Patient-specific computational fluid dynamic simulation of cerebrospinal fluid flow in the intracranial space

Fillingham, Patrick; Rane, Swati; Andre, Jalal B.; Parsey, Carolyn; Bindschadler, Michael; Friedman, Seth D.; Kurt, Mehmet; Aliseda, Alberto; Levitt, Michael R.

doi:10.1016/j.brainres.2022.147962

Cited by 5 publications

(12 citation statements)

References 46 publications

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“…Here, fluid flow across a cross-section of the SAS and the cerebral aqueduct showed a good agreement with phase-contrast MRI measurements (Figure 4) with aqueduct flow in the order of 0.11 ml/s and SAS flow in the order of 3.4 ml/s. This model, thereby, produces similar flow amplitudes as obtained in previous studies for the third ventricle and aqueduct in (Sweetman et al, 2011;Wagshul et al, 2011) and the SAS in CFD (Fillingham et al, 2022;Khani et al, 2022) and in vitro studies (Benninghaus et al, 2019). Adding respiration resulted in a pulsatile flow pattern across the foramen magnum with, evidently, the appearance of pulsations at the imposed frequencies (Figure 5) of 1 (cardiac) and 0.2 Hz (respiration).…”

Section: Discussionsupporting

confidence: 77%

“…Maximal velocities at the cerebral aqueduct are 1.2 cm/s, which is in the same order of magnitude as velocity measurements reported in (Matsumae et al, 2019). However, these velocities are lower than those presented in computational studies with maximal velocities of 2.4 cm/s (Sweetman et al, 2011) and 2 cm/s (Fillingham et al, 2022) in the cerebral aqueduct, and in vivo measurements of 3-4 cm/s reported for a reference cohort in (Eide et al, 2021).…”

Section: Discussionsupporting

confidence: 66%

“…In vivo pressure measurements in patients with idiopathic normal pressure hydrocephalus suggest a maximal pressure difference between the subdural and ventricles of 1 mmHg/m or 0.1 mmHg difference between the ventricles and foramen magnum (10 cm) (Vinje et al, 2019). Thus, both our model and Fillingham et al (2022) CFD model seem to underpredict, while the FSI model by Sweetman et al overpredicts the spatial pressure differences. The lower pressure difference in our model might be attributed to the larger diameter of the cerebral aqueduct in our model compared to the physiological diameter.…”

Section: Discussionmentioning

confidence: 62%

“…These spatial pressure differences FIGURE 4 CFD simulation results (blue) and literature measurements (black) of CSF flow through 2 cross-sections: (A) flow through a cross-section of the cerebral aqueduct with literature values reported in and reproduced from (Sweetman et al, 2011, Wagshul et al). (B) Flow through spinal SAS space cross-section with literature values reported in and reproduced from (Tangen et al, 2015;Benninghaus et al, 2019;Fillingham et al, 2022;Khani et al, 2022). Calculated pressures for cases A and B are visualized in Figure 7 for one cardiac cycle together with the average physiological range of 7-15 mmHg (Eide and Kerty, 2011;Dai et al, 2020;Singh and Cheng, 2021).…”

Section: Csf Pressurementioning

confidence: 99%

“…However, the exact processes altering the CSF dynamics and their relation with neurological complications are not well understood, and neither are the normal mechanisms governing CSF dynamics ( Bothwell et al, 2019 ). A deeper understanding of CSF dynamics and the processes affecting them should advance our insight into CSF-related neurological disorders, which is critical to come up with better management and treatment of these disorders ( Fillingham et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Computational fluid dynamics model to predict the dynamical behavior of the cerebrospinal fluid through implementation of physiological boundary conditions

Vandenbulcke

Pauw

Dewaele

et al. 2022

Front. Bioeng. Biotechnol.

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Cerebrospinal fluid (CSF) dynamics play an important role in maintaining a stable central nervous system environment and are influenced by different physiological processes. Multiple studies have investigated these processes but the impact of each of them on CSF flow is not well understood. A deeper insight into the CSF dynamics and the processes impacting them is crucial to better understand neurological disorders such as hydrocephalus, Chiari malformation, and intracranial hypertension. This study presents a 3D computational fluid dynamics (CFD) model which incorporates physiological processes as boundary conditions. CSF production and pulsatile arterial and venous volume changes are implemented as inlet boundary conditions. At the outlets, 2-element windkessel models are imposed to simulate CSF compliance and absorption. The total compliance is first tuned using a 0D model to obtain physiological pressure pulsations. Then, simulation results are compared with in vivo flow measurements in the spinal subarachnoid space (SAS) and cerebral aqueduct, and intracranial pressure values reported in the literature. Finally, the impact of the distribution of and total compliance on CSF pressures and velocities is evaluated. Without respiration effects, compliance of 0.17 ml/mmHg yielded pressure pulsations with an amplitude of 5 mmHg and an average value within the physiological range of 7–15 mmHg. Also, model flow rates were found to be in good agreement with reported values. However, when adding respiration effects, similar pressure amplitudes required an increase of compliance value to 0.51 ml/mmHg, which is within the range of 0.4–1.2 ml/mmHg measured in vivo. Moreover, altering the distribution of compliance over the four different outlets impacted the local flow, including the flow through the foramen magnum. The contribution of compliance to each outlet was directly proportional to the outflow at that outlet. Meanwhile, the value of total compliance impacted intracranial pressure. In conclusion, a computational model of the CSF has been developed that can simulate CSF pressures and velocities by incorporating boundary conditions based on physiological processes. By tuning these boundary conditions, we were able to obtain CSF pressures and flows within the physiological range.

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