameters and boundary conditions and computation of spatial distribution of scalar, vector or tensor quantities of interest (simulation). |
Solving Mysteries of Autism via The Power of Collaboration
Dr. Guido Gerig Early-Brain Development Research Reveals Vibrant Clues
By Peta Owens-Liston
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| Dr. Guido Gerig |
Pencil in hand, Gerig fills three pages with a whirl of sketches as he explains how his imaging work illuminates clinical findings in his research involving early brain development, and more specifically autism. The sketches fade to stick figure-status as Gerig jumps back and forth between the paper and the color-exploding images on his computer screen. Vivid and seemingly pulsating with life, the brain-development images are a result of thousands of highly precise, quantifiable measurements never before captured visually.
MRL Releases FEBio 1.0 Software Suite
The Scientific Computing and Imaging Institute and the Musculoskeletal Research Laboratories are proud to announce the 1.0 release of the software FEBio, "Finite Elements for Biomechanics". FEBio is a nonlinear finite element software package that is specifically designed to address problems in computational biomechanics. Some of the features of note include capabilities for contact, rigid bodies and kinematic joints, nonlinear anisotropic constitutive models, simulation of active contraction, poroelasticity, element formulations for nearly-incompressible materials and parallel solution of the linear system of equations. After extensive testing in our lab and with our collaborators, we are happy to offer this free software to the research community. FEBio is currently available for WindowsXP, MacOS/X, Suse Linux (64 bit Opteron/Athlon64) and SGI Altix (64 bit Itanium2). We would be happy to port FEBio to other Unix/Linux platforms. The FEBio distribution includes the User's Manual, Theory Manual and several test problems to verify proper operation.