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Biomedical Modeling and Simulation

Biomedical Modeling and Simulation. Richard C. Ward Modeling and Simulation Group Computational Sciences and Engineering Division Research supported by the Department of Energy’s Office of Science Office of Advanced Scientific Computing Research. Biomedical modeling and simulation at ORNL.

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Biomedical Modeling and Simulation

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  1. Biomedical Modeling and Simulation Richard C. Ward Modeling and Simulation Group Computational Sciences andEngineering Division Research supported by the Department of Energy’s Office of ScienceOffice of Advanced Scientific Computing Research

  2. Biomedical modeling and simulationat ORNL • Three-dimensional organ and tissue modeling using CT or other imagery (pulmonary, arterial, musculoskeletal) • Integration of models at multiple temporal and spatial scales • Biokinetic and biotransport modeling • Prediction of outcomes based on biomedical models • Computational environments (data repositories, search tools, visualization, etc.) in support of biomedical and medical applications • Design of middleware to address interoperability

  3. 3 Ward_BioModelSim_0611 Geometry models using imaging data X-ray CT data (example: NationalLibrary of Medicine Visible Human) NURBS (nonuniformrational B-spline) modelfrom visible human CT data Finite elementanalysis (FEA)from NURBS

  4. Vascular systems modeling:Predicting rupture of abdominalaortic aneurysm Collaboration withUniversity of TennesseeMedical Center Department of Surgery and VascularResearch Laboratory • CT scans used to construct geometrical model of AAA • Numerical simulations give wall mechanical stress distribution • Models predict AAA rupture site from stress distribution 4 Ward_BioModelSim_0611 4 Ward_BioModelSim_0611

  5. Hyperelastic model of AAA modifies stress analysis Produceshigher stress concentrationsat same location Hyperelastic:0.61 N/cm2 Linear elastic: 0.49 N/cm2 5 Ward_BioModelSim_0611

  6. Collaboration with A.J. Baker, UT, and Shawn Ericson, UT/ORNL JICS Using high-performance computingresources for pulmonary flow modeling • Finite element problem-solving environment • Computational fluid dynamics • Fluid-structure interactions • Equation formulator • Java GUI on user’s desktop computer • Automatic mesh partitioning • Computations routed to high-performance computer using NetSolve • Results returned to user’s desktop computer • Links to client-server visualization software • Automated archiving of scientific data sets

  7. Deposit of particulatesrelated to complexityof flow revealed Rotational flow inairways visualized Airway model Comen, Kleinstreuer, and Zhang(J Fluid Mech, 435, pp. 25-52, 2001)

  8. Species pulmonary flow modeling PICMSS (Parallel Interoperable Mechanics System Simulator) used to generate species flow using the airway model Airway model Image courtesy ofShawn Ericson, JICS Comen, Kleinstreuer, and Zhang(J Fluid Mech, 435, pp. 25-52, 2001)

  9. Cardiovascular modeling environments Integrate Connect Models Computations High-performancecomputing resources Visualization Predictions

  10. Hg0 exhaled after conversion from Hg++ Promptly exhaled Hg0 Promptly exhaled Hg0 Respiratory tract model Red blood cells PlasmaHg0 PlasmaHg0 Brain Long-term Diffusible Other Long-term Liver Long-term Non-diffusible Kidneys Long-term Urine Urinarybladder GI tract model Feces Modeling toxic exposure: Inhalation of Hg vapor Model developed by R. W. Leggett, K. F. Eckerman, and N. B. MunroLife Sciences Division

  11. Spatial modelingof cell migration Kinetic modelingof biochemicals Result: A multi-scale hybrid continuous-discrete predictive model for tissue pathology Organ Tissue Cellular Biomolecular MMP3 MMP9 MMP3 TIMP1 MMP9 TIMP1 Inhibited TIMP1 TIMP2 MMP3 proMMP9 MMP3 TIMP2 TIMP2 MMP9 MMP9 MMP9 proMMP9 Inhibited TIMP3 ACTIVE MMP9 TIMP3 proMMP9 Collagen IV TIMP1 TIMP1 MMP-9-inducedcollagenolysis Inhibited Collagen IV MMP9 MMP9 Activation of MMP-9 Inhibition of MMP-9 Predictive multiscale modeling Goal: Predict migration of smooth muscle cells from mediato intima due to inflammatory response after injury Model for predictingvascular disease Atherosclerotic artery Matrix metalloproteinases (MMPs)

  12. 12 Ward_BioModelSim_0611 Virtual Soldier Project Support provided byDefense Advanced Research Projects Agency (DARPA)Program Manager: Rick Satava

  13. Post-wounding information Pre-wounding information Use pre- and post-wounding individual data to create predictive model of specific patient ORNL contributes toDARPA Virtual Soldier Preparation Post-wounding Assemble detailed individual medical records Store records on “dog tags” Build computer model of “generic” patient ORNLinvolved Computer model provides total informational awareness for forward medical team 13 Ward_BioModelSim_0611

  14. Cardiovascular/pulmonary flow High-level integrative physiological models System circulation Circuit models describe blood flow and arterial and venous pressures Four-Chamberheart model Airway mechanics + - - + Pulmonarysystem + - + - Computations performed by University of Washington 14 Ward_BioModelSim_0611

  15. Finite-element heart simulations • Computations combine biomechanical, electrophysiology, and biochemistry models • Simulations conducted on two 105-nodedual Opteron Dell Linux clusters • Typically used only up to 32 nodesper simulation • Overall, obtained substantial speedups by combining new algorithms and high-performancecomputing • Used pre-computation and interpolation to allow team to develop real-time models for 2 h worth of heartbeats Conducted byAndrew McCulloch’s Cardiac Mechanics Research Group(University of California in San Diego) 15 Ward_BioModelSim_0611

  16. 10-0 10-1 10 minutes/beat 10-2 2.3 GHz Pentium 4 21 ODE model 16 dual CPU nodes of Linux cluster 2.3 GHz Pentium 4 76 ODE model 96 dual CPU nodes of Linux cluster Computational Speed (beats/second) 10-3 78 hours/beat 2.0 GHz Pentium 4 21 ODE model 1 CPU 10-4 300 MHz SGI Origin 2100 2 ODE model 1 CPU 833 MHz Pentium 3 2 ODE model 1 CPU 10-5 10-6 2002 2003 2004 2005 2006 2007 2008 Year Computational speed up for finite-element simulations Data courtesy of the Cardiac Mechanics Research Group, UCSD

  17. Taxonomy Simulation Results Results Results Results Ontology Results ORNL developed middleware architecture 3D segmentedanatomy model Wound trajectorydatabase Experimentaldata Prediction software An early plan VSP middleware WS WS WS Data repository WS = Web services

  18. ORNL HotBox integrates all the DARPA Virtual Soldier windows Predicted locationof wound SCIRun Net Anatomical ontology: Foundational model of anatomy HotBox interface Physiology display Geometry window with thorax model 18 Ward_BioModelSim_0611

  19. Convert CT slice data to finite-element mesh Abdominal aneurysms Prediction of wounds Data repositories Parallel computations Computational tools for toxicants Agent technologies Ontologies and informatics ORNL solves biomedical problems 19 Ward_BioModelSim_0611

  20. Contacts Richard Ward Senior Research Scientist Computational Sciences and Engineering Division (865) 574-5449 wardrc@ornl.gov Barbara Beckerman Program Manager, Biomedical Engineering Computational Sciences and Engineering Division (865) 576-2681 beckermanbg@ornl.gov 20 Ward_BioModelSim_0611

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