9 Modelling of Cerebrospinal Fluid Flow by Computational Fluid Dynamics
235
11. Heidari Pahlavian, S., Yiallourou, T., Tubbs, R.S., Bunck, A.C., Loth, F., Goodin, M.,
Raisee, M., Martin, B.A.: The impact of spinal cord nerve roots and denticulate ligaments
on cerebrospinal fluid dynamics in the cervical spine. PLoS One. 9(4), e91888 (2014)
12. Sass, L.R., Khani, M., Natividad, G.C., Tubbs, R.S., Baledent, O., Martin, B.A.: A 3D subjectspecific model of the spinal subarachnoid space with anatomically realistic ventral and dorsal
spinal cord nerve rootlets. Fluids Barriers CNS. 14(1), 36 (2017)
13. 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)
14. 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)
15. Killer, H.E., Laeng, H.R., Flammer, J., Groscurth, P.: 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)
16. Scott, G.G., Coats, B.: Microstructural characterization of the pia-arachnoid complex using
optical coherence tomography. IEEE Trans. Med. Imaging. 34(7), 1452–1459 (2015)
17. Mortazavi, M.M., Quadri, S.A., Khan, M.A., Gustin, A., Suriya, S.S., Hassanzadeh, T.,
Fahimdanesh, K.M., Adl, F.H., Fard, S.A., Taqi, M.A., Armstrong, I., Martin, B.A., Tubbs,
R.S.: Subarachnoid trabeculae: a comprehensive review of their embryology, histology,
morphology, and surgical significance. World Neurosurg. 111, 279–290 (2018)
18. Vandenwesthuizen, J., Duplessis, J.P.: Quantification of unidirectional fiber bed permeability.
J. Compos. Mater. 28(7), 619–637 (1994)
19. Chai, Z.H., Shi, B.C., Lu, J.H., Guo, Z.L.: Non-Darcy flow in disordered porous media: a
lattice Boltzmann study. Comput. Fluids. 39(10), 2069–2077 (2010)
20. Tully, B., Ventikos, Y.: Cerebral water transport using multiple-network poroelastic theory:
application to normal pressure hydrocephalus. J. Fluid Mech. 667, 188–215 (2011)
21. 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)
22. Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen
segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13(6),
819–845 (2009)
23. Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: Userguided 3D active contour segmentation of anatomical structures: significantly improved
efficiency and reliability. NeuroImage. 31(3), 1116–1128 (2006)
24. The Vascular Modeling Toolkit. Available from: www.vmtk.org
25. Jain, K.: Transition to turbulence in physiological flows: Direct numerical simulation of
hemodynamics in intracranial aneurysms and cerebrospinal fluid hydrodynamics in the spinal
canal. 2016, Universität Siegen
26. Almotiri, J., Elleithy, K., Elleithy, A.: Retinal vessels segmentation techniques and algorithms: a survey. Appl. Sci. 8(2), 155 (2018)
27. Simonovsky, M., et al.: A deep metric for multimodal registration. In: 19th International
Conference on Medical Image Computing and Computer-Assisted Intervention. Springer,
Athens, Greece (2016)
28. Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain
MRI segmentation: state of the art and future directions. J. Digit. Imaging. 30(4), 449–459
(2017)
29. Hjelle, Ø., Dæhlen, M.: Triangulations and applications. In: Mathematics and Visualization,
p. 234. Springer-Verlag, Berlin/New York (2006)
30. De Boor, C.: A practical guide to splines. In: Applied Mathematical Sciences, Rev edn, p.
346. Springer. xviii, New York (2001)
31. Helgeland, A., Mardal, K.A., Haughton, V., Reif, B.A.: Numerical simulations of the
pulsating flow of cerebrospinal fluid flow in the cervical spinal canal of a Chiari patient.
J. Biomech. 47(5), 1082–1090 (2014)
32. Kurtcuoglu, V., Soellinger, M., Summers, P., Boomsma, K., Poulikakos, D., Boesiger, P.,
Ventikos, Y.: Computational investigation of subject-specific cerebrospinal fluid flow in the
third ventricle and aqueduct of Sylvius. J. Biomech. 40(6), 1235–1245 (2007)
235
11. Heidari Pahlavian, S., Yiallourou, T., Tubbs, R.S., Bunck, A.C., Loth, F., Goodin, M.,
Raisee, M., Martin, B.A.: The impact of spinal cord nerve roots and denticulate ligaments
on cerebrospinal fluid dynamics in the cervical spine. PLoS One. 9(4), e91888 (2014)
12. Sass, L.R., Khani, M., Natividad, G.C., Tubbs, R.S., Baledent, O., Martin, B.A.: A 3D subjectspecific model of the spinal subarachnoid space with anatomically realistic ventral and dorsal
spinal cord nerve rootlets. Fluids Barriers CNS. 14(1), 36 (2017)
13. 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)
14. 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)
15. Killer, H.E., Laeng, H.R., Flammer, J., Groscurth, P.: 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)
16. Scott, G.G., Coats, B.: Microstructural characterization of the pia-arachnoid complex using
optical coherence tomography. IEEE Trans. Med. Imaging. 34(7), 1452–1459 (2015)
17. Mortazavi, M.M., Quadri, S.A., Khan, M.A., Gustin, A., Suriya, S.S., Hassanzadeh, T.,
Fahimdanesh, K.M., Adl, F.H., Fard, S.A., Taqi, M.A., Armstrong, I., Martin, B.A., Tubbs,
R.S.: Subarachnoid trabeculae: a comprehensive review of their embryology, histology,
morphology, and surgical significance. World Neurosurg. 111, 279–290 (2018)
18. Vandenwesthuizen, J., Duplessis, J.P.: Quantification of unidirectional fiber bed permeability.
J. Compos. Mater. 28(7), 619–637 (1994)
19. Chai, Z.H., Shi, B.C., Lu, J.H., Guo, Z.L.: Non-Darcy flow in disordered porous media: a
lattice Boltzmann study. Comput. Fluids. 39(10), 2069–2077 (2010)
20. Tully, B., Ventikos, Y.: Cerebral water transport using multiple-network poroelastic theory:
application to normal pressure hydrocephalus. J. Fluid Mech. 667, 188–215 (2011)
21. 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)
22. Lesage, D., Angelini, E.D., Bloch, I., Funka-Lea, G.: A review of 3D vessel lumen
segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13(6),
819–845 (2009)
23. Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: Userguided 3D active contour segmentation of anatomical structures: significantly improved
efficiency and reliability. NeuroImage. 31(3), 1116–1128 (2006)
24. The Vascular Modeling Toolkit. Available from: www.vmtk.org
25. Jain, K.: Transition to turbulence in physiological flows: Direct numerical simulation of
hemodynamics in intracranial aneurysms and cerebrospinal fluid hydrodynamics in the spinal
canal. 2016, Universität Siegen
26. Almotiri, J., Elleithy, K., Elleithy, A.: Retinal vessels segmentation techniques and algorithms: a survey. Appl. Sci. 8(2), 155 (2018)
27. Simonovsky, M., et al.: A deep metric for multimodal registration. In: 19th International
Conference on Medical Image Computing and Computer-Assisted Intervention. Springer,
Athens, Greece (2016)
28. Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain
MRI segmentation: state of the art and future directions. J. Digit. Imaging. 30(4), 449–459
(2017)
29. Hjelle, Ø., Dæhlen, M.: Triangulations and applications. In: Mathematics and Visualization,
p. 234. Springer-Verlag, Berlin/New York (2006)
30. De Boor, C.: A practical guide to splines. In: Applied Mathematical Sciences, Rev edn, p.
346. Springer. xviii, New York (2001)
31. Helgeland, A., Mardal, K.A., Haughton, V., Reif, B.A.: Numerical simulations of the
pulsating flow of cerebrospinal fluid flow in the cervical spinal canal of a Chiari patient.
J. Biomech. 47(5), 1082–1090 (2014)
32. Kurtcuoglu, V., Soellinger, M., Summers, P., Boomsma, K., Poulikakos, D., Boesiger, P.,
Ventikos, Y.: Computational investigation of subject-specific cerebrospinal fluid flow in the
third ventricle and aqueduct of Sylvius. J. Biomech. 40(6), 1235–1245 (2007)