234
V. Kurtcuoglu et al.
and spinal subarachnoid spaces. We can state with confidence that CFD has become
an important tool for CSF dynamics research.
At the same time, further efforts are needed to characterize CSF space geometry
and boundary deformation, to optimize inclusion of microstructures and coupling
of CSF with interstitial fluid and to model the connection to the lymphatic system.
Overarching developments in CFD, namely, more efficient use of HPC resources,
must be adopted to enable large-scale calculations of CSF dynamics.
CFD complements and extends MRI. For example, while subject-specific CFD
is dependent to a large extent on anatomic and physiologic data derived from
magnetic resonance imaging, information on CSF pressure gradients cannot be
derived from MRI without flow computations. What we will likely see in the
future is the integration of CFD in MRI sequences and scanner software, ensuring
mass and momentum conservation while making use of measured velocities in
the entire domain rather than only at selected boundaries. Stand-alone CFD will
continue to play a major role when it comes to, for example, investigating the effects
of virtually imposed changes in physiology and anatomy, calculating transport
processes, optimizing medical devices and surgical interventions and interpreting
in vivo tracer studies.
References
1. Hladky, S.B., Barrand, M.A.: Mechanisms of fluid movement into, through and out of the
brain: evaluation of the evidence. Fluids Barriers CNS. 11(1), 26 (2014)
2. Linninger, A.A., Tangen, K., Hsu, C.-Y., Frim, D.: Cerebrospinal fluid mechanics and its
coupling to cerebrovascular dynamics. Annu. Rev. Fluid Mech. 48(1), 219–257 (2016)
3. Brinker, T., Stopa, E., Morrison, J., Klinge, P.: A new look at cerebrospinal fluid circulation.
Fluids Barriers CNS. 11, 10 (2014)
4. 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)
5. Brydon, H.L., Hayward, R., Harkness, W., Bayston, R.: 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)
6. Jacobson, E.E., Fletcher, D.F., Morgan, M.K., Johnston, I.H.: Fluid dynamics of the cerebral
aqueduct. Pediatr. Neurosurg. 24(5), 229–236 (1996)
7. Caiazzo, A., Vignon-Clementel, I.E.: Mathematical modeling of blood flow in the cardiovascular system. In: Sack, I., Schaeffter, T. (eds.) Quantification of Biophysical Parameters in
Medical Imaging, pp. 45–70. Springer, Cham (2018)
8. 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)
9. Gupta, S., Soellinger, M., Boesiger, P., Poulikakos, D., Kurtcuoglu, V.: Three-dimensional
computational modeling of subject-specific cerebrospinal fluid flow in the subarachnoid
space. J. Biomech. Eng. 131(2), 021010 (2009)
10. Gupta, S., Soellinger, M., Grzybowski, D.M., Boesiger, P., Biddiscombe, J., Poulikakos, D.,
Kurtcuoglu, V.: 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)
V. Kurtcuoglu et al.
and spinal subarachnoid spaces. We can state with confidence that CFD has become
an important tool for CSF dynamics research.
At the same time, further efforts are needed to characterize CSF space geometry
and boundary deformation, to optimize inclusion of microstructures and coupling
of CSF with interstitial fluid and to model the connection to the lymphatic system.
Overarching developments in CFD, namely, more efficient use of HPC resources,
must be adopted to enable large-scale calculations of CSF dynamics.
CFD complements and extends MRI. For example, while subject-specific CFD
is dependent to a large extent on anatomic and physiologic data derived from
magnetic resonance imaging, information on CSF pressure gradients cannot be
derived from MRI without flow computations. What we will likely see in the
future is the integration of CFD in MRI sequences and scanner software, ensuring
mass and momentum conservation while making use of measured velocities in
the entire domain rather than only at selected boundaries. Stand-alone CFD will
continue to play a major role when it comes to, for example, investigating the effects
of virtually imposed changes in physiology and anatomy, calculating transport
processes, optimizing medical devices and surgical interventions and interpreting
in vivo tracer studies.
References
1. Hladky, S.B., Barrand, M.A.: Mechanisms of fluid movement into, through and out of the
brain: evaluation of the evidence. Fluids Barriers CNS. 11(1), 26 (2014)
2. Linninger, A.A., Tangen, K., Hsu, C.-Y., Frim, D.: Cerebrospinal fluid mechanics and its
coupling to cerebrovascular dynamics. Annu. Rev. Fluid Mech. 48(1), 219–257 (2016)
3. Brinker, T., Stopa, E., Morrison, J., Klinge, P.: A new look at cerebrospinal fluid circulation.
Fluids Barriers CNS. 11, 10 (2014)
4. 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)
5. Brydon, H.L., Hayward, R., Harkness, W., Bayston, R.: 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)
6. Jacobson, E.E., Fletcher, D.F., Morgan, M.K., Johnston, I.H.: Fluid dynamics of the cerebral
aqueduct. Pediatr. Neurosurg. 24(5), 229–236 (1996)
7. Caiazzo, A., Vignon-Clementel, I.E.: Mathematical modeling of blood flow in the cardiovascular system. In: Sack, I., Schaeffter, T. (eds.) Quantification of Biophysical Parameters in
Medical Imaging, pp. 45–70. Springer, Cham (2018)
8. 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)
9. Gupta, S., Soellinger, M., Boesiger, P., Poulikakos, D., Kurtcuoglu, V.: Three-dimensional
computational modeling of subject-specific cerebrospinal fluid flow in the subarachnoid
space. J. Biomech. Eng. 131(2), 021010 (2009)
10. Gupta, S., Soellinger, M., Grzybowski, D.M., Boesiger, P., Biddiscombe, J., Poulikakos, D.,
Kurtcuoglu, V.: 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)