Judy Shum

The Role of Geometric and Biomechanical Factors in Abdominal Aortic Aneurysm Rupture Risk Assessment

The current clinical management of abdominal aortic aneurysm (AAA) disease is based to a great extent on measuring the aneurysm maximum diameter to decide when timely intervention is required. Decades of clinical evidence show that aneurysm diameter is positively associated with the risk of rupture, but other parameters may also play a role in causing or predisposing the AAA to rupture. Geometric factors such as vessel tortuosity, intraluminal thrombus volume, and wall surface area are implicated in the differentiation of ruptured and unruptured AAAs. Biomechanical factors identified by means of computational modeling techniques, such as peak wall stress, have been positively correlated with rupture risk with a higher accuracy and sensitivity than maximum diameter alone. The objective of this review is to examine these factors, which are found to influence AAA disease progression, clinical management and rupture potential, as well as to highlight on-going research by our group in aneurysm modeling and rupture risk assessment.

A Framework for the Automatic Generation of Surface Topologies for Abdominal Aortic Aneurysm Models

Patient-specific abdominal aortic aneurysms (AAAs) are characterized by local curvature changes, which we assess using a feature-based approach on topologies representative of the AAA outer wall surface. The application of image segmentation methods yields 3D reconstructed surface polygons that contain low-quality elements, unrealistic sharp corners, and surface irregularities. To optimize the quality of the surface topology, an iterative algorithm was developed to perform interpolation of the AAA geometry, topology refinement, and smoothing. Triangular surface topologies are generated based on a Delaunay triangulation algorithm, which is adapted for AAA segmented masks. The boundary of the AAA wall is represented using a signed distance function prior to triangulation. The irregularities on the surface are minimized by an interpolation scheme and the initial coarse triangulation is refined by forcing nodes into equilibrium positions. A surface smoothing algorithm based on a low-pass filter is applied to remove sharp corners. The optimal number of iterations needed for polygon refinement and smoothing is determined by imposing a minimum average element quality index with no significant AAA sac volume change. This framework automatically generates high-quality triangular surface topologies that can be used to characterize local curvature changes of the AAA wall.

Surface Curvature as a Classifier of Abdominal Aortic Aneurysms: A Comparative Analysis

An abdominal aortic aneurysm (AAA) carries one of the highest mortality rates among vascular diseases when it ruptures. To predict the role of surface curvature in rupture risk assessment, a discriminatory analysis of aneurysm geometry characterization was conducted. Data was obtained from 205 patient-specific computed tomography image sets corresponding to three AAA population subgroups: patients under surveillance, those that underwent elective repair of the aneurysm, and those with an emergent repair. Each AAA was reconstructed and their surface curvatures estimated using the biquintic Hermite finite element method. Local surface curvatures were processed into ten global curvature indices. Statistical analysis of the data revealed that the L2-norm of the Gaussian and Mean surface curvatures can be utilized as classifiers of the three AAA population subgroups. The application of statistical machine learning on the curvature features yielded 85.5% accuracy in classifying electively and emergent repaired AAAs, compared to a 68.9% accuracy obtained by using maximum aneurysm diameter alone. Such combination of non-invasive geometric quantification and statistical machine learning methods can be used in a clinical setting to assess the risk of rupture of aneurysms during regular patient follow-ups.

The Association of Wall Mechanics and Morphology: A Case Study of Abdominal Aortic Aneurysm Growth

The purpose of this study is to evaluate the potential correlation between peak wall stress (PWS) and abdominal aortic aneurysm (AAA) morphology and how it relates to aneurysm rupture potential. Using in-house segmentation and meshing software, six 3-dimensional (3D) AAA models from a single patient followed for 28 months were generated for finite element analysis. For the AAA wall, both isotropic and anisotropic materials were used, while an isotropic material was used for the intraluminal thrombus (ILT). These models were also used to calculate 36 geometric indices characteristic of the aneurysm morphology. Using least squares regression, seven significant geometric features (p < 0.05) were found to characterize the AAA morphology during the surveillance period. By means of nonlinear regression, PWS estimated with the anisotropic material was found to be highly correlated with three of these features: maximum diameter (r = 0.992, p = 0.002), sac volume (r = 0.989, p = 0.003) and diameter to diameter ratio (r = 0.947, p = 0.033). The correlation of wall mechanics with geometry is nonlinear and reveals that PWS does not increase concomitantly with aneurysm diameter. This suggests that a quantitative characterization of AAA morphology may be advantageous in assessing rupture risk.

Biological, Geometric and Biomechanical Factors Influencing Abdominal Aortic Aneurysm Rupture Risk: A Comprehensive Review

The current clinical management of abdominal aortic aneurysm (AAA) disease is based to a great extent on measuring the aneurysm maximum diameter to decide when timely intervention is required. Decades of clinical evidence show that aneurysm diameter is positively associated with the probability of rupture, but that other parameters may also play a role in causing or predisposing the AAA to rupture. Biological factors associated with smooth muscle apoptosis are implicated in AAA expansion while geometric and biomechanical factors identified by means of computational modeling techniques have been positively correlated with rupture risk with a higher accuracy and sensitivity than maximum diameter alone. The objective of this review is to examine the factors found to influence AAA disease progression, clinical management and rupture, as well as a patent review that highlights developments in this arena in the past few years.

AAA RUPTURE RISK ASSESSMENT IN THE CLINIC: WALL STRESS OR GEOMETRIC CHARACTERIZATION?

The current clinical management of abdominal aortic aneurysm (AAA) disease is based to a great extent on measuring the aneurysm maximum diameter to decide when timely intervention is required. Decades of clinical evidence show that aneurysm diameter is positively associated with the risk of rupture, but other parameters may also play a role in causing or predisposing the AAA to rupture. Geometric factors such as vessel tortuosity, intraluminal thrombus volume, and wall surface area are implicated in the differentiation of ruptured and unruptured AAAs. Biomechanical factors identified by means of computational modeling techniques, such as peak wall stress, have been positively correlated with rupture risk with a higher accuracy and sensitivity than maximum diameter alone. In the present work, we performed a controlled study targeted at evaluating the effect of uncertainty of the constitutive material model used for the vascular wall in the ensuing peak wall stress. Based on the outcome of this study, a second analysis was conducted based on the geometric characterization of surface curvature in two groups of aneurysm geometries, to discern which curvature metric can adequately discriminate ruptured from electively repaired AAA. The outcome of this work provides preliminary evidence on the importance of quantitative geometry characterization for AAA rupture risk assessment in the clinic.Copyright

Toward Improved Prediction of AAA Rupture Risk: Implementation of Feature-Based Geometry Quantification Measures Compared to Maximum Diameter Alone

Data mining techniques are capable of extracting important relationships and correlations among large amounts of data while machine learning methodologies can utilize these correlations to generate models capable of classification and prediction. The combination of machine learning and data mining is an important contribution of the present work for two reasons: (1) given a large database of features that describe the geometry of native abdominal aortic aneurysms (AAAs), patterns and relationships in the data are derived that may not be apparent to the human eye, and (2) statistical models are generated that can classify new data and determine which features discriminate among different aneurysm populations. The objectives of this study were to use anatomically realistic AAA models to evaluate a proposed set of global geometric indices describing the size, shape and individual wall thickness of the aneurysm sac, and use a learning algorithm to develop a model that is capable of discriminating the rupture status of these aneurysms.Copyright

A Comprehensive Tool for Patient-Specific AAA Geometry and Biomechanics Assessment

Annual mortality from ruptured abdominal aortic aneurysm (AAA) in the United States alone is approximately 150,000, which is currently ranked as the 13th leading cause of death and the 10th leading cause of death in men over 55 years of age [1]. The vascular surgeon needs to weigh the risk of AAA rupture against the risk of surgical intervention to decide the best course of treatment. Several steps are involved when using computational techniques to evaluate risk of rupture [2], namely medical image segmentation, 3D reconstruction, finite element mesh generation, derivation of boundary conditions, specification of tissue material properties, etc. Currently, computational analysis of AAA biomechanics includes the use of multiple third-party commercial software tools to accomplish each of these steps, which makes its clinical implementation impractical, time-consuming and requiring to interface multiple software tools as this demands an engineering skill set. Additionally, the versatility of general purpose off-the-shelf software comes at the cost of simplifying assumptions regarding geometric modeling, limited user control and boundary conditions. This makes subsequent computational results vulnerable to inaccuracies. In this work, we describe the software tool AAAVASC, built on a MATLAB platform, with an integrated approach for image-based modeling and a novel pipeline that facilitates both geometry quantification and computational analysis of AAA biomechanics.Copyright

Abdominal Aortic Aneurysm Growth: The Association of Aortic Wall Mechanics and Geometry

In an effort to prevent rupture, patients with known AAA undergo periodic abdominal ultrasound or CT scan surveillance. When the aneurysm grows to a diameter of 5.0–5.5 cm or is shown to expand at a rate greater than 1 cm/yr, elective operative repair is undertaken. While this strategy certainly prevents a number of potentially catastrophic ruptures, AAA rupture can occur at sizes less than 5 cm. From a biomechanical standpoint, aneurysm rupture occurs when wall stress exceeds wall strength. By using non-invasive techniques, such as finite element analysis (FEA), wall stress can be estimated for patient specific AAA models, which can perhaps more carefully predict the rupture potential of a given aneurysm, regardless of size. FEA is a computational method that can be used to evaluate complicated structures such as aneurysms. To this end, it was reported earlier that AAA peak wall stress provides a better assessment of rupture risk than the commonly used maximum diameter criterion [1]. What has yet to be examined, however, is the relationship between wall stress and AAA geometry during aneurysm growth. Such finding has the potential for providing individualized predictions of AAA rupture potential during patient surveillance. The purpose of this study is to estimate peak wall stress for an AAA under surveillance and evaluate its potential correlation with geometric features characteristic of the aneurysm’s morphology.Copyright

Machine Learning Techniques for the Assessment of AAA Rupture Risk

ABSTRACT indices that describe the size, shape, curvature, and regional variations Recent clinical studies have shown that the maximum transverse diameter of an abdominal aortic aneurysm (AAA) alone, or in combination with its expansion rate are not entirely reliable indicators of rupture potential. We hypothesize that AAA shape, size, and wall thickness may be related to rupture risk and can be deciding factors in the clinical management of the disease. A non-invasive, image-based evaluation of AAA size and geometry was implemented using an in-house code (AAAVASC v1.0, Carnegie Mellon University) on a retrospective study of 88 subjects. The contrast enhanced, computed tomography (CT) scans of 44 .patients who suffered AAA rupture within 1 month of the scan were compared to those of 44 patients who received elective repair. The images were segmented and three-dimensional models were generated. Twenty-eight geometry-based indices were calculated to characterize the size and shape of each AAA and estimate regional variations in wall thickness. A multivariate analysis of variance was performed for all indices comparing the ruptured and non-ruptured data sets to determine which indices were statistically significant. A classification model was created using a J48 decision tree algorithm and its performance was assessed using 10-fold cross validation. The model correctly classified eighty-six data sets and had an average prediction accuracy of 74% ( Figure 1. Flowchart = 0.69). Such a decision model can be used in a clinical setting to assess the risk of AAA rupture with minimal user intervention.