Title |
A framework for detailed numerical simulation of patient-specific cerebrospinal fluid flow for relevant clinical appplications : |
Authors |
Misiulis, Edgaras ; Dziugys, Algis ; Barkauskiene, Alina ; Preiksaitis, Aidanas ; Ratkunas, Vytenis ; Skarbalius, Gediminas ; Navakas, Robertas ; Iesmantas, Tomas ; Alzbutas, Robertas ; Lukosevicius, Saulius ; Serpytis, Mindaugas ; Rocka, Saulius ; Lapinskiene, Indre ; Petkus, Vytautas |
DOI |
10.2139/ssrn.4570985 |
Full Text |
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Is Part of |
SSRN: Social science research network: Elsevier’s open preprint server.. Rochester, NY : Elsevier. 2023, p. 1-17 |
Keywords [eng] |
computational fluid dynamics ; cerebrospinal fluid ; subarachnoid space ; patient-specificity ; periarterial space |
Abstract [eng] |
Background and Objectives: Recently, computational fluid dynamics simulations were used to model cerebrospinal fluid (CSF) flow in a simplified subarachnoid space (SAS), in which SAS geometry was coarsened and smoothed, thereby, losing the periarterial spaces and bays common to SAS, which could play a crucial role for the resulting patient’s outcome after subarachnoid hemorrhage (SAH). Our objective was to develop a computational framework for CSF flow numerical finite element method (FEM) based model that incorporates detailed patient-specific cranial CSF space (cCSFS) combining ventricular system, SAS, and periarterial spaces reconstructed from the T2-weighted magnetic resonance imaging (MRI). Following model is useful when considering the clearance of unwanted substances such as leaked blood resulting from the aneurysmal rupture or when considering the drug spreading in CSF spaces. In addition, the inclusion of periarterial spaces allows evaluation of the direct contact between cerebrospinal fluid, parenchyma and artery walls. Methods: A single, healthy, 42 years white old male was enrolled in this study. The 3D Slicer software was used for the cCSFS volume segmentation, while the COMSOL Multiphysics® v6.0 was used for CSF flow pattern prediction. The projection operations were used to analyze the cranial CSF space geometrical and CSF flow parameters. Results: We demonstrated that the computational effort can be significantly reduced without cCSFS coarsening. This was achieved by using first-order finite elements instead of second-order ones. Lower finite elements order resulted in only 1.4 % difference in simulated transmantle pressures, and lower than 10 % difference in momentum in about 90 % of all elements, while the time required to model the CSF flow was reduced about four times from ~20 min to ~5 min, and the random access memory usage was reduced about five times from ~150 GB to ~30 GB. Our model showed that a constant net CSF flow of 500 ml/day was maintained, while the transmantle pressure was 1.8 Pa. Conclusions: The presented CSF flow model holds promise in becoming a viable tool for the timely prediction of CSF flow pathways after SAH. |
Published |
Rochester, NY : Elsevier |
Type |
Journal article |
Language |
English |
Publication date |
2023 |
CC license |
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