This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Scientific visualization is concerned with helping researchers explore measured or simulated data to gain insight into structures and relationships within the data. The impact of scientific visualization can be seen in all areas of science, medicine, and engineering. A central aim is to bring cutting edge visualization research and technology to biomedical scientists. The goals of the visualization technology core are to develop and then to implement advanced, efficient, high-performance algorithms and software for visualizing large, spatially distributed and/or time varying data sets. In order to achieve its full potential as an effective scientific tool, visualization must be not just the natural end point of the biomedical computing pipeline but a ubiquitous component of every step within that pipeline: it must enable to user to see the data from raw images to finished simulation and then to visualize the errors and uncertainties that arise from the measurements and computations applied to those data. In order to achieve these goals, we aim to greatly increase the breadth and sophistication of visualization technologies available for biomedical researchers, first by leveraging existing expertise within the Scientific Computing and Imaging Institute, then by carrying out new research directed at such areas as time-dependent image data, flow fields from bioelectric fields and other ion-transport behaviors, diffusion weighted MRI image sets, and data error/uncertainty and by combining such data types into intuitive, quantitative, interactive displays. Three primary visualization goals focus on both research and development: (1) to research new visualization techniques for biomedical applications, (2) to develop visualization tools and software for biomedical visualization based upon state- of-the-art visualization research developed within the Scientific Computing and Imaging Institute and elsewhere, and (3) to leverage third-party visualization software to take advantage of existing software.
These aims both reflect the existing expertise of the center's investigators and include substantial components that have originated with the collaborative projects. Such close research ties between the center and its collaborators will improve the quality of the projects by broadening the sources of feedback and intellectual contributions and so help to maximize their impact on the field. Our research will include new work directed at such areas as multi-dimensional transfer function volume visualization of image data, multi-field visualization for bioelectric fields and other ion-transport behaviors, visualization of diffusion weighted MRI, and the creation of new visual representations for data error/uncertainty in experimental and computational data sets. In addition to our research goals, we aim to develop a set of powerful, interactive, quantitative, usable, and integrated visualization tools for biomedical scientists. The utility and impact of the research lie not only in the specific techniques we propose to develop and implement, but also in the way that these techniques will be integrated into BioPSE. Some of the techniques will be tuned to the specific needs of our collaborators or the particular research or clinical application, and many others, such as the multi-dimensional volume rendering, error and uncertainty visualization, and multi-field visualization, will also be appropriate for a broader range of applications. As part of the BioPSE infrastructure, these techniques will become immediately available to all users of the software for a range of related purposes. Below we give a brief summary of the center s visualization research and development goals: (1) Investigate new diffusion tensor visualization and analysis techniques. (2) Develop and harden state-of-the-art Scientific Computing and Imaging visualization research prototypes in scalar, vector, and tensor field visualization into robust BioPSE components. (3) Supply techniques that support extensive and flexible examination of the quantitative aspects of bioelectric field data, such as voltage gradients and isochrone velocities. (4) Integrate our multi-dimensional transfer function volume rendering system in BioPSE. (5) Expand the capabilities of map3d , especially in the areas of time-dependent geometry and multiple-data visualization to meet the needs of the collaborators and other users, especially those in application areas outside of bioelectric fields. (6) Develop visual methods for comparisons of simulation results based upon the proposed visual representation of error and uncertainty research. (7) Extend BioPSE visualization modules to take advantage of remote visualization utilities.
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