Vortex Analysis of Intra-Aneurismal Flow in Cerebral Aneurysms
This study aims to develop an alternative vortex analysis method by measuring structure ofIntracranial aneurysm (IA) flow vortexes across the cardiac cycle, to quantify temporal stability of aneurismal flow. Hemodynamics were modeled in “patient-specific” geometries, using computational fluid dynami...
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Series: | Computational and Mathematical Methods in Medicine |
Online Access: | http://dx.doi.org/10.1155/2016/7406215 |
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doaj-8c7c7ec651ab457289af798ba518e70f2020-11-24T23:18:47ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182016-01-01201610.1155/2016/74062157406215Vortex Analysis of Intra-Aneurismal Flow in Cerebral AneurysmsKevin Sunderland0Christopher Haferman1Gouthami Chintalapani2Jingfeng Jiang3Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USADepartment of Biomedical Engineering, Michigan Technological University, Houghton, MI, USASiemens Medical Solution USA, Inc., Heffernan Estate, IL, USADepartment of Biomedical Engineering, Michigan Technological University, Houghton, MI, USAThis study aims to develop an alternative vortex analysis method by measuring structure ofIntracranial aneurysm (IA) flow vortexes across the cardiac cycle, to quantify temporal stability of aneurismal flow. Hemodynamics were modeled in “patient-specific” geometries, using computational fluid dynamics (CFD) simulations. Modified versions of known λ2 and Q-criterion methods identified vortex regions; then regions were segmented out using the classical marching cube algorithm. Temporal stability was measured by the degree of vortex overlap (DVO) at each step of a cardiac cycle against a cycle-averaged vortex and by the change in number of cores over the cycle. No statistical differences exist in DVO or number of vortex cores between 5 terminal IAs and 5 sidewall IAs. No strong correlation exists between vortex core characteristics and geometric or hemodynamic characteristics of IAs. Statistical independence suggests this proposed method may provide novel IA information. However, threshold values used to determine the vortex core regions and resolution of velocity data influenced analysis outcomes and have to be addressed in future studies. In conclusions, preliminary results show that the proposed methodology may help give novel insight toward aneurismal flow characteristic and help in future risk assessment given more developments.http://dx.doi.org/10.1155/2016/7406215 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kevin Sunderland Christopher Haferman Gouthami Chintalapani Jingfeng Jiang |
spellingShingle |
Kevin Sunderland Christopher Haferman Gouthami Chintalapani Jingfeng Jiang Vortex Analysis of Intra-Aneurismal Flow in Cerebral Aneurysms Computational and Mathematical Methods in Medicine |
author_facet |
Kevin Sunderland Christopher Haferman Gouthami Chintalapani Jingfeng Jiang |
author_sort |
Kevin Sunderland |
title |
Vortex Analysis of Intra-Aneurismal Flow in Cerebral Aneurysms |
title_short |
Vortex Analysis of Intra-Aneurismal Flow in Cerebral Aneurysms |
title_full |
Vortex Analysis of Intra-Aneurismal Flow in Cerebral Aneurysms |
title_fullStr |
Vortex Analysis of Intra-Aneurismal Flow in Cerebral Aneurysms |
title_full_unstemmed |
Vortex Analysis of Intra-Aneurismal Flow in Cerebral Aneurysms |
title_sort |
vortex analysis of intra-aneurismal flow in cerebral aneurysms |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2016-01-01 |
description |
This study aims to develop an alternative vortex analysis method by measuring structure ofIntracranial aneurysm (IA) flow vortexes across the cardiac cycle, to quantify temporal stability of aneurismal flow. Hemodynamics were modeled in “patient-specific” geometries, using computational fluid dynamics (CFD) simulations. Modified versions of known λ2 and Q-criterion methods identified vortex regions; then regions were segmented out using the classical marching cube algorithm. Temporal stability was measured by the degree of vortex overlap (DVO) at each step of a cardiac cycle against a cycle-averaged vortex and by the change in number of cores over the cycle. No statistical differences exist in DVO or number of vortex cores between 5 terminal IAs and 5 sidewall IAs. No strong correlation exists between vortex core characteristics and geometric or hemodynamic characteristics of IAs. Statistical independence suggests this proposed method may provide novel IA information. However, threshold values used to determine the vortex core regions and resolution of velocity data influenced analysis outcomes and have to be addressed in future studies. In conclusions, preliminary results show that the proposed methodology may help give novel insight toward aneurismal flow characteristic and help in future risk assessment given more developments. |
url |
http://dx.doi.org/10.1155/2016/7406215 |
work_keys_str_mv |
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