Center for Integrative Biomedical Computing

The simulation of bioelectric fields, and estimation of their parameters from measurements, have always been a core research domain for CIBC, and we continue to pursue exciting new topics in our Simulation & Estimation TR&D. We also continue to create and support a complete and comprehensive set of software tools for relevant image-based modeling, simulation, and estimation. While the simulation and estimation aspects focus on electricity in the setting of biomedical problems, they span a broad range of fields that includes both intrinsic electric currents flowing from organs in the body and the application of external currents to simulate or modulate function in the heart and brain. Software integration is a major technical theme of the Simulation & Estimation TR&D as we continue to explore efficient means to interact with third party programs and libraries at that same time that we develop our own tools for many aspects of this domain in which we have the expertise and experience. Our goal is to provide a useful and flexible framework into which many new and existing components can interact and provide our DBP partners and collaborators with a rich environment for their biomedical and technical research.

Just as interactions with DBPs and collaborations drive the simulation research and development and provide it with valuable test beds, the synergy within the CIBC is also essential to success. Simulation is perhaps the most dependent beneficiary of the creation of image based segmentation and mesh generation tools, of the visualization capabilities necessary to view all aspects of the simulation studies, and of the mature infrastructure that integrates and supports the simulation software components. Within the Center, the simulation applications also serve as internal drivers and test beds for all the other tools, a microcosm of the larger collaborative environment in which we have a large, and growing, impact.

The figure below is an excellent example of our integrative approach: research emerging from the synergy of two DBP partners, Drs. Trayanova and Plank and Dr. Marrouche that resulted in a publication and a cover figure in a leading journal in cardiac electrophysiology. The model generation, visualization, and exploration of the simulation results were all made possible by CIBC software and the simulation was constructed by interfacing with the CARP software tools developed by Dr. Plank and used extensively in our collaborations with Dr. Trayanova. The target application of these simulations was motivated by Dr. Marrouche and his colleagues, who interpret the morphology and timing of signals recorded with a bipolar catheter in the chambers of the heart. Despite considerable clinical experience, they have only incomplete understanding of the effects of catheter orientation and location so that findings from simulations like this can provide necessary insights to improve diagnostic determinations.


Example of the application of integrated software in an interdisciplinary collaboration. This figure contains schematic diagrams of a slab of cardiac tissue (upper panel) through which activation waves are simulated using the CARP software and color coded in a visualization using SCIRun. The lower panel shows the bipolar cardiac catheter with which we sampled the surface and volume above the slab, with some of the resulting electrogram signals shown in the lower, right hand panel.
The Image and Geometric Analysis TRD addresses an ongoing demand for software that allows biomedical scientists to quickly build geometric and statistical representations from collections of images. Motivated by the needs of collaborators and the technical strengths of the investigators and the SCI Institute, the focus of this TRD is on tools for image preprocessing and segmentation, geometrically adaptive and conforming meshes, and statistical models of uncertainty and variability anatomical and biological shapes.

The aims are designed to address specific technical needs that have been established by interactions with the driving biomedical projects (DBPs) and other collaborators in diverse scientific and clinical specialties. In these collaborations, the types of imaging data range from microscopy to MRI and CT, technical specialties range from engineering to clinical research, and clinical applications range from cardiology to orthopedics.

The Image and Geometric Analysis TRD invents new technologies in image and geometric analysis utilizing the considerable research expertise of the Investigators. This TRD also develops and releases an associated set of software tools. These tools include Seg3D for low-level image processing (left), Cleaver for multimaterial meshing (center), and ShapeWorks for statistical shape analysis (right). Together these tools have resulted in over 45,000 software downloads and have been cited by dozens of papers authored by laboratories not associated with the CIBC.

seg3d cleaver shapeworks
Seg3D, a software application developed in the Image and Geometric Analysis TRD, is used to segment, visualize, compute distance fields, and register femur data for a subsequent shape study. An adaptive, tetrahedral mesh of a human torso generated in Cleaver for use in a simulation of a cardiac defibrillation. ShapeWorks analysis used to produce visualizations of group differences in the tibia/fibula geometry, at different stages of development, between wild types and a knock-out mouse model for osteochondroma.

Because the development of infrastructure required for conducting and analyzing volume data-intensive experiments and simulations has not kept pace with our collective ability to gather and create large-scale data, current data analysis and volume visualization techniques and tools now create a bottleneck to scientific discovery rather than opening opportunities. The Visualization TR&D has a research focus on algorithm scalability to address continually growing data set size and a set of uncertainty visualization tools to help our Driving Biomedical Projects directly explore simulation and measurement results, design and troubleshoot experiments, and better understand the relationship of multiple parameter uncertainties to simulation results. The aims of this TRD address specific technical needs of our Driving Biomedical Projects and will be applicable to the broader biomedical field utilizing microscopy, simulation, and clinical applications such a deep brain stimulation.

The Visualization TR&D will develop novel methods and techniques for visualizing structured volumetric data, also sometimes called volumes or 3D images. This will be accomplished through methods that use a data-centric level of detail to minimize or eliminate the need for extensive pre-processing, coupling quantitative methods with the data-centric approach. Such visualization methods will scale with large data sizes and will be based on our demonstrated research and software development expertise where we have experienced the need for reducing or eliminating pre-processing in the current visualization systems we have created: ImageVis3D and FluoRender. By focusing on streaming-based methods that perform level-of-detail computations on demand, we will be able to enhance interactivity. The novel software produced will rely on new techniques for volumetric rendering that accelerate visualization, incorporate data analysis directly in the rendering process, and provide the ability to drill down from summary information to details in the data, thereby enabling biomedical researchers to more quickly and robustly gain understanding of their data. The tools include ImageVis3D Mobile for clinical applications, FluoRender (below) for the visualization and analysis of confocal datasets and μ View for the visualization of uncertainty.

fl diaphragm

DBP collaborator, Dr. Gabrielle Kardon, uses FluoRender to help discover how developmental innovations led to the evolution of the muscularized mouse diaphragm. In this figure, a 14.5 day mouse embryo diaphragm with muscle (red via immunofluorescent labeling of myosin), tendon (green labeled genetically via GFP), and nerves (blue via immunofluorescent labeling of connective tissue) using the FluoRender visualization system developed by the Investigators.