BIGDATA: Modeling Simulation and Visulization for Medical Device Design

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DESCRIPTION (provided by applicant): The long-term objectives of this project are to enable a paradigm-shifting future for simulation-based engineering with big data and to demonstrate this future through specific applications to challenging problems in medical device design. The approach is to couple the valuable and intense amounts of medical imaging, physical simulation, and other life sciences data that are generated today with new computational tools that not only support automated data analysis but also powerfully leverage our own human capabilities to see, touch, explore, and analyze. The potential impact on health is profound. By adopting a human- centric approach to big data science, including significant new research in the areas of data visualization and human-computer interfaces, the work is expected to not only accelerate basic research and discovery but also make the results of big data science accessible to doctors, medical device engineers, and countless other creative thinkers who do not necessarily have a core background in computational methods. There are three specific aims: (1) Advancing medical device engineering through applications of new data- intensive design tools; (2) Developing a creative new as-direct-as-possible inverse method for simulation- based engineering; and (3) Coupling data-intensive virtual design with new tangible tools for working with big data. These aims are relevant to the mission of NIBIB. Public health will be improved by developing advanced biomedical technologies that radically improve the abilities of scientists, engineers, and doctors to move from data to insight as they work with vast, complex health datasets. The research will be applied to two driving medical device engineering problems: design of cardiac leads and design of vacuum assisted biopsy devices. The work is likely to result in new design insights for these devices. More importantly, the new data-intensive design methods and tools developed are expected to generalize to many other big data bioengineering problems, illuminating a path toward a new generation of data-intensive simulation-based engineering tools for improving public health by creating safer and more effective medical devices.