scholarly journals A Computational Study of the Magnitude and Direction of Migration Forces in Patient-specific Abdominal Aortic Aneurysm Stent-Grafts

2010 ◽  
Vol 52 (3) ◽  
pp. 800-801
Author(s):  
D.S. Molony ◽  
E.G. Kavanagh ◽  
P. Madhavan ◽  
M.T. Walsh ◽  
T.M. McGloughlin
Author(s):  
Florian Stefanov ◽  
Patrick Delassus ◽  
Tim McGloughlin ◽  
Liam Morris

Abdominal aortic aneurysm (AAA) represents an asymptomatic cardiovascular type of disease, that is diagnosed in elder people over 60 years old. It is characterised by a ballooning of the abdominal aorta, which grows, at different rates in different patients. If left untreated, it will rupture causing severe internal bleeding, which can lead to shock or death [1]. Medical devices such as bifurcated stent grafts (SG) are used for the treatment of this disease. To help improve SG performance, biomedical engineers design benchtop models for testing.


2017 ◽  
Vol 39 ◽  
pp. 292.e5-292.e8 ◽  
Author(s):  
Mafalda Massara ◽  
Roberto Prunella ◽  
Pasquale Gerardi ◽  
Giovanni De Caridi ◽  
Raffaele Serra ◽  
...  

2016 ◽  
Vol 138 (10) ◽  
Author(s):  
Santanu Chandra ◽  
Vimalatharmaiyah Gnanaruban ◽  
Fabian Riveros ◽  
Jose F. Rodriguez ◽  
Ender A. Finol

In this work, we present a novel method for the derivation of the unloaded geometry of an abdominal aortic aneurysm (AAA) from a pressurized geometry in turn obtained by 3D reconstruction of computed tomography (CT) images. The approach was experimentally validated with an aneurysm phantom loaded with gauge pressures of 80, 120, and 140 mm Hg. The unloaded phantom geometries estimated from these pressurized states were compared to the actual unloaded phantom geometry, resulting in mean nodal surface distances of up to 3.9% of the maximum aneurysm diameter. An in-silico verification was also performed using a patient-specific AAA mesh, resulting in maximum nodal surface distances of 8 μm after running the algorithm for eight iterations. The methodology was then applied to 12 patient-specific AAA for which their corresponding unloaded geometries were generated in 5–8 iterations. The wall mechanics resulting from finite element analysis of the pressurized (CT image-based) and unloaded geometries were compared to quantify the relative importance of using an unloaded geometry for AAA biomechanics. The pressurized AAA models underestimate peak wall stress (quantified by the first principal stress component) on average by 15% compared to the unloaded AAA models. The validation and application of the method, readily compatible with any finite element solver, underscores the importance of generating the unloaded AAA volume mesh prior to using wall stress as a biomechanical marker for rupture risk assessment.


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