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

PURPOSE: Image-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 ME...

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Autores principales: Park, Soonchan, Lee, Sang-Wook, Lim, Ok Kyun, Min, Inki, Nguyen, Minhtuan, Ko, Young Bae, Yoon, Kyunghwan, Suh, Dae Chul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Interventional Neuroradiology 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3601276/
https://www.ncbi.nlm.nih.gov/pubmed/23515355
http://dx.doi.org/10.5469/neuroint.2013.8.1.23
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author Park, Soonchan
Lee, Sang-Wook
Lim, Ok Kyun
Min, Inki
Nguyen, Minhtuan
Ko, Young Bae
Yoon, Kyunghwan
Suh, Dae Chul
author_facet Park, Soonchan
Lee, Sang-Wook
Lim, Ok Kyun
Min, Inki
Nguyen, Minhtuan
Ko, Young Bae
Yoon, Kyunghwan
Suh, Dae Chul
author_sort Park, Soonchan
collection PubMed
description PURPOSE: Image-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 METHODS: Reconstructed 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. RESULTS: The 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. CONCLUSION: Our 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.
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spelling pubmed-36012762013-03-20 Computational Modeling with Fluid-Structure Interaction of the Severe M1 Stenosis Before and After Stenting Park, Soonchan Lee, Sang-Wook Lim, Ok Kyun Min, Inki Nguyen, Minhtuan Ko, Young Bae Yoon, Kyunghwan Suh, Dae Chul Neurointervention Original Paper PURPOSE: Image-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 METHODS: Reconstructed 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. RESULTS: The 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. CONCLUSION: Our 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. Korean Society of Interventional Neuroradiology 2013-02 2013-02-28 /pmc/articles/PMC3601276/ /pubmed/23515355 http://dx.doi.org/10.5469/neuroint.2013.8.1.23 Text en Copyright © 2013 Korean Society of Interventional Neuroradiology http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Park, Soonchan
Lee, Sang-Wook
Lim, Ok Kyun
Min, Inki
Nguyen, Minhtuan
Ko, Young Bae
Yoon, Kyunghwan
Suh, Dae Chul
Computational Modeling with Fluid-Structure Interaction of the Severe M1 Stenosis Before and After Stenting
title Computational Modeling with Fluid-Structure Interaction of the Severe M1 Stenosis Before and After Stenting
title_full Computational Modeling with Fluid-Structure Interaction of the Severe M1 Stenosis Before and After Stenting
title_fullStr Computational Modeling with Fluid-Structure Interaction of the Severe M1 Stenosis Before and After Stenting
title_full_unstemmed Computational Modeling with Fluid-Structure Interaction of the Severe M1 Stenosis Before and After Stenting
title_short Computational Modeling with Fluid-Structure Interaction of the Severe M1 Stenosis Before and After Stenting
title_sort computational modeling with fluid-structure interaction of the severe m1 stenosis before and after stenting
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3601276/
https://www.ncbi.nlm.nih.gov/pubmed/23515355
http://dx.doi.org/10.5469/neuroint.2013.8.1.23
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