Effects of Wall Calcifications in Patient-Specific Wall Stress Analyses of Abdominal Aortic Aneurysms

2006 ◽  
Vol 129 (1) ◽  
pp. 105-109 ◽  
Author(s):  
Lambert Speelman ◽  
Ajay Bohra ◽  
E. Marielle H. Bosboom ◽  
Geert Willem H. Schurink ◽  
Frans N. van de Vosse ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Finite Element ◽  
Material Properties ◽  
Peak Stress ◽  
Wall Stress ◽  
Ct Scans ◽  
Patient Specific ◽  
Stress Distributions ◽  
Rupture Risk ◽  
Abdominal Aortic

It is generally acknowledged that rupture of an abdominal aortic aneurysm (AAA) occurs when the stress acting on the wall over the cardiac cycle exceeds the strength of the wall. Peak wall stress computations appear to give a more accurate rupture risk assessment than AAA diameter, which is currently used for a diagnose. Despite the numerous studies utilizing patient-specific wall stress modeling of AAAs, none investigated the effect of wall calcifications on wall stress. The objective of this study was to evaluate the influence of calcifications on patient-specific finite element stress computations. In addition, we assessed whether the effect of calcifications could be predicted directly from the CT-scans by relating the effect to the amount of calcification present in the AAA wall. For 6 AAAs, the location and extent of calcification was identified from CT-scans. A finite element model was created for each AAA and the areas of calcification were defined node-wise in the mesh of the model. Comparisons are made between maximum principal stress distributions, computed without calcifications and with calcifications with varying material properties. Peak stresses are determined from the stress results and related to a calcification index (CI), a quantification of the amount of calcification in the AAA wall. At calcification sites, local stresses increased, leading to a peak stress increase of 22% in the most severe case. Our results displayed a weak correlation between the CI and the increase in peak stress. Additionally, the results showed a marked influence of the calcification elastic modulus on computed stresses. Inclusion of calcifications in finite element analysis of AAAs resulted in a marked alteration of the stress distributions and should therefore be included in rupture risk assessment. The results also suggest that the location and shape of the calcified regions—not only the relative amount—are considerations that influence the effect on AAA wall stress. The dependency of the effect of the wall stress on the calcification elastic modulus points out the importance of determination of the material properties of calcified AAA wall.

scholarly journals Predictors of Abdominal Aortic Aneurysm Risks

2020 ◽  
Vol 7 (3) ◽  
pp. 79
Author(s):  
Stephen J. Haller ◽  
Amir F. Azarbal ◽  
Sandra Rugonyi
Keyword(s):  
Risk Assessment ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Wall Stress ◽  
Patient Specific ◽  
Rupture Risk ◽  
Element Analysis ◽  
Computational Biomechanics ◽  
Wall Strength ◽  
Abdominal Aortic

Computational biomechanics via finite element analysis (FEA) has long promised a means of assessing patient-specific abdominal aortic aneurysm (AAA) rupture risk with greater efficacy than current clinically used size-based criteria. The pursuit stems from the notion that AAA rupture occurs when wall stress exceeds wall strength. Quantification of peak (maximum) wall stress (PWS) has been at the cornerstone of this research, with numerous studies having demonstrated that PWS better differentiates ruptured AAAs from non-ruptured AAAs. In contrast to wall stress models, which have become progressively more sophisticated, there has been relatively little progress in estimating patient-specific wall strength. This is because wall strength cannot be inferred non-invasively, and measurements from excised patient tissues show a large spectrum of wall strength values. In this review, we highlight studies that investigated the relationship between biomechanics and AAA rupture risk. We conclude that combining wall stress and wall strength approximations should provide better estimations of AAA rupture risk. However, before personalized biomechanical AAA risk assessment can become a reality, better methods for estimating patient-specific wall properties or surrogate markers of aortic wall degradation are needed. Artificial intelligence methods can be key in stratifying patients, leading to personalized AAA risk assessment.

scholarly journals A Methodology for the Derivation of Unloaded Abdominal Aortic Aneurysm Geometry With Experimental Validation

2016 ◽  
Vol 138 (10) ◽  
Author(s):  
Santanu Chandra ◽  
Vimalatharmaiyah Gnanaruban ◽  
Fabian Riveros ◽  
Jose F. Rodriguez ◽  
Ender A. Finol
Keyword(s):  
Finite Element ◽  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Stress Component ◽  
Wall Stress ◽  
Patient Specific ◽  
Element Analysis ◽  
Nodal Surface ◽  
Abdominal Aortic ◽  
Peak Wall Stress

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.

scholarly journals On the Relative Impact of Intraluminal Thrombus Heterogeneity on Abdominal Aortic Aneurysm Mechanics

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Joseph R. Leach ◽  
Evan Kao ◽  
Chengcheng Zhu ◽  
David Saloner ◽  
Michael D. Hope
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Aneurysm Rupture ◽  
Aortic Aneurysms ◽  
Patient Specific ◽  
Rupture Risk ◽  
Intraluminal Thrombus ◽  
Element Analysis ◽  
Aneurysm Wall ◽  
Abdominal Aortic

Intraluminal thrombus (ILT) is present in the majority of abdominal aortic aneurysms (AAA) of a size warranting consideration for surgical or endovascular intervention. The rupture risk of AAAs is thought to be related to the balance of vessel wall strength and the mechanical stress caused by systemic blood pressure. Previous finite element analyses of AAAs have shown that ILT can reduce and homogenize aneurysm wall stress. These works have largely considered ILT to be homogeneous in mechanical character or have idealized a stiffness distribution through the thrombus thickness. In this work, we use magnetic resonance imaging (MRI) to delineate the heterogeneous composition of ILT in 7 AAAs and perform patient–specific finite element analysis under multiple conditions of ILT layer stiffness disparity. We find that explicit incorporation of ILT heterogeneity in the finite element analysis is unlikely to substantially alter major stress analysis predictions regarding aneurysm rupture risk in comparison to models assuming a homogenous thrombus, provided that the maximal ILT stiffness is the same between models. Our results also show that under a homogeneous ILT assumption, the choice of ILT stiffness from values common in the literature can result in significantly larger variations in stress predictions compared to the effects of thrombus heterogeneity.

Local Mechanical Properties of Abdominal Aortic Aneurysms

2008 ◽  
Author(s):  
Evelyne van Dam ◽  
Marcel Rutten ◽  
Frans van de Vosse
Keyword(s):  
Mechanical Properties ◽  
Stress Analysis ◽  
Vessel Wall ◽  
Computational Models ◽  
Wall Stress ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Patient Specific ◽  
Rupture Risk ◽  
Abdominal Aortic

Rupture risk of abdominal aortic aneurysms (AAA) based on wall stress analysis may be superior to the currently used diameter-based rupture risk prediction [4; 5; 6; 7]. In patient specific computational models for wall stress analysis, the geometry of the aneurysm is obtained from CT or MR images. The wall thickness and mechanical properties are mostly assumed to be homogeneous. The pathological AAA vessel wall may contain collageneous areas, but also calcifications, cholesterol crystals and large amounts of fat cells. No research has yet focused yet on the differences in mechanical properties of the components present within the degrading AAA vessel wall.

A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model

2021 ◽  
Author(s):  
Balaji Rengarajan ◽  
Sourav Patnaik ◽  
Ender A. Finol
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Modeling ◽  
Wall Stress ◽  
Aortic Aneurysms ◽  
Multivariate Adaptive Regression Splines ◽  
Patient Specific ◽  
Clinical Images ◽  
Element Modeling ◽  
Abdominal Aortic

Abstract In the present work, we investigated the use of geometric indices to predict patient-specific abdominal aortic aneurysm (AAA) wall stress by means of a novel neural network (NN) modeling approach. We conducted a retrospective review of existing clinical images of two patient groups: 98 asymptomatic and 50 symptomatic AAA. The images were subject to a protocol consisting of image segmentation, processing, volume meshing, finite element modeling, and geometry quantification, from which 53 geometric indices and the spatially averaged wall stress (SAWS) were calculated. We developed feed-forward NN models composed of an input layer, two dense layers, and an output layer using Keras, a deep learning library in Python. The NN models were trained, tested, and validated independently for both AAA groups using all geometric indices, as well as a reduced set of indices resulting from a variable reduction procedure. We compared the performance of the NN models with two standard machine learning algorithms (MARS: multivariate adaptive regression splines and GAM: generalized additive model) and a linear regression model (GLM: generalized linear model). The NN-based approach exhibited the highest overall mean goodness-of-fit and lowest overall relative error compared to MARS, GAM, and GLM, when using the reduced sets of indices to predict SAWS for both AAA groups. The use of NN modeling represents a promising alternative methodology for the estimation of AAA wall stress using geometric indices as surrogates, in lieu of finite element modeling.

COMPARISON OF TWO METHODS FOR ESTIMATING THE UNLOADED STATE FOR ABDOMINAL AORTIC ANEURYSM STRESS CALCULATIONS

2019 ◽  
Vol 19 (03) ◽  
pp. 1950015 ◽  
Author(s):  
JOSEPH R. LEACH ◽  
CHENGCHENG ZHU ◽  
DAVID SALONER ◽  
MICHAEL D. HOPE
Keyword(s):  
Finite Element ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Patient Specific ◽  
Rupture Risk ◽  
Geometric Accuracy ◽  
Abdominal Aortic ◽  
Unloaded State ◽  
Geometric Scaling

Biomechanical analyses can be used to better understand the rupture risk of abdominal aortic aneurysms (AAAs) on a patient-specific basis using vascular geometries obtained from medical imaging. Methodologies of varying complexity are used to estimate the unloaded state of the imaged vessel to provide a reference configuration for finite element simulations. In this work, we compare the implementation and results of two of these methods, one based on geometric scaling and the other using an iterative determination of unloaded vessel geometry. We find that the two methods result in significantly different stress predictions, and that the iterative method offers superior geometric accuracy. Our findings lend context to the variation in finite element results presented in the AAA stress analysis literature.

scholarly journals Biomechanical rupture risk assessment of abdominal aortic aneurysms based on a novel probabilistic rupture risk index

2015 ◽  
Vol 12 (113) ◽  
pp. 20150852 ◽  
Author(s):  
Stanislav Polzer ◽  
T. Christian Gasser
Keyword(s):  
Risk Assessment ◽  
Wall Thickness ◽  
Risk Index ◽  
Wall Stress ◽  
Aortic Aneurysms ◽  
Rupture Risk ◽  
Discriminative Power ◽  
Wall Strength ◽  
Abdominal Aortic ◽  
Wide Range

A rupture risk assessment is critical to the clinical treatment of abdominal aortic aneurysm (AAA) patients. The biomechanical AAA rupture risk assessment quantitatively integrates many known AAA rupture risk factors but the variability of risk predictions due to model input uncertainties remains a challenging limitation. This study derives a probabilistic rupture risk index (PRRI). Specifically, the uncertainties in AAA wall thickness and wall strength were considered, and wall stress was predicted with a state-of-the-art deterministic biomechanical model. The discriminative power of PRRI was tested in a diameter-matched cohort of ruptured ( n = 7) and intact ( n = 7) AAAs and compared to alternative risk assessment methods. Computed PRRI at 1.5 mean arterial pressure was significantly ( p = 0.041) higher in ruptured AAAs (20.21(s.d. 14.15%)) than in intact AAAs (3.71(s.d. 5.77)%). PRRI showed a high sensitivity and specificity (discriminative power of 0.837) to discriminate between ruptured and intact AAA cases. The underlying statistical representation of stochastic data of wall thickness, wall strength and peak wall stress had only negligible effects on PRRI computations. Uncertainties in AAA wall stress predictions, the wide range of reported wall strength and the stochastic nature of failure motivate a probabilistic rupture risk assessment. Advanced AAA biomechanical modelling paired with a probabilistic rupture index definition as known from engineering risk assessment seems to be superior to a purely deterministic approach.

Development and Validation of Patient-Specific Finite Element Models of the Hemipelvis Generated From a Sparse CT Data Set

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Vickie B. Shim ◽  
Rocco P. Pitto ◽  
Robert M. Streicher ◽  
Peter J. Hunter ◽  
Iain A. Anderson
Keyword(s):  
Finite Element ◽  
Material Properties ◽  
Patient Data ◽  
Ct Scans ◽  
Patient Specific ◽  
Data Sets ◽  
Fe Model ◽  
Data Set ◽  
Spatially Varying ◽  
Ct Slices

To produce a patient-specific finite element (FE) model of a bone such as the pelvis, a complete computer tomographic (CT) or magnetic resonance imaging (MRI) geometric data set is desirable. However, most patient data are limited to a specific region of interest such as the acetabulum. We have overcome this problem by providing a hybrid method that is capable of generating accurate FE models from sparse patient data sets. In this paper, we have validated our technique with mechanical experiments. Three cadaveric embalmed pelves were strain gauged and used in mechanical experiments. FE models were generated from the CT scans of the pelves. Material properties for cancellous bone were obtained from the CT scans and assigned to the FE mesh using a spatially varying field embedded inside the mesh while other materials used in the model were obtained from the literature. Although our FE meshes have large elements, the spatially varying field allowed them to have location dependent inhomogeneous material properties. For each pelvis, five different FE meshes with a varying number of patient CT slices (8–12) were generated to determine how many patient CT slices are needed for good accuracy. All five mesh types showed good agreement between the model and experimental strains. Meshes generated with incomplete data sets showed very similar stress distributions to those obtained from the FE mesh generated with complete data sets. Our modeling approach provides an important step in advancing the application of FE models from the research environment to the clinical setting.

Aortic Wall Mechanics: A Geometry-Driven Problem

2011 ◽  
Author(s):  
Samarth S. Raut ◽  
Peng Liu ◽  
Anirban Jana ◽  
Ender A. Finol
Keyword(s):  
Risk Assessment ◽  
Aortic Wall ◽  
Wall Stress ◽  
Aneurysm Rupture ◽  
Intraluminal Pressure ◽  
Wall Material ◽  
Patient Specific ◽  
Rupture Risk ◽  
Constitutive Material Model ◽  
Constitutive Material

Abdominal Aortic Aneurysm (AAA) is a vascular disease that occurs predominantly in people over 60 years of age. The rupture of an AAA is a catastrophic event associated with up to a 90% mortality rate. Hence, it is important for vascular surgeons to justify the risk of repair vis-à-vis the risk of aneurysm rupture. In clinical practice, rupture risk assessment is based on measuring the maximum aneurysm diameter where 5.5 cm is accepted as the critical size for recommending (surgical or endovascular) intervention. However, this criterion is based on an extensive history of evidence-based medicine rather than an individualized assessment of the aneurysm’s potential to rupture. Primary among the biomechanical factors associated with the rupture assessment of an AAA is mechanical wall stress, which is dependent on the accuracy of the geometry reconstruction, intraluminal pressure loading and the constitutive material model used for the aortic wall. We hypothesize that in unruptured, asymptomatic AAA, the wall mechanics is the outcome of primarily the patient specific aneurysm shape and to a lesser extent, the constitutive material property model used to characterize the vascular wall. Evaluating the relative contributions of wall material properties and AAA geometry to wall mechanics estimation will increase our understanding of the factors that influence peak wall stress as an indicator for rupture risk assessment. In the present work, we evaluate the aforementioned hypothesis using a size-matched approach.

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