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Computational Fluid Dynamics for the Assessment of Cerebrospinal Fluid Flow and Its Coupling with Cerebral Blood Flow

  • Vartan KurtcuogluEmail author
Chapter
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)

Abstract

The dynamics of cerebrospinal fluid flow are directly linked to those of the ­cardiovascular system. The heart not only drives blood flow, but is also at the origin of CSF pulsation through the expansion and contraction of cerebral blood vessels. As was detailed in the preceding chapter, CSF dynamics can be altered by diseases and conditions such as hydrocephalus and, in turn, CSF dynamics can be analyzed to aid in the diagnosis of these. Bulk models describing intracranial fluid dynamics and punctual flow measurements using MRI have thus become important tools for this purpose.

Keywords

Permeability Porosity Pyramid Hydrocephalus Incompressibility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

Swiss National Science Foundation through SmartShunt – The Hydrocephalus Project.

References

  1. 1.
    Jacobson, E.E., Fletcher, D.F., Morgan, M.K., et al.: Fluid dynamics of the cerebral aqueduct. Pediatr. Neurosurg. 24(5), 229–236 (1996)CrossRefGoogle Scholar
  2. 2.
    Ballester, M.A.G., Zisserman, A., Brady, M.: Segmentation and measurement of brain structures in MRI including confidence bounds. Med. Image Anal. 4(3), 189–200 (2000)CrossRefGoogle Scholar
  3. 3.
    Gupta, S., Soellinger, M., Boesiger, P., et al.: Three-dimensional computational modeling of subject-specific cerebrospinal fluid flow in the subarachnoid space. J. Biomech. Eng. 131(2), 021010 (2009). doi: 10.1115/1.3005171 CrossRefGoogle Scholar
  4. 4.
    Gupta, S., Soellinger, M., Grzybowski, D.M., et al.: Cerebrospinal fluid dynamics in the human cranial subarachnoid space: an overlooked mediator of cerebral disease. I. Computational model. J. R. Soc. Interface 7(49), 1195–1204 (2010). doi: rsif.2010.0033 [pii]10.1098/rsif.2010.0033 CrossRefGoogle Scholar
  5. 5.
    Allen, D.J., Low, F.N.: Scanning electron microscopy of the subarachnoid space in the dog. III. Cranial levels. J. Comp. Neurol. 161(4), 515–539 (1975). doi: 10.1002/cne.901610404 CrossRefGoogle Scholar
  6. 6.
    Cloyd, M.W., Low, F.N.: Scanning electron microscopy of the subarachnoid space in the dog. I. Spinal cord levels. J. Comp. Neurol. 153(4), 325–368 (1974). doi: 10.1002/cne.901530402 CrossRefGoogle Scholar
  7. 7.
    Killer, H.E., Laeng, H.R., Flammer, J., et al.: Architecture of arachnoid trabeculae, pillars, and septa in the subarachnoid space of the human optic nerve: anatomy and clinical considerations. Br. J. Ophthalmol. 87(6), 777–781 (2003)CrossRefGoogle Scholar
  8. 8.
    Vandenwesthuizen, J., Duplessis, J.P.: Quantification of unidirectional fiber bed permeability. J. Compos. Mater. 28(7), 619–637 (1994)Google Scholar
  9. 9.
    Chai, Z.H., Shi, B.C., Lu, J.H., et al.: Non-Darcy flow in disordered porous media: a lattice Boltzmann study. Comput. Fluids 39(10), 2069–2077 (2010). doi: 10.1016/j.compfluid.2010.07.012 MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kang, Q.J., Zhang, D.X., Chen, S.Y.: Unified lattice Boltzmann method for flow in multiscale porous media. Phys Rev E 66(5), 056307 (2002). doi: 10.1103/Physreve.66.056307 ADSCrossRefGoogle Scholar
  11. 11.
    Lesage, D., Angelini, E.D., Bloch, I., et al.: A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13(6), 819–845 (2009). doi: 10.1016/j.media.2009.07.011 CrossRefGoogle Scholar
  12. 12.
    Hjelle, Ø., Dæhlen, M.: Triangulations and Applications. Mathematics and Visualization. Springer, Berlin (2006)Google Scholar
  13. 13.
    De Boor, C.: A Practical Guide to Splines. Applied Mathematical Sciences, vol 27, Rev. edn. Springer, New York (2001)Google Scholar
  14. 14.
    Hadaczek, P., Yamashita, Y., Mirek, H., et al.: The “perivascular pump” driven by arterial pulsation is a powerful mechanism for the distribution of therapeutic molecules within the brain. Mol. Ther. 14(1), 69–78 (2006). doi: S1525-0016(06)00113-4[pii]10.1016/j.ymthe.2006.02.018 CrossRefGoogle Scholar
  15. 15.
    Mittal, R., Iaccarino, G.: Immersed boundary methods. Annu. Rev. Fluid Mech. 37, 239–261 (2005). doi: 10.1146/annurev.fluid.37.061903.175743 MathSciNetADSCrossRefGoogle Scholar
  16. 16.
    Spiegel, M., Redel, T., Zhang, Y.J., et al.: Tetrahedral vs. polyhedral mesh size evaluation on flow velocity and wall shear stress for cerebral hemodynamic simulation. Comput. Meth. Biomech. Biomed. Eng. 14(1), 9–22 (2011). doi: 10.1080/10255842.2010.518565 CrossRefGoogle Scholar
  17. 17.
    Haslam, M., Zamir, M.: Pulsatile flow in tubes of elliptic gross sections. Ann. Biomed. Eng. 26(5), 780–787 (1998)CrossRefGoogle Scholar
  18. 18.
    Gupta, S., Poulikakos, D., Kurtcuoglu, V.: Analytical solution for pulsatile viscous flow in a straight elliptic annulus and application to the motion of the cerebrospinal fluid. Phys. Fluids 20(9), 093607 (2008). doi: 10.1063/1.2988858 ADSCrossRefGoogle Scholar
  19. 19.
    Kurtcuoglu, V., Soellinger, M., Summers, P., et al.: Computational investigation of subject-specific cerebrospinal fluid flow in the third ventricle and aqueduct of Sylvius. J. Biomech. 40(6), 1235–1245 (2007). doi: S0021-9290(06)00215-6[pii]10.1016/j.jbiomech.2006.05.031 CrossRefGoogle Scholar
  20. 20.
    Boutsianis, E., Gupta, S., Boomsma, K., et al.: Boundary conditions by Schwarz-Christoffel mapping in anatomically accurate hemodynamics. Ann. Biomed. Eng. 36(12), 2068–2084 (2008). doi: 10.1007/s10439-008-9571-3 CrossRefGoogle Scholar
  21. 21.
    Soellinger, M., Rutz, A.K., Kozerke, S., et al.: 3D cine displacement-encoded MRI of pulsatile brain motion. Magn. Reson. Med. 61(1), 153–162 (2009). doi: 10.1002/mrm.21802 CrossRefGoogle Scholar
  22. 22.
    Shannon, C.E.: Communication in the presence of noise. Proc. Inst. Radio Eng. 37(1), 10–21 (1949)MathSciNetGoogle Scholar
  23. 23.
    Luke, H.D.: The origins of the sampling theorem. IEEE Commun. Mag. 37(4), 106–108 (1999)CrossRefGoogle Scholar
  24. 24.
    Greitz, D., Greitz, T., Hindmarsh, T.: A new view on the CSF-circulation with the potential for pharmacological treatment of childhood hydrocephalus. Acta Paediatr. 86(2), 125–132 (1997)CrossRefGoogle Scholar
  25. 25.
    Koh, L., Zakharov, A., Johnston, M.: Integration of the subarachnoid space and lymphatics: is it time to embrace a new concept of cerebrospinal fluid absorption? Cerebrospinal Fluid Res. 2, 6 (2005). doi: 10.1186/1743-8454-2-6 CrossRefGoogle Scholar
  26. 26.
    Grzybowski, D.M., Herderick, E.E., Kapoor, K.G., et al.: Human arachnoid granulations. Part I: a technique for quantifying area and distribution on the superior surface of the cerebral cortex. Cerebrospinal Fluid Res. 4, 6 (2007). doi: 10.1186/1743-8454-4-6 CrossRefGoogle Scholar
  27. 27.
    Holman, D.W., Kurtcuoglu, V., Grzybowski, D.M.: Cerebrospinal fluid dynamics in the human cranial subarachnoid space: an overlooked mediator of cerebral disease. II. In vitro arachnoid outflow model. J. R. Soc. Interface 7(49), 1205–1218 (2010). doi: rsif.2010.0032 [pii]10.1098/rsif.2010.0032 CrossRefGoogle Scholar
  28. 28.
    Bateman, G.: Hyperemic hydrocephalus: a new form of childhood hydrocephalus analogous to hyperemic intracranial hypertension in adults. J. Neurosurg. Pediatr. 5(1), 20–26 (2010). doi: 10.3171/2009.8.PEDS09204 CrossRefGoogle Scholar
  29. 29.
    Knight, J., Olgac, U., Saur, S.C., et al.: Choosing the optimal wall shear parameter for the prediction of plaque location-A patient-specific computational study in human right coronary arteries. Atherosclerosis 211(2), 445–450 (2010). doi: 10.1016/j.atherosclerosis.2010.03.001 CrossRefGoogle Scholar
  30. 30.
    Bloomfield, I.G., Johnston, I.H., Bilston, L.E.: Effects of proteins, blood cells and glucose on the viscosity of cerebrospinal fluid. Pediatr. Neurosurg. 28(5), 246–251 (1998)CrossRefGoogle Scholar
  31. 31.
    Brydon, H.L., Hayward, R., Harkness, W., et al.: Physical properties of cerebrospinal fluid of relevance to shunt function. 1: the effect of protein upon CSF viscosity. Br. J. Neurosurg. 9(5), 639–644 (1995)CrossRefGoogle Scholar
  32. 32.
    Chen, S., Doolen, G.D.: Lattice Boltzmann method for fluid flows. Annu. Rev. Fluid Mech. 30, 329–364 (1998)MathSciNetADSCrossRefGoogle Scholar
  33. 33.
    Jacobson, E.E., Fletcher, D.F., Morgan, M.K., et al.: Computer modelling of the cerebrospinal fluid flow dynamics of aqueduct stenosis. Med. Biol. Eng. Comput. 37(1), 59–63 (1999)CrossRefGoogle Scholar
  34. 34.
    Fin, L., Grebe, R.: Three dimensional modeling of the cerebrospinal fluid dynamics and brain interactions in the aqueduct of sylvius. Comput. Methods Biomech. Biomed. Engin. 6(3), 163–170 (2003). doi: 10.1080/1025584031000097933016C8J55KCLQF4JT[pii] CrossRefGoogle Scholar
  35. 35.
    Kurtcuoglu, V., Poulikakos, D., Ventikos, Y.: Computational modeling of the mechanical behavior of the cerebrospinal fluid system. J. Biomech. Eng. 127(2), 264–269 (2005)CrossRefGoogle Scholar
  36. 36.
    Du Boulay, G., O’Connell, J., Currie, J., et al.: Further investigations on pulsatile movements in the cerebrospinal fluid pathways. Acta Radiol. Diagn. (Stockh) 13, 496–523 (1972)Google Scholar
  37. 37.
    Kurtcuoglu, V., Soellinger, M., Summers, P., et al.: Mixing and modes of mass transfer in the third cerebral ventricle: a computational analysis. J. Biomech. Eng. 129(5), 695–702 (2007). doi: 10.1115/1.2768376 CrossRefGoogle Scholar
  38. 38.
    Tricoire, H., Locatelli, A., Chemineau, P., et al.: Melatonin enters the cerebrospinal fluid through the pineal recess. Endocrinology 143(1), 84–90 (2002)CrossRefGoogle Scholar
  39. 39.
    Cheng, S., Tan, K., Bilston, L.E.: The effects of the interthalamic adhesion position on cerebrospinal fluid dynamics in the cerebral ventricles. J. Biomech. 43(3), 579–582 (2010). doi: S0021-9290(09)00566-1[pii]10.1016/j.jbiomech.2009.10.002 CrossRefGoogle Scholar
  40. 40.
    Howden, L., Giddings, D., Power, H., et al.: Three-dimensional cerebrospinal fluid flow within the human ventricular system. Comput. Methods Biomech. Biomed. Engin. 11(2), 123–133 (2008). doi: 785046036[pii]10.1080/10255840701492118 CrossRefGoogle Scholar
  41. 41.
    Linninger, A.A., Xenos, M., Zhu, D.C., et al.: Cerebrospinal fluid flow in the normal and hydrocephalic human brain. IEEE Trans. Biomed. Eng. 54(2), 291–302 (2007). doi: 10.1109/TBME.2006.886853 CrossRefGoogle Scholar
  42. 42.
    Sweetman, B., Linninger, A.A.: Cerebrospinal fluid flow dynamics in the central nervous system. Ann. Biomed. Eng. 39(1), 484–496 (2011). doi: 10.1007/s10439-010-0141-0 CrossRefGoogle Scholar
  43. 43.
    Sweetman, B., Xenos, M., Zitella, L., et al.: Three-dimensional computational prediction of cerebrospinal fluid flow in the human brain. Comput. Biol. Med. 41(2), 67–75 (2011). doi: 10.1016/j.compbiomed.2010.12.001 CrossRefGoogle Scholar
  44. 44.
    Loth, F., Yardimci, M.A., Alperin, N.: Hydrodynamic modeling of cerebrospinal fluid motion within the spinal cavity. J. Biomech. Eng. 123(1), 71–79 (2001)CrossRefGoogle Scholar
  45. 45.
    The Visible Human Project. 1997. http://www.nlm.nih.gov/research/visible/visible_human.html. Accessed February 2011
  46. 46.
    Stockman, H.W.: Effect of anatomical fine structure on the flow of cerebrospinal fluid in the spinal subarachnoid space. J. Biomech. Eng. 128(1), 106–114 (2006)CrossRefGoogle Scholar
  47. 47.
    Bilston, L.E., Fletcher, D.F., Stoodley, M.A.: Focal spinal arachnoiditis increases subarachnoid space pressure: a computational study. Clin. Biomech. (Bristol, Avon) 21(6), 579–584 (2006)CrossRefGoogle Scholar
  48. 48.
    Hentschel, S., Mardal, K.A., Lovgren, A.E., et al.: Characterization of cyclic CSF flow in the foramen magnum and upper cervical spinal canal with MR flow imaging and computational fluid dynamics. AJNR Am. J. Neuroradiol. 31(6), 997–1002 (2010). doi: ajnr.A1995 [pii] 10.3174/ajnr.A1995 CrossRefGoogle Scholar
  49. 49.
    Linge, S.O., Haughton, V., Lovgren, A.E., et al.: CSF flow dynamics at the craniovertebral junction studied with an idealized model of the subarachnoid space and computational flow analysis. AJNR Am. J. Neuroradiol. 31(1), 185–192 (2010). doi: ajnr.A1766 [pii] 10.3174/ajnr.A1766 CrossRefGoogle Scholar
  50. 50.
    Kuttler, A., Dimke, T., Kern, S., et al.: Understanding pharmacokinetics using realistic computational models of fluid dynamics: biosimulation of drug distribution within the CSF space for intrathecal drugs. J. Pharmacokinet. Pharmacodyn. 37(6), 629–644 (2010). doi: 10.1007/s10928-010-9184-y CrossRefGoogle Scholar
  51. 51.
    Riley, N.: Steady streaming. Annu. Rev. Fluid Mech. 33, 43–65 (2001)MathSciNetADSCrossRefGoogle Scholar
  52. 52.
    Bilston, L.E., Fletcher, D.F., Brodbelt, A.R., et al.: Arterial pulsation-driven cerebrospinal fluid flow in the perivascular space: a computational model. Comput. Methods Biomech. Biomed. Engin. 6(4), 235–241 (2003)CrossRefGoogle Scholar
  53. 53.
    Wang, P., Olbricht, W.L.: Fluid mechanics in the perivascular space. J. Theor. Biol. (2011). doi: 10.1016/j.jtbi.2011.01.014 Google Scholar
  54. 54.
    Bilston, L.E., Stoodley, M.A., Fletcher, D.F.: The influence of the relative timing of arterial and subarachnoid space pulse waves on spinal perivascular cerebrospinal fluid flow as a possible factor in syrinx development. J. Neurosurg. 112(4), 808–813 (2010). doi: 10.3171/2009.5.JNS08945 CrossRefGoogle Scholar
  55. 55.
    Tully, B., Ventikos, Y.: Coupling poroelasticity and CFD for cerebrospinal fluid hydrodynamics. IEEE Trans. Biomed. Eng. 56(6), 1644–1651 (2009). doi: 10.1109/TBME.2009.2016427 CrossRefGoogle Scholar
  56. 56.
    Sarntinoranont, M., Chen, X., Zhao, J., et al.: Computational model of interstitial transport in the spinal cord using diffusion tensor imaging. Ann. Biomed. Eng. 34(8), 1304–1321 (2006). doi: 10.1007/s10439-006-9135-3 CrossRefGoogle Scholar
  57. 57.
    Tillmann, B.: Atlas der Anatomie des Menschen. Springer, Berlin (2005)Google Scholar
  58. 58.
    Schmid Daners, M., Knobloch, V., Soellinger, M., et al.: Age-specific characteristics and coupling of cerebral arterial inflow and cerebrospinal fluid dynamics (2011)Google Scholar
  59. 59.
    Schley, D., Carare-Nnadi, R., Please, C.P., et al.: Mechanisms to explain the reverse perivascular transport of solutes out of the brain. J Theor Biol. 238(4), 962–974 (2006). doi: 10.1016/j.jtbi.2005.07.005 Google Scholar

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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Laboratory of Thermodynamics in Emerging Technologies, Department of Mechanical and Process EngineeringETH ZurichZurichSwitzerland

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