Study of the Hemodynamics Effects of an Isolated Systolic Hypertension (ISH) Condition on Cerebral Aneurysms Models, Using FSI Simulations

Hemodynamics is recognized as a relevant factor in the development and rupture of cerebral aneurysms, so further studies related to different physiological conditions in human represent an advance in understanding the pathology and rupture risk. In this paper, Fluid-structure interaction simulations (FSI) were carried out in six models of cerebral aneurysms, in order to study the hemodynamics effects of an isolated systolic hypertension (ISH) condition and compare it to a normal or normotensive pressure condition and a higher hypertension condition. Interestingly, the ISH condition showed, in general, the greatest hemodynamics changes, evidenced in the Time-Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), and Relative Residence Time (RRT) parameters, with respect to a normal condition. These results could imply that a not high-pressure condition (ISH), characterized with a different shape and an abrupt change in its diastolic and systolic range may present more adverse hemodynamic changes compared to a higher-pressure condition (such as a hypertensive condition) and therefore have a greater incidence on the arterial wall remodeling and rupture risk.

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COMPUTATIONAL STUDY ON THE RUPTURE RISK IN REAL CEREBRAL ANEURYSMS WITH GEOMETRICAL AND FLUID-MECHANICAL PARAMETERS USING FSI SIMULATIONS AND MACHINE LEARNING ALGORITHMS

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519419500143 ◽

2019 ◽

Vol 19 (03) ◽

pp. 1950014

Author(s):

ALFREDO ARANDA ◽

ALVARO VALENCIA

Keyword(s):

Machine Learning ◽

Computational Study ◽

Learning Algorithms ◽

Area Under The Curve ◽

Cerebral Aneurysms ◽

Machine Learning Algorithms ◽

Maximum Height ◽

Rupture Risk ◽

Relative Residence Time ◽

Von Mises

Fluid-mechanical and morphological parameters are recognized as major factors in the rupture risk of human aneurysms. On the other hand, it is well known that a lot of machine learning tools are available to study a variety of problems in many fields. In this work, fluid–structure interaction (FSI) simulations were carried out to examine a database of 60 real saccular cerebral aneurysms (30 ruptured and 30 unruptured) using reconstructions by angiography images. With the results of the simulations and geometric analyses, we studied the analysis of variance (ANOVA) statistic test in many variables and we obtained that aspect ratio (AR), bottleneck factor (BNF), maximum height of the aneurysms (MH), relative residence time (RRT), Womersley number (WN) and Von-Mises strain (VMS) are statically significant and good predictors for the models. In consequence, these ones were used in five machine learning algorithms to determine the rupture risk predictions of the aneurysms, where the adaptative boosting (AdaBoost) was calculated with the highest area under the curve (AUC) in the receiver operating characteristic (ROC) curve (AUC 0.944).

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Identifying the Distribution of Heterogeneous Anisotropic Elastic Properties in Cerebral Aneurysms

ASME 2009 Summer Bioengineering Conference, Parts A and B ◽

10.1115/sbc2009-206659 ◽

2009 ◽

Author(s):

Xuefeng Zhao ◽

Madhavan L. Raghavan ◽

Jia Lu

Keyword(s):

Arterial Wall ◽

Cerebral Aneurysms ◽

Wall Stress ◽

Constitutive Behavior ◽

Stress And Strain ◽

Rupture Risk ◽

Material Parameters ◽

Heterogeneous Nature ◽

Anisotropic Elastic

Cerebral aneurysms are focal dilatations of the intracranial arterial wall, whose rupture risk is likely related to pressure induced wall stress. Fundamental to stress and strain prediction in aneurysms is the constitutive behavior of wall tissue. However, delineating the constitutive equation of aneurismal tissue, in particular, experimental determination of the material parameters, presents some significant challenges due to the nonlinear, anisotropic and heterogeneous nature of the aneurysmal tissue.

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