The overall organization of the Center is based on four technical cores supported and integrated by our SCIRun software, into which BioPSE (Biomedical Problem Solving Environment) is now fully integrated. This software provides the researcher with enormous capabilities and infrastructure, as well with a comprehensive set of building blocks allowing the creation of specific software pipelines for almost any imaginable task. The Center’s software encapsulates all the techniques that flow from the research cores.
Image Based Modeling
In biology and medicine, the problem of using images to derive geometric models is ubiquitous. Biomedical scientists need to quickly build geometric and statistical representations from collections of images. Geometric models are useful in a staggering range of biomedical applications, from patient-specific treatment decisions for orthopaedic surgery and cardiology to basic scientific questions about blood flow and degenerative disease.
Generating discrete, geometric representations for subsequent analysis is one of the important challenges in image-based modeling. The Image Based Modeling Research Core focuses on using image data to construct geometric models to be used for simulation, visualization, and quantitative analysis. Specifically, our focus is on tools for geometrically adaptive and conforming meshes, as well as statistical models of anatomical and biological shapes.
At the Center, the work of application scientists is tightly integrated with the research vision. Imaging and scientific computing advances allow for exciting opportunities to use simulation and visualization methods to improve clinical practice. The Center's developments focus on the robustness of imaging tools, in addition to open-source software which researchers may modify to better suit their needs. The Center strives to advance the state of the art through its strategies of image-based modeling, open-source tools, and application-driven development.
The NIH-NSF Visualization Research Challenges report notes that one of the greatest scientific challenges of the 21st Century is to effectively understand and use the vast amounts of information being produced. In the past three decades, computational and acquisition technologies experienced unprecedented growth. This growth improved our ability to sense the physical world in precise detail and to model and simulate complex physical phenomenon.As a field, visualization focuses on creating images that convey salient information about underlying data and processes. Visualization is crucial to our ability to comprehend such large and complex data. This data, in two, three, or more dimensions, conveys insight into diverse biomedical applications, from understanding the bioelectric currents in the heart to understanding morphology difference between different genetic mice phenotypes.
The Visualization Research Core provides cutting-edge visualization research and software to biomedical scientists, helping researchers gain insight into measured or simulated data. The Visualization technology core develops and implements advanced high-performance algorithms and software for visualizing large, spatially distributed and/or time varying sets. The applications developed at the Center, such as ImageVis3D, place powerful visualization capabilities into the hands of biomedical researchers. These applications allow researchers to interactively explore and gain insight from large-scale image, experimental, and simulation data, all with the potential to revolutionize biology and medicine.
Although computer-based simulation has a relatively modern history, the basic elements of simulation are as old as mathematics. Predicting the trajectory of a cannon ball or the pathways of the planets makes use of the two essential elements of simulation: mathematical equations that describe behavior and a means to solve those equations under realistic conditions.
In the past 50 years, simulation and computing performance have experienced parallel advances and facilitated breakthroughs in science and medicine. Now, virtually any biological system can have a simulation counterpart cat can play numerous roles in biomedical research and clinical practice. Performing experiments using computer simulation enables access to parameters that are otherwise impossible, impractical, or unethical to measure in a physical experiment. Simulation is also used to create predictive models that integrate vast amounts of diverse parameters and behaviors; this diverse and broad range of parameters and features are necessary to even begin to capture realistic behavior.
In the context of the Center, simulation is defined as predicting the behavior of cells, tissues, and organs under simplifying assumptions over known anatomical domains in response to pre-determined boundary or initial conditions. The Center provides biomedical scientists with access to software tools that allow them to define the assumptions, the domains, and the initial or boundary conditions. The scientists will then be able to carry out simulations using a variety of algorithms. The Center's continued focus on multi-scale modeling of electrical activity in the body allows for a range of diverse projects, from shocking a heart that has fallen into fatal, uncontrolled, electrical spasm to stimulating bone growth into metallic implants in amputees.
This core concentrates on algorithms and software for the estimation of biomedically meaningful parameters and values that are either outputs of the other CIBC Technology Research and Development Cores or are needed as inputs or parameters for those Cores. It includes both direct estimation from data and "indirect" estimation via solutions to inverse problems. The CIBC's overall goal is to increase the ability of biomedical researchers to extract meaningful quantitative results from solutions to a variety of imaging problems. In some aspects of the collaborations in which CIBC participates, this can be achieved directly by image-based modeling or simulation of mechanistic processes or visualization of those models or simulation results. In these situations the connection between the computational output and the underlying biomedical problem is so direct that the required information can be extracted implicitly, e.g. by user observation or simple computation using pre-set thresholds.
In other aspects of the Center's work, though, we need to deal more explicitly with a variety of kinds of uncertainty in developing computational tools. For example, when designing models for bioelectric simulations there is a feedback loop between choosing the parameters of the physical model and the output of the resulting simulations, requiring development of computational tools to evaluate the simulation outputs and guide optimization of the models that produce them. In other Driving Biological Projects (DBPs) we need to explicitly solve inverse problems, i.e. using geometric models and simulation algorithms to determine underlying parameters of clinical or scientific interest. For instance, in our DBP collaboration with Dr. Tucker, we plan to use voltage and current measurements from external electrodes on the scalp to solve an electrical impedance tomography (EIT) problem to estimate internal conductivities.
All of these problems have a broad range of uncertainties that require the use of estimation-based algorithms that are designed to provide a reasonable degree of robustness. The CIBC Estimation Core includes various kinds of estimation under a single framework, all in the context of computations based on the assumption of uncertainty in our measurements and modeling. The Estimation Core helps to bind the other Research Cores together, as well as uniting the biomedical user community with imaging results, furthering enabling translational biomedical advances.