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Computational Modeling with Fluid-Structure Interaction of the Severe M1 Stenosis Before and After Stenting

Authors :
Soonchan Park
Sang-Wook Lee
Ok Kyun Lim
Inki Min
Minhtuan Nguyen
Young Bae Ko
Kyunghwan Yoon
Dae Chul Suh
Source :
Neurointervention, Vol 8, Iss 1, Pp 23-28 (2013)
Publication Year :
2013
Publisher :
Korean Society of Interventional Neuroradiology, 2013.

Abstract

PurposeImage-based computational models with fluid-structure interaction (FSI) can be used to perform plaque mechanical analysis in intracranial artery stenosis. We described a process in FSI study applied to symptomatic severe intracranial (M1) stenosis before and after stenting.Materials and MethodsReconstructed 3D angiography in STL format was transferred to Magics for smoothing of vessel surface and trimming of branch vessels and to HyperMesh for generating tetra volume mesh from triangular surface-meshed 3D angiogram. Computational analysis of blood flow in the blood vessels was performed using the commercial finite element software ADINA Ver 8.5. The distribution of wall shear stress (WSS), peak velocity and pressure was analyzed before and after intracranial stenting.ResultsThe wall shear stress distributions from Computational fluid dynamics (CFD) simulation with rigid wall assumption as well as FSI simulation before and after stenting could be compared. The difference of WSS between rigid wall and compliant wall model both in pre- and post-stent case is only minor except at the stenosis region. These WSS values were greatly reduced after stenting to 15~20 Pa at systole and 3~5 Pa at end-diastole in CFD simulation, which are similar in FSI simulations.ConclusionOur study revealed that FSI simulation before and after intracranial stenting was feasible despite of limited vessel wall dimension and could reveal change of WSS as well as flow velocity and wall pressure.

Details

Language :
English
ISSN :
20939043 and 22336273
Volume :
8
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Neurointervention
Publication Type :
Academic Journal
Accession number :
edsdoj.931ae979acda4779b6b275b423ab6c36
Document Type :
article
Full Text :
https://doi.org/10.5469/neuroint.2013.8.1.23