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Medical-GiD: From Medical Images to Simulations, 4D MRI Flow Analysis

  • Eduardo SoudahEmail author
  • Julien Pennecot
  • Jorge S. Pérez
  • Maurizio Bordone
  • Eugenio Oñate
Chapter
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 19)

Abstract

Medical imaging techniques, such as MRI and CT scanning, are valuable tools for getting a lot of information non-invasively and it is useful for reconstructing the geometry of complex objects about the patients. Medical-GiD is a medical image platform that incorporates a module to read directly the blood velocity profile from the MR scan, in particular for deformable registration of 4D MRI images, Electrocardiography (ECG)-synchronized and respiration controlled 3D magnetic resonance (MR) velocity mapping (flow-sensitive 4D MRI), 3D morphologic and three-directional blood flow data. Furthermore, Medical-GiD is focus in the medical image processing in the biomechanical research field to generating meshes from the medical images, to apply in Computational Fluid Dynamics (CFD) or structural mechanics (stress analysis). To date, these techniques have largely been applied to compute meshes for numerical simulations, but with Medical-GiD, we will have the integration between the real data and numerical simulations.

Keywords

Computational Fluid Dynamics Mesh generation Blood flow Aorta Magnetic resonance 

Notes

Acknowledgements

The authors would like to acknowledge Dr. Frances Carreras (from Hospital de la Santa Creu i Sant Pau) and Michael Markl (from the Departments of Diagnostic Radiology and Medical Physics, Freiburg, Germany) for their contributions and the support give us to do this work. The medical images used during this work are from the Departments of Diagnostic Radiology, Medical Physics; Neurology and Clinical Neurophysiology; and Cardiovascular Surgery, University Hospital Freiburg, Freiburg, Germany.

References

  1. 1.
    Chorin, A.J.: Numerical solution of the Navier Stokes Equations. Math. Comput. 22, 745–762 (1968)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    COMPASS Ingeniera y Sistemas SA.Tdyn. Environment for Fluid Dynamics (Navier Stokes equations), Turbulence, Heat Transfer, Advection of Species and Free surface simulation. Theoretical background and Tdyn 3D tutorial, March 2002Google Scholar
  3. 3.
    Formaggia, L., Nobile, F., Quarteroni, A., Veneziani, A.: Multiscale modelling of the circulatory system: a preliminary analysis. Comput. Visual. Sci. 2, 75–83 (1999)zbMATHCrossRefGoogle Scholar
  4. 4.
    GiD Reference Manual. The personal pre and postprocessor. Ribó, R., de Riera Pasenau, M.A., Escolano, E., Suit, J., Colls, A., May 2010, CIMNE. ( ftp://www.gidhome.com/pub/GiD_Documentation/Docs/GiD_Reference_Manual.pdf)
  5. 5.
    Girault, V., Raviart, P.-A.: Finite Element Methods for Navier-Stokes Equations, p. 374 Springer, Berlin/Heidelberg/New York/Tokyo: (1986)Google Scholar
  6. 6.
    The ITK Software Guide Second Edition. Ibáñez, L., Schroeder, W., Ng, L., Cates, J., and the Insight Software Consortium, August 30, 2005, Kitware Inc.Google Scholar
  7. 7.
    Labelle, F., Shewchuk, J.R.: Isosurface Stuffing: fast tetrahedral meshes with good dihedral angles. ACM Transactions on Graphics, 26(3), Article 57, July 2007Google Scholar
  8. 8.
    Lohner, R., Parikh, P.: Three dimensional grid generation by the advancing-front method. Int. J. Numer. Meth. Fluid. 8, 1135–1149 (1988)CrossRefGoogle Scholar
  9. 9.
    Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3D surface construction algorithm. ACM SIGGRAPH Computer Graphic, 21(4), 163–169, July 1987CrossRefGoogle Scholar
  10. 10.
    Markl, M., Harloff, A., Bley, T.A., Frydrychiwicz, A., et al.: Time-resolved 3D MR velocity mapping at 3T: improved navigator-gated assessment of vascular anatomy and blood flow. J. Magn. Reson. Imag. Art: 06-0195, 25:000–000 (2007)Google Scholar
  11. 11.
    Markl, M., Draney, M.T., Hope, M.D., et al.: Time-resolved 3D velocity mapping in the thoracic aorta: visualization of three-directional blood flow patterns in healthy volunteers and patients. J. Comput. Assist. Tomogr. 28, 459–468 (2004)CrossRefGoogle Scholar
  12. 12.
    Markl, M., Chan, F.P., Alley, M.T., et al.: Time-resolved 3D phase-contrast MRI. J. Magn. Reson. Imag. 17, 499–506 (2003)CrossRefGoogle Scholar
  13. 13.
    Oñate, E., García, J., Idelsohn, S.R., del Pin, F.: Finite calculus formulation for finite element analysis of incompressible flows. Eulerian, ALE and Lagrangian approaches. Comput. Meth. Appl. Mech. Eng. Elsevier, Laussane (Switzerland) (2006). ISSN 0045-7825Google Scholar
  14. 14.
    Oñate, E., Valls, A., García, J.: FIC/FEM formulation with matrix stabilizing terms for incompressible flows at low and high Reynolds numbers. Comput. Mech. Springer, Berlin (2006). ISSN: 0178-7675 (Paper) 1432-0924 (Online)Google Scholar
  15. 15.
    Oñate, E., Valls, A., García, J.: Computation of turbulent flows using a Finite calculus – finite element formulation. Int. J. Numer. Meth. Fluid. Wiley, London (GB) (2007). ISSN 0271-2091Google Scholar
  16. 16.
    Oñate, E., Valls, A., García, J.: Modeling incompressible flow at low and high reynolds numbers via a Finite calculus–Finite element approach. J. Comput. Phys. Elsevier, New York (USA) (2007). ISSN 0021-9991Google Scholar
  17. 17.
    Perktold, K., et al.: Pulsatile non-Newtonian blood flow in three-dimensional carotid bifurcation models: a numerical study of flow phenomena under different bifurcation angles. J. Biomed. Eng. 13, 507–515 (1991)CrossRefGoogle Scholar
  18. 18.
    Perktold, K., Rappitsch, G.: Mathematical modeling of local arterial flow and vessel mechanics. In: Crolet, J., Ohayon, R. (eds.) Computational Methods for Fluid Structure Interaction, pp. 230–245. Wiley, New York (1994)Google Scholar
  19. 19.
    Schroeder, W., Martin, K., Lorensen, B.: Visualization toolkit. An Object-Oriented approach to 3D graphics, 4th edn. Kitware Inc. (2006)Google Scholar
  20. 20.
    Quartapelle, L.: Numerical solution of the incompressible Navier–Stokes equations. Birkhäuser Verlag, Basel (1993)zbMATHGoogle Scholar
  21. 21.
    Taylor, C.A.: A Computation Framework for Investigating Hemodynamic Factors in Vascular Adaptation and Disease, Thesis. Stanford University, California, USA (1996)Google Scholar
  22. 22.
    Welch, B., Jones, K., Hobbs, J.: Practical programming in Tcl and Tk, 4th edn. Published by Prentice Hall, PTR (2003)Google Scholar
  23. 23.
    Schroeder, W., Martin, K., Lorensen, B.: The visualization toolkit. An Object-Oriented approach to 3D graphics, 4th edn. Kitware Inc. (2006)Google Scholar
  24. 24.
    van Steenhoven, A.A., van de Vossea, F.N., Rindt, C.C.M., Janssenu, J.D., Renemanb, R.S.: Experimental and numerical analysis of carotid arterv blood flow. Liepsch, D.W. (ed.) Blood Flow in Large Arteries: Applications to Atherogenesis and Clinical Medicine. Monogr Atheroscler, vol. 15, pp. 250–260. Basel, Karger (1990)Google Scholar
  25. 25.
    VTK User’s Guide, Install, use and extent the visualization toolkit. Version 5, Kitware Inc. March 2010Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Eduardo Soudah
    • 1
    Email author
  • Julien Pennecot
    • 1
  • Jorge S. Pérez
    • 1
  • Maurizio Bordone
    • 1
  • Eugenio Oñate
    • 1
  1. 1.International Center for Numerical Methods in EngineeringTechnical University of CataloniaBarcelonaSpain

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