European Radiology

, Volume 22, Issue 10, pp 2094–2102 | Cite as

Haemodynamic imaging of thoracic stent-grafts by computational fluid dynamics (CFD): presentation of a patient-specific method combining magnetic resonance imaging and numerical simulations

  • Marco MidullaEmail author
  • Ramiro Moreno
  • Adil Baali
  • Ming Chau
  • Anne Negre-Salvayre
  • Franck Nicoud
  • Jean-Pierre Pruvo
  • Stephan Haulon
  • Hervé Rousseau



In the last decade, there was been increasing interest in finding imaging techniques able to provide a functional vascular imaging of the thoracic aorta. The purpose of this paper is to present an imaging method combining magnetic resonance imaging (MRI) and computational fluid dynamics (CFD) to obtain a patient-specific haemodynamic analysis of patients treated by thoracic endovascular aortic repair (TEVAR).


MRI was used to obtain boundary conditions. MR angiography (MRA) was followed by cardiac-gated cine sequences which covered the whole thoracic aorta. Phase contrast imaging provided the inlet and outlet profiles. A CFD mesh generator was used to model the arterial morphology, and wall movements were imposed according to the cine imaging. CFD runs were processed using the finite volume (FV) method assuming blood as a homogeneous Newtonian fluid.


Twenty patients (14 men; mean age 62.2 years) with different aortic lesions were evaluated. Four-dimensional mapping of velocity and wall shear stress were obtained, depicting different patterns of flow (laminar, turbulent, stenosis-like) and local alterations of parietal stress in-stent and along the native aorta.


A computational method using a combined approach with MRI appears feasible and seems promising to provide detailed functional analysis of thoracic aorta after stent-graft implantation.

Key Points

Functional vascular imaging of the thoracic aorta offers new diagnostic opportunities

CFD can model vascular haemodynamics for clinical aortic problems

Combining CFD with MRI offers patient specific method of aortic analysis

Haemodynamic analysis of stent-grafts could improve clinical management and follow-up.


Time-resolved 3D MRI CFD TEVAR Stent-graft Haemodynamics 



Magnetic resonance imaging


Computational fluid dynamics


Thoracic endovascular aortic repair


Magnetic resonance angiography


Phase contrast imaging


Wall shear stress


Balanced turbo field echo


Thoracic aortic aneurysms


Penetrating ulcers


Intramural haematoma


Acute traumatic aortic rupture



Ramiro Moreno has been financed during the development of the project by the Medtronic Vascular (Santa Rosa, CA); the project OCFIA has been supported by the French National Agency for Research (ANR 07-CIS7-006-01). CINES (Montpellier) is acknowledged for CFD calculations.

The early results of the project have been presented twice at the ECR, in 2008 and in 2011. The first presentation was honoured with the first prize in the vascular topic, of which we are sincerely proud.

Supplementary material

Video 1

(AVI 10546 kb)

Video 2

(AVI 6548 kb)

Video 3

(AVI 5007 kb)

Video 4

(AVI 3903 kb)

Video 5

(AVI 3169 kb)

330_2012_2465_MOESM6_ESM.avi (3 mb)
Video 6a (AVI 3033 kb)
Video 6b

(AVI 3376 kb)

Video 6c

(AVI 3906 kb)

330_2012_2465_MOESM9_ESM.avi (6.1 mb)
Video 7 (AVI 6251 kb)
330_2012_2465_MOESM10_ESM.avi (10.8 mb)
Video 8 Tetrahedral moving mesh obtained after segmentation of the aortic volume and imposition of the wall movements according to the MRI cine imaging. The approach proposed with the presented method would yield more realistic conditions for the numerical simulations and could be a further step towards patient-specific computational flow modelling via a combination of MRI with CFD (see text) (AVI 11031 kb)


  1. 1.
    Fillinger MF, Greenberg RK, McKinsey JF, Chaikof EL (2010) Society for Vascular Surgery Ad Hoc Committee on TEVAR Reporting Standards. Reporting standards for thoracic endovascular aortic repair (TEVAR). J Vasc Surg 52:1022–1033PubMedCrossRefGoogle Scholar
  2. 2.
    Hodgson KJ, Matsumura JS, Ascher E, Dake MD, Sacks D, Krol K, Bersin RM, SVS/SIR/SCAI/SVMB Writing Committee (2006) Clinical competence statement on thoracic endovascular aortic repair (TEVAR)—multispecialty consensus recommendations. A report of the SVS/SIR/SCAI/SVMB Writing Committee to develop a clinical competence standard for TEVAR. J Vasc Surg 43:858–862PubMedCrossRefGoogle Scholar
  3. 3.
    Corbillon E, Bergeron P, Poullié AI, Primus C, Ojasoo T, Gay J (2008) The French National Authority for Health reports on thoracic stent grafts. J Vasc Surg 47:1099–1107PubMedCrossRefGoogle Scholar
  4. 4.
    Frydrychowicz A, Francois CJ, Turski PA (2011) Four-dimensional phase contrast magnetic resonance angiography: potential clinical applications. Eur J Radiol 80:24–35PubMedCrossRefGoogle Scholar
  5. 5.
    Hope TA, Herfkens RJ (2008) Imaging of the thoracic aorta with time-resolved three-dimensional phase-contrast MRI: a review. Semin Thorac Cardiovasc Surg 20:358–364PubMedCrossRefGoogle Scholar
  6. 6.
    Markl M, Draney MT, Miller DC et al (2005) Time-resolved three-dimensional magnetic resonance velocity mapping of aortic flow in healthy volunteers and patients after valve-sparing aortic root replacement. J Thorac Cardiovasc Surg 130:456–463PubMedCrossRefGoogle Scholar
  7. 7.
    Hope TA, Markl M, Wigstrom L, Alley MT, Miller DC, Herfkens RJ (2007) Comparison of flow patterns in ascending aortic aneurysms and volunteers using four-dimensional magnetic resonance velocity mapping. J Magn Reson Imaging 26:1471–1479PubMedCrossRefGoogle Scholar
  8. 8.
    Frydrychowicz A, Harloff A, Jung B et al (2007) Time-resolved, 3-dimensional magnetic resonance flow analysis at 3 T: visualization of normal and pathological aortic vascular hemodynamics. J Comput Assist Tomogr 31:9–15PubMedCrossRefGoogle Scholar
  9. 9.
    Howell BA, Kim T, Cheer A, Dwyer H, Saloner D, Chuter TA (2007) Computational fluid dynamics within bifurcated abdominal aortic stent-grafts. J Endovasc Ther 14:138–143PubMedCrossRefGoogle Scholar
  10. 10.
    Frauenfelder T, Lotfey M, Boehm T, Wildermuth S (2006) Computational fluid dynamics: hemodynamic changes in abdominal aortic aneurysm after stent-graft implantation. Cardiovasc Intervent Radiol 29:613–623PubMedCrossRefGoogle Scholar
  11. 11.
    Sethian JA (1999) Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science, 2nd edn. Cambridge University Press, CambridgeGoogle Scholar
  12. 12.
    Figueroa CA, Taylor CA, Chiou AJ, Yeh V, Zarins CK (2009) Magnitude and direction of pulsatile displacement forces acting on thoracic aortic endografts. J Endovasc Ther 16:350–358PubMedCrossRefGoogle Scholar
  13. 13.
    Moreno RNF, Veunac L, Rousseau H (2006) Non-linear-transformation-field to build moving meshes for patient specific blood flow simulations. In: Wesseling P, Onate E, Periaux J (eds) European conference on computational fluid dynamics. TU Delft, DelftGoogle Scholar
  14. 14.
    Canstein C, Cachot P, Faust A et al (2008) 3D MR flow analysis in realistic rapid-prototyping model systems of the thoracic aorta: comparison with in vivo data and computational fluid dynamics in identical vessel geometries. Magn Reson Med 59:535–546PubMedCrossRefGoogle Scholar
  15. 15.
    Markl M, Harloff A, Bley TA et al (2007) Time-resolved 3D MR velocity mapping at 3 T: improved navigator-gated assessment of vascular anatomy and blood flow. J Magn Reson Imaging 25:824–831PubMedCrossRefGoogle Scholar
  16. 16.
    Lima JA, Desai MY (2004) Cardiovascular magnetic resonance imaging: current and emerging applications. J Am Coll Cardiol 44:1164–1171PubMedCrossRefGoogle Scholar
  17. 17.
    Molony DS, Kavanagh EG, Madhavan P, Walsh MT, McGloughlin TM (2010) A computational study of the magnitude and direction of migration forces in patient-specific abdominal aortic aneurysm stent-grafts. Eur J Vasc Endovasc Surg 40:332–339PubMedCrossRefGoogle Scholar
  18. 18.
    Prasad A, To LK, Gorrepati ML, Zarins CK, Figueroa CA (2011) Computational analysis of stresses acting on intermodular junctions in thoracic aortic endografts. J Endovasc Ther 18:559–568PubMedCrossRefGoogle Scholar
  19. 19.
    Srichai MB, Kim S, Axel L, Babb J, Hecht EM (2010) Non-gadolinium-enhanced 3-dimensional magnetic resonance angiography for the evaluation of thoracic aortic disease: a preliminary experience. Tex Heart Inst J 37:58–65PubMedGoogle Scholar
  20. 20.
    Tay WB, Tseng YH, Lin LY, Tseng WY (2011) Towards patient-specific cardiovascular modeling system using the immersed boundary technique. Biomed Eng Online 10:52Google Scholar
  21. 21.
    Saber NR, Gosman AD, Wood NB, Kilner PJ, Charrier CL, Firmin DN (2001) Computational flow modeling of the left ventricle based on in vivo MRI data: initial experience. Ann Biomed Eng 29:275–283PubMedCrossRefGoogle Scholar
  22. 22.
    Kitajima HD, Sundareswaran KS, Teisseyre TZ, et al (2008) Comparison of particle image velocimetry and phase contrast MRI in a patient-specific extracardiac total cavopulmonary connection. J Biomech Eng 130:041004Google Scholar
  23. 23.
    Nichols WW, O’Rourke MF (2005) McDonald’s blood flow in arteries: theoretical, experimental and clinical principles, 5th edn. Hodder Arnold, LondonGoogle Scholar
  24. 24.
    Saber NR, Wood NB, Gosman AD et al (2003) Progress towards patient-specific computational flow modeling of the left heart via combination of magnetic resonance imaging with computational fluid dynamics. Ann Biomed Eng 31:42–52PubMedCrossRefGoogle Scholar
  25. 25.
    Wentzel JJ, Corti R, Fayad ZA, Wisdom P, Macaluso F, Winkelman MO, Fuster V, Badimon JJ (2005) Does shear stress modulate both plaque progression and regression in the thoracic aorta? Human study using serial magnetic resonance imaging. J Am Coll Cardiol 45:846–854PubMedCrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2012

Authors and Affiliations

  • Marco Midulla
    • 1
    Email author
  • Ramiro Moreno
    • 2
    • 6
  • Adil Baali
    • 6
  • Ming Chau
    • 5
  • Anne Negre-Salvayre
    • 6
  • Franck Nicoud
    • 3
  • Jean-Pierre Pruvo
    • 1
  • Stephan Haulon
    • 4
  • Hervé Rousseau
    • 2
    • 6
  1. 1.Cardiovascular RadiologyUniversity Hospital of LilleLilleFrance
  2. 2.RadiologyRangueil University HospitalToulouseFrance
  3. 3.University Montpellier II – CNRS UMR 5149 I3M, CC 051MontpellierFrance
  4. 4.Vascular SurgeryUniversity Hospital of LilleLilleFrance
  5. 5.ASA, Advanced Solutions Accelerator, University of Toulouse 3 Paul SabatierMontpellierFrance
  6. 6.INSERM/UMR 1048 Cardiovascular and Metabolic DiseasesUniversity of Toulouse 3 Paul SabatierToulouseFrance

Personalised recommendations