3D CFD in Complex Vascular Systems: A Case Study

  • Olivia Miraucourt
  • Olivier Génevaux
  • Marcela Szopos
  • Marc Thiriet
  • Hugues Talbot
  • Stéphanie Salmon
  • Nicolas Passat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8789)


Modeling the flowing blood in vascular structures is crucial to perform in silico simulations in various clinical contexts. This remains however an emerging and challenging research field, that raises several open issues. In particular, a compromise is generally made between the completeness of the simulation and the complicated architecture of the vasculature: reduced order simulations (lumped parameter models) represent vascular networks, whereas detailed models are devoted to small regions of interest. However, technical improvements enable targeting of compartments of the blood circulation rather than focusing on vascular branched segments. This article aims at investigating the cerebral flow in the entire venous drainage that can be reconstructed from medical imaging.


Transjugular Intrahepatic Portosystemic Shunt Cerebral Vein Lump Parameter Model Venous Network Hemodynamic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Schaller, B.: Physiology of cerebral venous blood flow: From experimental data in animals to normal function in human. Brain Res. Brain Res. Rev. 46, 243–260 (2004)CrossRefGoogle Scholar
  2. 2.
    Stoquart-Elsankari, S., Lehmann, P., Villette, A., Czosnyka, M., Meyer, M.E., Deramond, H., Balédent, O.: A phase-contrast MRI study of physiologic cerebral venous flow. J. Cereb. Blood F. Met. 29, 1208–1215 (2009)CrossRefGoogle Scholar
  3. 3.
    Formaggia, L., Quarteroni, A., Veneziani, A.: Cardiovascular Mathematics. MS & A, vol. 1. Springer (2009)Google Scholar
  4. 4.
    Cebral, J.R., Castro, M.A., Appanaboyina, S., Putman, C.M., Millan, D., Frangi, A.F.: Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: Technique and sensitivity. IEEE T. Med. Imaging 24, 457–467 (2005)CrossRefGoogle Scholar
  5. 5.
    Larrabide, I., Kim, M., Augsburger, L., Villa-Uriol, M.C., Rüfenacht, D., Frangi, A.F.: Fast virtual deployment of self-expandable stents: Method and in vitro evaluation for intracranial aneurysmal stenting. Med. Image Anal. 16, 721–730 (2012)CrossRefGoogle Scholar
  6. 6.
    Morales, H.G., Larrabide, I., Geers, A.J., Román, L.S., Blasco, J., Macho, J.M., Frangi, A.F.: A virtual coiling technique for image-based aneurysm models by dynamic path planning. IEEE T. Med. Imaging 32, 119–129 (2013)CrossRefGoogle Scholar
  7. 7.
    Taylor, C.A., Figueroa, C.A.: Patient-specific modeling of cardiovascular mechanics. Annu. Rev. Biomed. Eng. 11, 109–134 (2009)CrossRefGoogle Scholar
  8. 8.
    Ho, H., Mithraratne, K., Hunter, P.: Numerical simulation of blood flow in an anatomically-accurate cerebral venous tree. IEEE T. Med. Imaging 32, 85–91 (2013)CrossRefGoogle Scholar
  9. 9.
    Reymond, P., Merenda, F., Perren, F., Rüfenacht, D., Stergiopulos, N.: Validation of a one-dimensional model of the systemic arterial tree. Am. J. Physiol. 297, 208–222 (2009)Google Scholar
  10. 10.
    Blanco, P.J., Leiva, J.S., Buscaglia, G.C.: A black-box decomposition approach for coupling heterogeneous components in hemodynamics simulations. Int. J. Num. Meth. Biomed. Eng. 29, 408–427 (2013)CrossRefGoogle Scholar
  11. 11.
    Müller, L.O., Toro, E.F.: A global multiscale mathematical model for the human circulation with emphasis on the venous system. Int. J. Num. Meth. Biomed. Eng. (in press)Google Scholar
  12. 12.
    Xiao, N., Alastruey, J., Figueroa, C.A.: A systematic comparison between 1-D and 3-D hemodynamics in compliant arterial models. Int. J. Num. Meth. Biomed. Eng. 30, 204–231 (2014)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.): STACOM 2013. LNCS, vol. 8330. Springer, Heidelberg (2014)Google Scholar
  14. 14.
    Boissonnat, J.D., Chaine, R., Frey, P., Malandain, G., Salmon, S., Saltel, E., Thiriet, M.: From arteriographies to computational flow in saccular aneurisms: The INRIA experience. Med. Image Anal. 9, 133–143 (2005)CrossRefGoogle Scholar
  15. 15.
    Sato, K., Imai, Y., Ishikawa, T., Matsuki, N., Yamaguchi, T.: The importance of parent artery geometry in intra-aneurysmal hemodynamics. Med. Eng. Phys. 30, 774–782 (2008)CrossRefGoogle Scholar
  16. 16.
    Ho, H., Sorrell, K., Peng, L., Yang, Z., Holden, A., Hunter, P.: Hemodynamic analysis for transjugular intrahepatic portosystemic shunt (TIPS) in the liver based on a CT-image. IEEE T. Med. Imaging 32, 92–98 (2013)CrossRefGoogle Scholar
  17. 17.
    Passerini, T., de Luca, M., Formaggia, L., Quarteroni, A., Veneziani, A.: A 3D/1D geometrical multiscale model of cerebral vasculature. J. Eng. Math. 64, 319–330 (2009)CrossRefMATHGoogle Scholar
  18. 18.
    Blanco, P.J., Pivello, M.R., Urquiza, S.A., Feijoo, R.A.: On the potentialities of 3D-1D coupled models in hemodynamics simulations. J. Biomech. 42, 919–930 (2009)CrossRefGoogle Scholar
  19. 19.
    Mut, F., Wright, S., Ascoli, G., Cebral, J.R.: Characterization of the morphometry and hemodynamics of cerebral arterial trees in humans: A preliminary study. In: CMBE, pp. 87–90 (2011)Google Scholar
  20. 20.
    Miraucourt, M., Salmon, S., Szopos, M., Thiriet, M.: Blood flow simulations in the cerebral venous network. In: CMBE, pp. 187–190 (2013)Google Scholar
  21. 21.
    Dufour, A., Tankyevych, O., Naegel, B., Talbot, H., Ronse, C., Baruthio, J., Dokládal, P., Passat, N.: Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology. Med. Image Anal. 17, 147–164 (2013)CrossRefGoogle Scholar
  22. 22.
    Thiriet, M.: Cell and Tissue Organization in the Circulatory and Ventilatory Systems. Springer (2011)Google Scholar
  23. 23.
    Sforza, D.M., Löhner, R., Putman, C., Cebral, J.R.: Hemodynamic analysis of intracranial aneurysms with moving parent arteries: Basilar tip aneurysms. Int. J. Num. Meth. Biomed. Eng. 26, 1219–1227 (2010)CrossRefMATHGoogle Scholar
  24. 24.
    Thiriet, M.: Biology and Mechanics of Blood Flows, part I: Biology of Blood Flows, part II: Mechanics and Medical Aspects of Blood Flows. Springer (2008)Google Scholar
  25. 25.
    Pironeau, O.: On the transport-diffusion algorithm and its applications to the Navier-Stokes equations. Numer. Math. 38, 309–332 (1982)CrossRefGoogle Scholar
  26. 26.
    Sheng, Z., Thiriet, M., Hecht, F.: A high-order scheme for the incompressible Navier-Stokes equations with open boundary condition. Int. J. Numer. Meth. Fl. 73, 58–73 (2013)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Guermond, J.L., Shen, J.: A new class of truly consistent splitting schemes for incompressible flows. J. Comput. Phys. 192, 262–276 (2003)MathSciNetCrossRefMATHGoogle Scholar
  28. 28.
    Hecht, F.: New development in Freefem++. J. Num. Math. 20, 251–265 (2012)MathSciNetMATHGoogle Scholar
  29. 29.
    Ethier, C.R., Steinman, D.A.: Exact fully 3D Navier-Stokes solutions for benchmarking. Int. J. Numer. Meth. Fl. 19, 369–375 (1994)CrossRefMATHGoogle Scholar
  30. 30.
    Gisolf, J., van Lieshout, J.J., van Heusden, K., Pott, F., Stok, W.J., Karemaker, J.M.: Human cerebral venous outflow pathway depends on posture and central venous pressure. J. Physiol. 560, 317–327 (2004)CrossRefGoogle Scholar
  31. 31.
    Ford, M.D., Stuhne, G.R., Nikolov, H.N., Habets, D.F., Lownie, S.P., Holdsworth, D.W., Steinman, D.A.: Virtual angiography for visualization and validation of computational models of aneurysm hemodynamics. IEEE T. Med. Imaging 24, 1586–1592 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Olivia Miraucourt
    • 1
    • 2
  • Olivier Génevaux
    • 3
  • Marcela Szopos
    • 4
  • Marc Thiriet
    • 5
  • Hugues Talbot
    • 2
  • Stéphanie Salmon
    • 1
  • Nicolas Passat
    • 6
  1. 1.LMRUniversité de Reims Champagne-ArdenneFrance
  2. 2.ESIEE, CNRS, LIGMUniversité Paris-EstFrance
  3. 3.CNRS, ICubeUniversité de StrasbourgFrance
  4. 4.CNRS, IRMAUniversité de StrasbourgFrance
  5. 5.CNRS, LJLLUniversité Paris 6France
  6. 6.CReSTICUniversité de Reims Champagne-ArdenneFrance

Personalised recommendations