ITR: Collaborative Research - ASE - (sim+dmc): Image-based Biophysical Modeling: Scalable Registration and Inversion Algorithms and Distributed Computing

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Abstract for Collaboration 0427985, 0427464, 0427094,0427912,0427695 A multidisciplinary team of researchers from Argonne National Laboratory, Carnegie Mellon University, Columbia University, University of Chicago, Emory University, and University of Pennsylvania, with collaborators from the Universities of Graz and Lubek, will initiate a long term research project on image-driven, inversion-based biophysical modeling. The team includes expertise in numerical algorithms and scientific computing, fluid and solid biomechanics, PDE optimization, inverse problems, medical image analysis and processing, and distributed and grid computing necessary to tackle this class of problems. This project aims to create a framework for assimilating multimodal dynamic medical image data to produce highly-resolved, physically-realistic, patient-specific biomechanics models. While the computational and algorithmic aspects of the project are widely applicable, the target application will be the construction of patient-specific cardiac biomechanics models from 4D image datasets of heart motion. Such models are useful for medical diagnosis and surgical planning. This places a premium on quick turnaround of the computations, which mean they must be fast, scalable, and capable of exploiting grid-based computing. Research will focus on three key areas that undergird the project's overall goals: registration, inversion, and distributed computing. The registration research component will create multilevel algorithms to extract cardiac deformation histories from time-varying medical image datasets via the solution of sequences of 3D image registration problems. The inversion research component will develop multilevel algorithms that use these deformation field histories as virtual observations to solve inverse problems for cardiac biomechanical parameters. The distributed computing research component will create tools for performance prediction and resource scheduling that support simulations across distributed computational resources. Dovetailing with the research components, the project will undertake an educational program designed to communicate the fruits of its work and of the wider benefits of the integration of the biomedical sciences, computing sciences, and computational sciences, to a more general audience of students, disciplinary researchers, and the lay public. The professional activities of the team members in the inversion, image registration, grid computing, and computational science communities will be parlayed to organize workshops and international meetings, edit volumes, teach summer schools, develop university and short courses, and engage in outreach activities---as they have done in the past---but with greater emphasis on the field of computational biomedicine. The proposed image-based cardiac biomechanics modeling application will provide an excellent opportunity to demonstrate the benefits to health and welfare that advances in optimization-based registration and inversion algorithms and Grid computing can provide.