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

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A multidisciplinary team of researchers from Argonne NationalLaboratory, Carnegie Mellon University, Columbia University,University of Chicago, Emory University, and University ofPennsylvania, with collaborators from the Universities of Graz andLubek, will initiate a long term research project on image-driven,inversion-based biophysical modeling. The team includes expertise innumerical algorithms and scientific computing, fluid and solidbiomechanics, PDE optimization, inverse problems, medical imageanalysis and processing, and distributed and grid computing necessaryto tackle this class of problems.This project aims to create a framework for assimilating multimodaldynamic medical image data to produce highly-resolved,physically-realistic, patient-specific biomechanics models. While thecomputational and algorithmic aspects of the project are widelyapplicable, the target application will be the construction ofpatient-specific cardiac biomechanics models from 4D image datasets ofheart motion. Such models are useful for medical diagnosis andsurgical planning. This places a premium on quick turnaround of thecomputations, which mean they must be fast, scalable, and capable ofexploiting grid-based computing.Research will focus on three key areas that undergird the project'soverall goals: registration, inversion, and distributed computing. Theregistration research component will create multilevel algorithms toextract cardiac deformation histories from time-varying medical imagedatasets via the solution of sequences of 3D image registrationproblems. The inversion research component will develop multilevelalgorithms that use these deformation field histories as virtualobservations to solve inverse problems for cardiac biomechanicalparameters. The distributed computing research component will createtools for performance prediction and resource scheduling that supportsimulations across distributed computational resources.Dovetailing with the research components, the project will undertakean educational program designed to communicate the fruits of its workand of the wider benefits of the integration of the biomedicalsciences, computing sciences, and computational sciences, to a moregeneral audience of students, disciplinary researchers, and the laypublic. The professional activities of the team members in theinversion, image registration, grid computing, and computationalscience communities will be parlayed to organize workshops andinternational meetings, edit volumes, teach summer schools, developuniversity and short courses, and engage in outreach activities---asthey have done in the past---but with greater emphasis on the field ofcomputational biomedicine. The proposed image-based cardiacbiomechanics modeling application will provide an excellentopportunity to demonstrate the benefits to health and welfare thatadvances in optimization-based registration and inversion algorithmsand Grid computing can provide.