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

Abstract

Objectives

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).

Methods

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.

Results

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.

Conclusions

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.

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Abbreviations

MRI:

Magnetic resonance imaging

CFD:

Computational fluid dynamics

TEVAR:

Thoracic endovascular aortic repair

MRA:

Magnetic resonance angiography

PCI:

Phase contrast imaging

WSS:

Wall shear stress

BTFE:

Balanced turbo field echo

TAA:

Thoracic aortic aneurysms

PU:

Penetrating ulcers

IMH:

Intramural haematoma

ATAR:

Acute traumatic aortic rupture

References

  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 52:1022–1033

    PubMed  Article  Google 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 43:858–862

    PubMed  Article  Google 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 47:1099–1107

    PubMed  Article  Google Scholar 

  4. 4.

    Frydrychowicz A, Francois CJ, Turski PA (2011) Four-dimensional phase contrast magnetic resonance angiography: potential clinical applications. Eur J Radiol 80:24–35

    PubMed  Article  Google 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 20:358–364

    PubMed  Article  Google 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 130:456–463

    PubMed  Article  Google 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 26:1471–1479

    PubMed  Article  Google 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 31:9–15

    PubMed  Article  Google 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 14:138–143

    PubMed  Article  Google 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 29:613–623

    PubMed  Article  Google 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, Cambridge

    Google 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 16:350–358

    PubMed  Article  Google 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, Delft

  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 59:535–546

    PubMed  Article  CAS  Google 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 25:824–831

    PubMed  Article  Google Scholar 

  16. 16.

    Lima JA, Desai MY (2004) Cardiovascular magnetic resonance imaging: current and emerging applications. J Am Coll Cardiol 44:1164–1171

    PubMed  Article  Google 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 40:332–339

    PubMed  Article  CAS  Google 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 18:559–568

    PubMed  Article  Google 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 37:58–65

    PubMed  Google 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 10:52

    Google 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 29:275–283

    PubMed  Article  CAS  Google 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 130:041004

    Google Scholar 

  23. 23.

    Nichols WW, O’Rourke MF (2005) McDonald’s blood flow in arteries: theoretical, experimental and clinical principles, 5th edn. Hodder Arnold, London

    Google 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 31:42–52

    PubMed  Article  Google 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 45:846–854

    PubMed  Article  Google Scholar 

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Acknowledgements

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.

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Correspondence to Marco Midulla.

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330_2012_2465_MOESM10_ESM.avi

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)

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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)

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Midulla, M., Moreno, R., Baali, A. et al. Haemodynamic imaging of thoracic stent-grafts by computational fluid dynamics (CFD): presentation of a patient-specific method combining magnetic resonance imaging and numerical simulations. Eur Radiol 22, 2094–2102 (2012). https://doi.org/10.1007/s00330-012-2465-7

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Keywords

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