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Comparison of hemodynamics of intracranial aneurysms between MR fluid dynamics using 3D cine phase-contrast MRI and MR-based computational fluid dynamics

  • Interventional Neuroradiology
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Abstract

Introduction

Hemodynamics is thought to play a very important role in the initiation, growth, and rupture of intracranial aneurysms. The purpose of our study was to compare hemodynamics of intracranial aneurysms of MR fluid dynamics (MRFD) using 3D cine PC MR imaging (4D-Flow) at 1.5 T and MR-based computational fluid dynamics (CFD).

Methods

4D-Flow was performed for five intracranial aneurysms by a 1.5 T MR scanner. 3D TOF MR angiography was performed for geometric information. The blood flow in the aneurysms was modeled using CFD simulation based on the finite element method. We used MR angiographic data as the vascular models and MR flow information as boundary conditions in CFD. 3D velocity vector fields, 3D streamlines, shearing velocity maps, wall shear stress (WSS) distribution maps and oscillatory shear index (OSI) distribution maps were obtained by MRFD and CFD and were compared.

Results

There was a moderate to high degree of correlation in 3D velocity vector fields and a low to moderate degree of correlation in WSS of aneurysms between MRFD and CFD using regression analysis. The patterns of 3D streamlines were similar between MRFD and CFD. The small and rotating shearing velocities and higher OSI were observed at the top of the spiral flow in the aneurysms. The pattern and location of shearing velocity in MRFD and CFD were similar. The location of high oscillatory shear index obtained by MRFD was near to that obtained by CFD.

Conclusion

MRFD and CFD of intracranial aneurysms correlated fairly well.

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References

  1. Press W, Teukolsky S, Vetterling W et al (1992) Numerical recipes in C. Cambridge University Press, Cambridge

    Google Scholar 

  2. Malek AM, Alper SL, Izumo S (1999) Hemodynamic shear stress and its role in atherosclerosis. JAMA 282:2035–2042

    Article  CAS  PubMed  Google Scholar 

  3. He X, Ku DN (1996) Pulsatile flow in the human left coronary artery bifurcation: average conditions. J Biomech Eng 118:74–82

    Article  CAS  PubMed  Google Scholar 

  4. Hollnagel DI, Summers PE, Kollias SS, Poulikakos D (2007) Laser Doppler velocimetry (LDV) and 3D phase-contrast magnetic resonanceangiography (PC-MRA) velocity measurements: validation in an anatomicallyaccurate cerebral artery aneurysm model with steady flow. J Magn Reson Imaging 26:1493–1505

    Article  PubMed  Google Scholar 

  5. Tateshima S, Tanishita K, Omura H, Villablanca JP, Vinuela F (2007) Intra-aneurysmal hemodynamics during the growth of an unruptured aneurysm: in vitro study using longitudinal CT angiogram database. AJNR Am J Neuroradiol 28:622–627

    CAS  PubMed  Google Scholar 

  6. Markl M, Chan FP, Alley MT et al (2003) Time-resolved three-dimensional phase-contrast MRI. J Magn Reson Imaging 17:499–506

    Article  PubMed  Google Scholar 

  7. Yamashita S, Isoda H, Hirano M et al (2007) Visualization of hemodynamics in intracranial arteries using time-resolved three-dimensional phase-contrast MRI. J Magn Reson Imaging 25:473–478

    Article  PubMed  Google Scholar 

  8. Bammer R, Hope TA, Aksoy M et al (2007) Time-resolved 3D quantitative flow MRI of the major intracranial vessels: initial experience and comparative evaluation at 1.5 T and 3.0 T in combination with parallel imaging. Magn Reson Med 57:127–140

    Article  PubMed  Google Scholar 

  9. Wetzel S, Meckel S, Frydrychowicz A et al (2007) In vivo assessment and visualization of intracranial arterial hemodynamics with flow-sensitized 4D MR imaging at 3 T. AJNR Am J Neuroradiol 28:433–438

    CAS  PubMed  Google Scholar 

  10. Mantha A, Karmonik C, Benndorf G et al (2006) Hemodynamics in a cerebral artery before and after the formation of an aneurysm. AJNR Am J Neuroradiol 27:1113–1118

    CAS  PubMed  Google Scholar 

  11. Shojima M, Oshima M, Takagi K et al (2004) Magnitude and role of wall shear stress on cerebral aneurysm: computational fluid dynamic study of 20 middle cerebral artery aneurysms. Stroke 35:2500–2505

    Article  PubMed  Google Scholar 

  12. Meng H, Wang Z, Hoi Y et al (2007) Complex hemodynamics at the apex of an arterial bifurcation induces vascular remodeling resembling cerebral aneurysm initiation. Stroke 38:1924–1931

    Article  PubMed  Google Scholar 

  13. Hassan T, Timofeev EV, Saito T (2004) Computational replicas: anatomic reconstructions of cerebral vessels as volume numerical grids at three-dimensional angiography. AJNR Am J Neuroradiol 25:1356–1365

    PubMed  Google Scholar 

  14. Jou LD, Wong G, Dispensa B et al (2005) Correlation between lumenal geometry changes and hemodynamics in fusiform intracranial aneurysms. AJNR Am J Neuroradiol 26:2357–2363

    PubMed  Google Scholar 

  15. Jou LD, Lee DH, Morsi H et al (2008) Wall shear stress on ruptured and unruptured intracranial aneurysms at the internal carotid artery. AJNR Am J Neuroradiol 29:1761–1767

    Article  PubMed  Google Scholar 

  16. Boussel L, Rayz V, McCulloch C et al (2008) Aneurysm growth occurs at region of low wall shear stress: patient-specific correlation of hemodynamics and growth in a longitudinal study. Stroke 39:2997–3002

    Article  PubMed  Google Scholar 

  17. Valencia A, Morales H, Rivera R et al (2008) Blood flow dynamics in patient-specific cerebral aneurysm models: the relationship between wall shear stress and aneurysm area index. Med Eng Phys 30:329–340

    Article  PubMed  Google Scholar 

  18. Cebral JR, Castro MA, Burgess JE, Pergolizzi RS, Sheridan MJ, Putman CM (2005) Characterization of cerebral aneurysms for assessing risk of rupture by using patient-specific computational hemodynamics models. AJNR Am J Neuroradiol 26:2550–2559

    PubMed  Google Scholar 

  19. Szikora I, Paal G, Ugron A et al (2008) Impact of aneurysmal geometry on intraaneurysmal flow: a computerized flow simulation study. Neuroradiology 50:411–421

    Article  PubMed  Google Scholar 

  20. Ohshima T, Miyachi S, Hattori K et al (2008) Risk of aneurysmal rupture: the importance of neck orifice positioning-assessment using computational flow simulation. Neurosurgery 62:767–773

    Article  PubMed  Google Scholar 

  21. Shimai H, Yokota H, Nakamura S et al (2005) Extraction from biological volume data of a region of interest with non-uniform intensity. Optomechatronic Machine Vision, edited by Kazuhiko Sumi, Proceedings of SPIE Vol. 6051, 605115

  22. Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. Comput Graph 21:163–169

    Article  Google Scholar 

  23. Masaryk AM, Frayne R, Unal O et al (1999) In vitro and in vivo comparison of three MR measurement methods for calculating vascular shear stress in the internal carotid artery. AJNR Am J Neuroradiol 20:237–245

    CAS  PubMed  Google Scholar 

  24. Cheng CP, Parker D, Taylor CA (2002) Quantification of wall shear stress in large blood vessels using Lagrangian interpolation functions with cine phase-contrast magnetic resonance imaging. Ann Biomed Eng 30:1020–1032

    Article  PubMed  Google Scholar 

  25. Zhao SZ, Papathanasopoulou P, Long Q, Marshall I, Xu XY (2003) Comparative study of magnetic resonance imaging and image-based computational fluid dynamics for quantification of pulsatile flow in a carotid bifurcation phantom. Ann Biomed Eng 31:962–971

    Article  CAS  PubMed  Google Scholar 

  26. Marshall I, Zhao S, Papathanasopoulou P, Hoskins P, Xu Y (2004) MRI and CFD studies of pulsatile flow in healthy and stenosed carotid bifurcationmodels. J Biomech 37:679–687

    Article  PubMed  Google Scholar 

  27. 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

    Article  CAS  PubMed  Google Scholar 

  28. Karmonik C, Klucznik R, Benndorf G (2008) Comparison of velocity patterns in an AComA aneurysm measured with 2D phase contrast MRI and simulated with CFD. Technol Health Care 16:119–128

    PubMed  Google Scholar 

  29. Marshall I, Zhao S, Papathanasopoulou P et al (2004) MRI and CFD studies of pulsatile flow in healthy and stenosed carotid bifurcation models. J Biomech 37:679–687

    Article  PubMed  Google Scholar 

  30. Moore JA, Steinman DA, Holdsworth DW, Ethier CR et al (1999) Accuracy of computational hemodynamics in complex arterial geometries reconstructed from magnetic resonance imaging. Ann Biomed Eng 27:32–41

    Article  CAS  PubMed  Google Scholar 

  31. Papathanasopoulou P, Zhao S, Köhler U et al (2003) MRI measurement of time-resolved wall shear stress vectors in a carotid bifurcation model, and comparison with CFD predictions. J Magn Reson Imaging 17:153–162

    Article  PubMed  Google Scholar 

  32. Ahn S, Shin D, Tateshima S, Tanishita K, Vinuela F, Sinha S (2007) Fluid-induced wall shear stress in anthropomorphic brain aneurysm models: MR phase-contrast study at 3 T. J Magn Reson Imaging 25:1120–1130

    Article  PubMed  Google Scholar 

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Acknowledgment

This study was supported by a grant from the Information-Technology Promotion Agency, Japan.

Conflict of interest statement

Dr. H. Isoda received a grant from the Renaissance of Technology Corporation.

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Correspondence to Haruo Isoda.

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Isoda, H., Ohkura, Y., Kosugi, T. et al. Comparison of hemodynamics of intracranial aneurysms between MR fluid dynamics using 3D cine phase-contrast MRI and MR-based computational fluid dynamics. Neuroradiology 52, 913–920 (2010). https://doi.org/10.1007/s00234-009-0634-4

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  • DOI: https://doi.org/10.1007/s00234-009-0634-4

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