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Influence of different computational approaches for stent deployment on cerebral aneurysm haemodynamics
Author(s) -
A. Bernardini,
Ignacio Larrabide,
Hernán G. Morales,
Giancarlo Pennati,
Lorenza Petrini,
Salvatore Cito,
Alejandro F. Frangi
Publication year - 2011
Publication title -
interface focus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 49
eISSN - 2042-8901
pISSN - 2042-8898
DOI - 10.1098/rsfs.2011.0004
Subject(s) - stent , hemodynamics , workflow , aneurysm , computational fluid dynamics , computer science , software deployment , finite element method , shear stress , radiology , medicine , cardiology , mechanics , physics , structural engineering , engineering , database , operating system
Cerebral aneurysms are abnormal focal dilatations of artery walls. The interest in virtual tools to help clinicians to value the effectiveness of different procedures for cerebral aneurysm treatment is constantly growing. This study is focused on the analysis of the influence of different stent deployment approaches on intra-aneurysmal haemodynamics using computational fluid dynamics (CFD). A self-expanding stent was deployed in an idealized aneurysmatic cerebral vessel in two initial positions. Different cases characterized by a progression of simplifications on stent modelling (geometry and material) and vessel material properties were set up, using finite element and fast virtual stenting methods. Then, CFD analysis was performed for untreated and stented vessels. Haemodynamic parameters were analysed qualitatively and quantitatively, comparing the cases and the two initial positions. All the cases predicted a reduction of average wall shear stress and average velocity of almost 50 per cent after stent deployment for both initial positions. Results highlighted that, although some differences in calculated parameters existed across the cases based on the modelling simplifications, all the approaches described the most important effects on intra-aneurysmal haemodynamics. Hence, simpler and faster modelling approaches could be included in clinical workflow and, despite the adopted simplifications, support clinicians in the treatment planning.

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