Assessment of Hemodynamics in DeBakey Type III Aortic Dissections for Planning Surgical Interventions and to Understand Post-Treatment Changes

  • Christof KarmonikEmail author
  • Jean Bismuth
  • Mark G. Davies
  • Dipan J. Shah
  • Alan B. Lumsden


Aortic dissections are a lethal disease affecting thousands of people in the USA each year. This chapter illustrates the application of personalized computational fluid dynamics (CFD) in understanding the hemodynamics of DeBakey type III/Stanford B aortic dissections (dissections confined to the descending aorta), pre- and post-surgical interventions, and simulating hemodynamic changes as a pretreatment planning tool. In this regard, CFD simulations using patient-derived data may be useful for gaining a conceptual understanding of the hemodynamic factors for a particular aortic dissection before intervention and how these factors change with treatment or disease progression. CFD simulations have a potential role in evaluating a number of scenarios and configurations, guiding therapy, and providing a basis for outcome prediction.


Hemodynamics Aortic dissections Surgical planning Computational fluid dynamic Cardiovascular disease Transcient flow Wall shear stress Pressure catheter False lumen Thrombosis Retrograde flow Fenestration TEVAR 


  1. 1.
    Di Martino ES, Guadagni G, Fumero A, Ballerini G, Spirito R, Biglioli P, Redaelli A (2001) Fluid-structure interaction within realistic three-dimensional models of the aneurysmatic aorta as a guidance to assess the risk of rupture of the aneurysm. Med Eng Phys 23:647–655CrossRefGoogle Scholar
  2. 2.
    Foutrakis GN, Yonas H, Sclabassi RJ (1999) Saccular aneurysm formation in curved and bifurcating arteries. AJNR Am J Neuroradiol 20:1309–1317Google Scholar
  3. 3.
    Leuprecht A, Perktold K, Kozerke S, Boesiger P (2002) Combined CFD and MRI study of blood flow in a human ascending aorta model. Biorheology 39:425–429Google Scholar
  4. 4.
    Long Q, Xu XY, Bourne M, Griffith TM (2000) Numerical study of blood flow in an anatomically realistic aorto-iliac bifurcation generated from MRI data. Magn Reson Med 43:565–576CrossRefGoogle Scholar
  5. 5.
    Steinman DA, Milner JS, Norley CJ, Lownie SP, Holdsworth DW (2003) Image-based computational simulation of flow dynamics in a giant intracranial aneurysm. AJNR Am J Neuroradiol 24:559–566Google Scholar
  6. 6.
    Wood NB, Weston SJ, Kilner PJ, Gosman AD, Firmin DN (2001) Combined MR imaging and CFD simulation of flow in the human descending aorta. J Magn Reson Imaging 13:699–713CrossRefGoogle Scholar
  7. 7.
    Nienaber CA, Fattori R, Mehta RH, Richartz BM, Evangelista A, Petzsch M, Cooper JV, Januzzi JL, Ince H, Sechtem U, Bossone E, Fang J, Smith DE, Isselbacher EM, Pape LA, Eagle KA (2004) Gender-related differences in acute aortic dissection. Circulation 109:3014–3021CrossRefGoogle Scholar
  8. 8.
    Siegal EM (2006) Acute aortic dissection. J Hosp Med 1:94–105CrossRefGoogle Scholar
  9. 9.
    Berstein MA, King KF, Zhou XJ (2004) Handbook of MRI pulse sequences. Elsevier/Academic, Burlington MA, San Diego CA, London UKGoogle Scholar
  10. 10.
    Zhao M, Charbel FT, Alperin N, Loth F, Clark ME (2000) Improved phase-contrast flow quantification by three-dimensional vessel localization. Magn Reson Imaging 18:697–706CrossRefGoogle Scholar
  11. 11.
    Karmonik C, Bismuth J, Shah DJ, Anya-Ayala JE, Davies MG, Lumsden AB (2010) Quantification of intra-arterial septum motion in type III B aortic dissections with dynamic MRI. In: Annual meeting of the society of clinical vascular surgery, Scottsdale, 2010Google Scholar
  12. 12.
    Karmonik C, Bismuth J, Shah DJ, Anya-Ayala JE, Davies MG, Lumsden AB (2010) Aortic flow rates and intra-arterial septum mobility in type B aortic dissections quantified with phase contrast magnetic resonance imaging. In: Annual meeting of the society of vascular medicine, Cleveland, 2010, p 25Google Scholar
  13. 13.
    Karmonik C, Bismuth J, Davies MG, Lumsden AB (2009) Computational fluid dynamics as a tool for visualizing hemodynamic flow patterns. Methodist Debakey Cardiovasc J 5:26–33Google Scholar
  14. 14.
    Qiao A, Liu Y (2008) Medical application oriented blood flow simulation. Clin Biomech (Bristol, Avon) 23(Suppl 1):S130–S136MathSciNetCrossRefGoogle Scholar
  15. 15.
    Tsai TT, Schlicht MS, Khanafer K, Bull JL, Valassis DT, Williams DM, Berguer R, Eagle KA (2008) Tear size and location impacts false lumen pressure in an ex vivo model of chronic type B aortic dissection. J Vasc Surg 47:844–851CrossRefGoogle Scholar
  16. 16.
    Tsai TT, Evangelista A, Nienaber CA, Myrmel T, Meinhardt G, Cooper JV, Smith DE, Suzuki T, Fattori R, Llovet A, Froehlich J, Hutchison S, Distante A, Sundt T, Beckman J, Januzzi JL Jr, Isselbacher EM, Eagle KA (2007) Partial thrombosis of the false lumen in patients with acute type B aortic dissection. N Engl J Med 357:349–359CrossRefGoogle Scholar
  17. 17.
    Hose R, Black MM (1995) Prosthetic heart valves – the integration of analysis with design. J Heart Valve Dis 4(Suppl 1):S50–S54Google Scholar
  18. 18.
    Katz IM, Martonen TB (1996) Three-dimensional fluid particle trajectories in the human larynx and trachea. J Aerosol Med 9:513–520CrossRefGoogle Scholar
  19. 19.
    Xu XY, Collins MW (1990) A review of the numerical analysis of blood flow in arterial bifurcations. Proc Inst Mech Eng H 204:205–216CrossRefGoogle Scholar

Copyright information

© Springer New York 2014

Authors and Affiliations

  • Christof Karmonik
    • 1
    Email author
  • Jean Bismuth
    • 2
  • Mark G. Davies
    • 2
  • Dipan J. Shah
    • 2
  • Alan B. Lumsden
    • 2
  1. 1.Department of Translational ImagingHouston Methodist Research InstituteHoustonUSA
  2. 2.Methodist DeBakey Heart and Vascular Center, Houston MethodistHoustonUSA

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