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A poroelastic fluid–structure interaction model of syringomyelia

Published online by Cambridge University Press:  10 November 2016

Matthias Heil*
Affiliation:
School of Mathematics, University of Manchester, Oxford Road, Manchester M13 9PL, UK
Christopher D. Bertram
Affiliation:
School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia
*
Email address for correspondence: [email protected]

Abstract

Syringomyelia is a medical condition in which one or more fluid-filled cavities (syrinxes) form in the spinal cord. The syrinxes often form near locations where the spinal subarachnoid space (SSS; the fluid-filled annular region surrounding the spinal cord) is partially obstructed. Previous studies showed that nonlinear interactions between the pulsatile fluid flow in the SSS and the elastic deformation of the tissues surrounding it can generate a fluid pressure distribution that would tend to drive fluid from the SSS into the syrinx if the tissue separating the two regions was porous. This provides a potential explanation for why a partial occlusion of the SSS can induce the growth of an already existing nearby syrinx. We study this hypothesis by analysing the mass transfer between the SSS and the syrinx, using a poroelastic fluid–structure interaction model of the spinal cord that includes a representation of the partially obstructed SSS, the syrinx and the poroelastic tissues surrounding these fluid-filled cavities. Our numerical simulations show that poroelastic fluid–structure interaction can indeed cause an increase (albeit relatively small) in syrinx volume. We analyse the seepage flows and show that their structure can be captured by an analytical model which explains why the increase in syrinx volume tends to be relatively small.

Type
Papers
Copyright
© 2016 Cambridge University Press 

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References

Amestoy, P. R., Duff, I. S., Koster, J. & L’Excellent, J.-Y. 2001 A fully asynchronous multifrontal solver using distributed dynamic scheduling. SIAM J. Matrix Anal. Applics. 23 (1), 1541.CrossRefGoogle Scholar
Badia, S., Quaini, A. & Quarteroni, A. 2009 Coupling Biot and Navier–Stokes equations for modelling fluid-poroelastic media interaction. J. Comput. Phys. 228 (21), 79868014.CrossRefGoogle Scholar
Beavers, G. S. & Joseph, D. D. 1967 Boundary conditions at a naturally permeable wall. J. Fluid Mech. 30, 197207.CrossRefGoogle Scholar
Berkouk, K. K., Carpenter, P. W. & Lucey, A. D. 2003 Pressure wave propagation in fluid-filled co-axial elastic tubes. Part 1: basic theory. Trans. ASME J. Biomech. Engng 125, 852856.CrossRefGoogle ScholarPubMed
Bertram, C. D. 2009 A numerical investigation of waves propagating in the spinal cord and subarachnoid space in the presence of a syrinx. J. Fluids Struct. 25, 11891205.CrossRefGoogle Scholar
Bertram, C. D. 2010 Evaluation by fluid/structure-interaction spinal-cord simulation of the effects of subarachnoid-space stenosis on an adjacent syrinx. Trans. ASME J. Biomech. Engng 132, 115.CrossRefGoogle Scholar
Bertram, C. D., Brodbelt, A. R. & Stoodley, M. A. 2005 The origins of syringomyelia: numerical models of fluid/structure interactions in the spinal cord. Trans. ASME J. Biomech. Engng 127, 10991109.CrossRefGoogle ScholarPubMed
Bertram, C. D. & Heil, M. 2016 A poroelastic fluid/structure-interaction model of cerebrospinal fluid dynamics in the cord with syringomyelia and adjacent subarachnoid-space stenosis. Trans. ASME J. Biomech. Engng 139, 011001,1–10.Google Scholar
Bunck, A. C., Kroger, J.-R., Juttner, A., Brentrup, A., Fiedler, B., Schaarschmidt, F., Crelier, G. R., Schwindt, W., Heindel, W., Niederstadt, T. & Maintz, D. 2011 Magnetic resonance 4D flow characteristics of cerebrospinal fluid at the craniocervical junction and the cervical spinal canal. Eur. Radiol. 21, 17881796.CrossRefGoogle ScholarPubMed
Carpenter, P. W., Berkouk, K. K. & Lucey, A. D. 2003 Pressure wave propagation in fluid-filled co-axial elastic tubes part 2: Mechanisms for the pathogenesis of syringomyelia. Trans. ASME J. Biomech. Engng 125, 857863.CrossRefGoogle ScholarPubMed
Carraro, T., Goll, C., Marciniak-Czochra, A. & Mikelic, A. 2013 Pressure jump interface law for the Stokes–Darcy coupling: confirmation by direct numerical simulations. J. Fluid Mech. 732, 510536.CrossRefGoogle Scholar
Chang, G. L., Hung, T. K. & Feng, W. W. 1988 An in-vivo measurement and analysis of viscoelastic properties of the spinal cord of cats. Trans. ASME J. Biomech. Engng 110, 115122.CrossRefGoogle ScholarPubMed
Cirovic, S. 2009 A coaxial tube model of the cerebrospinal fluid pulse propagation in the spinal column. Trans. ASME J. Biomech. Engng 131, 021008,1–9.CrossRefGoogle ScholarPubMed
Cirovic, S. & Kim, M. 2012 A one-dimensional model of the spinal cerebrospinal-fluid compartment. Trans. ASME J. Biomech. Engng 134, 021005,1–10.CrossRefGoogle ScholarPubMed
Cowin, S. C. 1999 Bone poroelasticity. J. Biomech. 32 (3), 217238.CrossRefGoogle ScholarPubMed
Detournay, E. & Cheng, A.H.-D. 1993 Fundamentals of poroelasticity. In Comprehensive Rock Engineering: Principles, Practice and Projects, Vol. II, Analysis and Design Method (ed. Fairhurst, C.), pp. 113171. Pergamon Press.Google Scholar
Dreha-Kulaczewski, S., Joseph, A. A., Merboldt, K.-D., Ludwig, H.-C., Gärtner, J. & Frahm, J. 2015 Inspiration is the major regulator of human CSF flow. J. Neurosci. 35 (6), 24852491.CrossRefGoogle ScholarPubMed
Elliott, N. J. 2012 Syrinx fluid transport: Modeling pressure-wave-induced flux across the spinal pial membrane. Trans. ASME J. Biomech. Engng 134, 031006,6–9.CrossRefGoogle ScholarPubMed
Elliott, N. S. J., Bertram, C. D., Martin, B. A. & Brodbelt, A. R. 2013 Syringomyelia: a review of the biomechanics. J. Fluids Struct. 40, 124.CrossRefGoogle Scholar
Elliott, N. S. J., Lockerby, D. A. & Brodbelt, A. R. 2009 The pathogenesis of syringomyelia: a re-evaluation of the elastic-jump hypothesis. Trans. ASME J. Biomech. Engng 131, 044503,1–6.CrossRefGoogle ScholarPubMed
Ervin, V. J. 2012 Computational bases for RT k and BDM k on triangles. Comput. Maths Applics. 64, 27652774.CrossRefGoogle Scholar
Ervin, V. J. 2013 Approximation of coupled Stokes–Darcy flow in an axisymmetric domain. Comput. Meth. Appl. Mech. Engng 258, 96108.CrossRefGoogle Scholar
Gupta, S., Soellinger, M., Boesiger, P., Poulikakos, D. & Kurtcuoglu, V. 2009 Three-dimensional computational modeling of subject-specific cerebrospinal fluid flow in the subarachnoid space. Trans. ASME J. Biomech. Engng 131, 021010,1–11.CrossRefGoogle ScholarPubMed
Gupta, S., Soellinger, M., Grzybowski, D. M., Boesiger, P., Biddiscombe, J., Poulikakos, D. & Kurtcuoglu, V. 2010 Cerebrospinal fluid dynamics in the human cranial subarachnoid space: an overlooked mediator of cerebral disease. I: computational model. J. R. Soc. Interface 7, 11951204.CrossRefGoogle ScholarPubMed
Hazel, A. L., Heil, M., Waters, S. L. & Oliver, J. M. 2012 On the liquid lining in fluid-conveying curved tubes. J. Fluid Mech. 705, 213233.CrossRefGoogle Scholar
Heidari Pahlavian, S., Loth, F., Luciano, M., Oshinski, J. & Martin, B. A. 2015 Neural tissue motion impacts cerebrospinal fluid dynamics at the cervical medullary junction: A patient-specific moving-boundary computational model. Ann. Biomed. Engng 43 (12), 29112923.CrossRefGoogle Scholar
Heidari Pahlavian, S., Yiallourou, T., Tubbs, R. S., Bunck, A. C., Loth, F., Goodin, M., Raisee, M. & Martin, B. A. 2014 The impact of spinal cord nerve roots and denticulate ligaments on cerebrospinal fluid dynamics in the cervical spine. PLoS ONE 9 (4), e91888.CrossRefGoogle ScholarPubMed
Heil, M. & Hazel, A. L. 2006 oomph-lib – an object-oriented multi-physics finite-element library. In Fluid-Structure Interaction (ed. Schäfer, M. & Bungartz, H.-J.), pp. 1949. Springer, oomph-lib is available as open-source software at http://www.oomph-lib.org.CrossRefGoogle Scholar
Hentschel, S., Mardal, K.-A., Lovgren, A. E., Linge, S. & Haughton, V. 2010 Characterization of cyclic CSF flow in the foramen magnum and upper cervical spinal canal with MR flow imaging and computational fluid dynamics. Amer. J. Neuroradiol. 31, 9971002.CrossRefGoogle ScholarPubMed
Hewitt, R. E., Hazel, A. L., Clarke, R. J. & Denier, J. P. 2011 Unsteady flow in a torus after a sudden change in rotation rate. J. Fluid Mech. 688, 88119.CrossRefGoogle Scholar
Humphreys, J. D. 2008 Mechanisms of arterial remodelling in hypertension: Coupled roles of wall shear and intramural stress. Hypertension 52 (2), 195200.CrossRefGoogle Scholar
Kistler, S. F. & Scriven, L. E. 1983 Coating flows. In Computational Analysis of Polymer Processing (ed. Pearson, J. R. A. & Richardson, S. M.), pp. 243299. Applied Science Publishers.CrossRefGoogle Scholar
Linninger, A. A., Xenos, M., Zhu, D. C., Somayaji, M. R., Srinivasa Kondapalli, S. & Penn, R. D. 2007 Cerebrospinal fluid flow in the normal and hydrocephalic human brain. IEEE Trans. Biomed. Engng 54 (2), 291302.CrossRefGoogle ScholarPubMed
Loth, F., Yardimci, M. A. & Alperin, N. 2001 Hydrodynamic modeling of cerebrospinal fluid motion within the spinal cavity. Trans. ASME J. Biomech. Engng 123 (1), 7179.CrossRefGoogle ScholarPubMed
Martin, B. A., Labuda, R., Royston, T. J., Oshinski, J. N., Iskandar, B. & Loth, F. 2010 Spinal subarachnoid space pressure measurements in an in vitro spinal stenosis model: implications on syringomyelia theories. Trans. ASME J. Biomech. Engng 132, 111007,1–11.CrossRefGoogle Scholar
Pihler-Puzovic, D., Juel, A., Peng, G. G., Lister, J. R. & Heil, M. 2015 Displacement flows under elastic membranes. Part 1: experiments and direct numerical simulations. J. Fluid Mech. 784, 487511.CrossRefGoogle Scholar
Rossi, C., Boss, A., Steidle, G., Martirosian, P., Klose, U., Capuani, S., Maraviglia, B., Claussen, C. D. & Schick, F. 2008 Water diffusion anisotropy in white and gray matter of the human spinal cord. J. Magn. Reson. Imag. 27 (3), 476482.CrossRefGoogle ScholarPubMed
Saffman, P. 1971 On the boundary condition at the surface of a porous medium. Stud. Appl. Maths 50, 93101.CrossRefGoogle Scholar
Shaffer, N., Martin, B. & Loth, F. 2011 Cerebrospinal fluid hydrodynamics in type I chiari malformation. Neurological Res. 33 (3), 247260.CrossRefGoogle ScholarPubMed
Shewchuk, J. R. 1996 Triangle: engineering a 2D quality mesh generator and delaunay triangulator. In Lecture Notes in Computer Science (ed. Lin, M. C. & Manocha, Dinesh), vol. 1148, pp. 203222. Springer, from the First ACM Workshop on Applied Computational Geometry.Google Scholar
Simon, B. R. 1992 Multiphase poroelastic finite element models for soft tissue structure. Appl. Mech. Rev. 45 (6), 191218.CrossRefGoogle Scholar
Smillie, A., Sobey, I. & Molnar, Z. 2005 A hydroelastic model of hydrocephalus. J. Fluid Mech. 539, 417443.CrossRefGoogle Scholar
Stockman, H. W. 2006 Effect of anatomical fine structure on the flow of cerebrospinal fluid in the spinal subarachnoid space. Trans. ASME J. Biomech. Engng 128, 106114.Google ScholarPubMed
Støverud, K. H., Alnæs, M., Langtangen, H. P., Haughton, V. & Mardal, K.-A.2015 Poro-elastic modeling of syringomyelia–a systematic study of the effects of pia mater, central canal, median fissure, white and gray matter on pressure wave propagation and fluid movement within the cervical spinal cord. Comput. Meth. Biomech. Biomed. Engng, pp. 1–13.Google Scholar
Tully, B. & Ventikos, Y. 2011 Cerebral water transport using multiple-network poroelastic theory: application to normal pressure hydrocephalus. J. Fluid Mech. 667, 188215.CrossRefGoogle Scholar
van de Vosse, F. N. & Stergiopulos, N. 2011 Pulse wave propagation in the arterial tree. Annu. Rev. Fluid Mech. 43 (1), 467499.CrossRefGoogle Scholar
Yiallourou, T. I., Kröger, J. R., Stergiopulos, N., Maintz, D., Martin, B. A. & Bunck, A. C. 2015 Quantitative comparison of 4D MRI flow measurements to 3D computational fluid dynamics simulation of cerebrospinal fluid movement in the spinal subarachnoid space. PLoS ONE 7, e52284.CrossRefGoogle Scholar