Abnormal blood flow conditions and structural fatigue within stented vessels may lead to undesired failure causing
death to the patient. Image-based computational modeling provides a physical and realistic insight into the patientspecific
biomechanics and enables accurate predictive simulations of development, growth and failure of cardiovascular
diseases as well as associated risks. Controlling the efficiency of an endovascular treatment is necessary for the
evaluation of potential complications and predictions on the assessment of the pathological state.
In this paper we investigate the effects of stent-graft implantation on the biomechanics in a patient-specific thoracic
aortic model. The patient geometry and the implanted stent-graft are obtained from morphological data based on a CT
scan performed during a controlling routine. Computational fluid dynamics (CFD) and computational structure
mechanics (CSM) simulations are conducted based on the finite volume method (FVM) and on the finite element
method (FEM) to compute the hemodynamics and the elastomechanics within the aortic model, respectively.
Physiological data based on transient pressure and velocity profiles are used to set the necessary boundary conditions.
Further, the effects of various boundary conditions and definition of contact interactions on the numerical stability of the
blood flow and the vessel wall simulation results are also investigated. The quantification of the hemodynamics and the
elastomechanics post endovascular intervention provides a realistic controlling of the state of the stented vessel and of
the efficiency of the therapy. Consequently, computational modeling would help in evaluating individual therapies and
optimal treatment strategies in the field of minimally invasive endovascular surgery.
KEYWORDS: Computer simulations, Data modeling, Finite element methods, Data acquisition, Surgery, In vivo imaging, Computed tomography, 3D modeling, 4D CT imaging, Therapeutics
Cardiovascular disease results from pathological biomechanical conditions and fatigue of the vessel wall. Image-based
computational modeling provides a physical and realistic insight into the patient-specific biomechanics and enables
accurate predictive simulations of development, growth and failure of cardiovascular disease. An experimental
validation is necessary for the evaluation and the clinical implementation of such computational models.
In the present study, we have implemented dynamic Computed-Tomography (4D-CT) imaging and catheter-based in
vivo measured pressures to numerically simulate and experimentally evaluate the biomechanics of the porcine aorta. The
computations are based on the Finite Element Method (FEM) and simulate the arterial wall response to the transient
pressure-based boundary condition. They are evaluated by comparing the numerically predicted wall deformation and
that calculated from the acquired 4D-CT data. The dynamic motion of the vessel is quantified by means of the hydraulic
diameter, analyzing sequences at 5% increments over the cardiac cycle.
Our results show that accurate biomechanical modeling is possible using FEM-based simulations. The RMS error of the
computed hydraulic diameter at five cross-sections of the aorta was 0.188, 0.252, 0.280, 0.237 and 0.204 mm, which is
equivalent to 1.7%, 2.3%, 2.7%, 2.3% and 2.0%, respectively, when expressed as a function of the time-averaged
hydraulic diameter measured from the CT images. The present investigation is a first attempt to simulate and validate
vessel deformation based on realistic morphological data and boundary conditions. An experimentally validated system
would help in evaluating individual therapies and optimal treatment strategies in the field of minimally invasive
endovascular surgery.
Image-based modeling of cardiovascular biomechanics may be very helpful for patients with aortic aneurysms to predict
the risk of rupture and evaluate the necessity of a surgical intervention. In order to generate a reliable support it is
necessary to develop exact patient-specific models that simulate biomechanical parameters and provide individual
structural analysis of the state of fatigue and characterize this to the potential of rupture of the aortic wall.
The patient-specific geometry used here originates from a CT scan of an Abdominal Aortic Aneurysm (AAA). The
computations are based on the Finite Element Method (FEM) and simulate the wall stress distribution and the vessel
deformation. The wall transient boundary conditions are based on real time-dependent pressure simulations obtained
from a previous computational fluid dynamics study. The physiological wall material properties consider a nonlinear
hyperelastic constitutive model, based on realistic ex-vivo analysis of the aneurismal arterial tissue.
The results showed complex deformation and stress distribution on the AAA wall. The maximum stresses occurred at
the systole and are found around the aneurismal bulge in regions close to inflection points.
Biomechanical modeling based on medical images and coupled with patient-specific hemodynamics allows analysing
and quantifying the effects of dilatation of the arterial wall due to the pulsatile aortic pressure. It provides a physical and
realistic insight into the wall mechanics and enables predictive simulations of AAA growth and assessment of rupture.
Further development integrating endovascular models would help evaluating non-invasively individual treatment
strategies for optimal placement and improved device design.
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