Acta Neurochirurgica

, Volume 151, Issue 5, pp 479–485 | Cite as

Intra-aneurysmal flow patterns and wall shear stresses calculated with computational flow dynamics in an anterior communicating artery aneurysm depend on knowledge of patient-specific inflow rates

  • Christof Karmonik
  • Christopher Yen
  • Robert G. Grossman
  • Richard Klucznik
  • Goetz Benndorf
Experimental Research



To evaluate if knowledge of patient-specific inflow data in computational fluid dynamics simulations is required for the accurate calculation of intra-aneurysmal flow patterns and wall shear stress in an aneurysm of the anterior communicating artery (AcomA).

Materials and methods

3D digital subtraction angiography (3D-DSA) and phase contrast magnetic resonance (pcMRI) images were obtained in a 71-year old patient with an unruptured aneurysm of the anterior communicating artery (AcomA). A baseline computational flow dynamics simulation was performed using inflow boundary conditions measured with pcMRI. Intra-aneurysmal flow patterns, maximum, minimum and average values of wall shear stress and wall shear stress histograms were calculated. Five additional computational flow dynamics simulations were performed, in which simulated inflow from the right and left A1 segment was varied, while keeping the total inflow constant. Intra-aneurysmal flow patterns measured with pcMRI were qualitatively compared to intra-aneurysmal flow patterns derived from the simulations.


Intra-aneurysmal flow patterns calculated in the baseline simulation were in good qualitative agreement with pcMRI measurements. Intra-aneurysmal flow patterns and wall shear stress changed considerably when inflow conditions were altered. Changes in the flow distribution between right and left A1 segments caused variations of the averaged wall shear stress as high as 43%.


Intra-aneurysmal flow patterns and wall shear stress in an AcomA aneurysm calculated with computational flow dynamics depended strongly on the flow distribution between A1 segments. Patient-specific flow data measured with pcMRI obtained prior to computational flow dynamics are necessary for an accurate simulation of intra-aneurysmal flow patterns and calculation of wall shear stress in AcomA aneurysms. Further studies may indicate if wall shear stress calculated with computational flow dynamics can predict aneurysm growth and/or rupture.


  1. 1.
    Acevedo-Bolton G, Jou LD, Dispensa BP, Lawton MT, Higashida RT, Martin AJ, Young WL, Saloner D (2006) Estimating the hemodynamic impact of interventional treatments of aneurysms: numerical simulation with experimental validation: technical case report. Neurosurgery 59:E429–E430 author reply E429–430. doi:10.1227/01.NEU.0000223495.39240.9A PubMedCrossRefGoogle Scholar
  2. 2.
    Castro MA, Putman CM, Cebral JR (2006) Patient-specific computational fluid dynamics modeling of anterior communicating artery aneurysms: a study of the sensitivity of intra-aneurysmal flow patterns to flow conditions in the carotid arteries. AJNR Am J Neuroradiol 27:2061–2068PubMedGoogle Scholar
  3. 3.
    Catro MA, Putman CM, Cebral JR (2006) Computational fluid dynamics modeling of intracranial aneurysms: effects of parent artery segmentation on intra-aneurysmal hemodynamics. AJNR Am J Neuroradiol 27:1703–1709Google Scholar
  4. 4.
    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 26:2550–2559PubMedGoogle Scholar
  5. 5.
    Ford MD, Alperin N, Lee SH, Holdsworth DW, Steinman DA (2005) Characterization of volumetric flow rate waveforms in the normal internal carotid and vertebral arteries. Physiol Meas 26:477–488. doi:10.1088/0967-3334/26/4/013 PubMedCrossRefGoogle Scholar
  6. 6.
    Heuer WDC, Marusic I (2005) Turbulence wall-shear stress sensor for the atmospheric surface layer. Meas Sci Technol 16:1644–1649. doi:10.1088/0957-0233/16/8/015 CrossRefGoogle Scholar
  7. 7.
    Jou LD, Wong G, Dispensa B, Lawton MT, Higashida RT, Young WL, Saloner D (2005) Correlation between lumenal geometry changes and hemodynamics in fusiform intracranial aneurysms. AJNR Am J Neuroradiol 26:2357–2363PubMedGoogle Scholar
  8. 8.
    Karmonik C, Klucznik R, Benndorf G (2008) Blood flow in cerebral aneurysms: comparison of phase contrast magnetic resonance and computational fluid dynamics—preliminary experience. Rofo 180:209–215PubMedGoogle Scholar
  9. 9.
    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 16:119–128PubMedGoogle Scholar
  10. 10.
    Kerber CW, Imbesi SG, Knox K (1999) Flow dynamics in a lethal anterior communicating artery aneurysm. AJNR Am J Neuroradiol 20:2000–2003PubMedGoogle Scholar
  11. 11.
    Malek AM, Izumo S (1995) Control of endothelial cell gene expression by flow. J Biomech 28:1515–1528. doi:10.1016/0021-9290(95)00099-2 PubMedCrossRefGoogle Scholar
  12. 12.
    Mantha A, Karmonik C, Benndorf G, Strother C, Metcalfe R (2006) Hemodynamics in a cerebral artery before and after the formation of an aneurysm. AJNR Am J Neuroradiol 27:1113–1118PubMedGoogle Scholar
  13. 13.
    Metcalfe RW (2003) The promise of computational fluid dynamics as a tool for delineating therapeutic options in the treatment of aneurysms. AJNR Am J Neuroradiol 24:553–554PubMedGoogle Scholar
  14. 14.
    Schirmer CM, Malek AM (2007) Prediction of complex flow patterns in intracranial atherosclerotic disease using computational fluid dynamics. Neurosurgery 61:842–851 discussion 852PubMedCrossRefGoogle Scholar
  15. 15.
    Schirmer CM, Malek AM (2007) Wall shear stress gradient analysis within an idealized stenosis using non-Newtonian flow. Neurosurgery 61:853–863 discussion 863–854PubMedCrossRefGoogle Scholar
  16. 16.
    Shojima M, Oshima M, Takagi K, Torii R, Hayakawa M, Katada K, Morita A, Kirino T (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. doi:10.1161/01.STR.0000144648.89172.0f PubMedCrossRefGoogle Scholar
  17. 17.
    Steiger HJ, Poll A, Liepsch DW, Reulen HJ (1988) Haemodynamic stress in terminal aneurysms. Acta Neurochir (Wien) 93:18–23. doi:10.1007/BF01409897 CrossRefGoogle Scholar
  18. 18.
    Venugopal P, Valentino D, Schmitt H, Villablanca JP, Vinuela F, Duckwiler G (2007) Sensitivity of patient-specific numerical simulation of cerebral aneurysm hemodynamics to inflow boundary conditions. J Neurosurg 106:1051–1060. doi:10.3171/jns.2007.106.6.1051 PubMedCrossRefGoogle Scholar
  19. 19.
    Zhao M, Amin-Hanjani S, Ruland S, Curcio AP, Ostergren L, Charbel FT (2007) Regional cerebral blood flow using quantitative MR angiography. AJNR Am J Neuroradiol 28:1470–1473. doi:10.3174/ajnr.A0582 PubMedCrossRefGoogle Scholar
  20. 20.
    Zhao M, Charbel FT, Alperin N, Loth F, Clark ME (2000) Improved phase-contrast flow quantification by three-dimensional vessel localization. Magn Reson Imaging 18:697–706. doi:10.1016/S0730-725X(00)00157-0 PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Christof Karmonik
    • 1
    • 2
    • 3
  • Christopher Yen
    • 4
  • Robert G. Grossman
    • 4
  • Richard Klucznik
    • 2
  • Goetz Benndorf
    • 5
  1. 1.Department of RadiologyThe Methodist Hospital Research InstituteHoustonUSA
  2. 2.Department of RadiologyThe Methodist Hospital Neurological InstituteHoustonUSA
  3. 3.Department of RadiologyWeil Medical College of Cornell UniversityNew YorkUSA
  4. 4.Department of NeurosurgeryThe Methodist Hospital Neurological InstituteHoustonUSA
  5. 5.Department of RadiologyBaylor College of MedicineHoustonUSA

Personalised recommendations