Predicting the Risk of Rupture of Abdominal Aortic Aneurysms by Utilizing Various Geometrical Parameters: Revisiting the Diameter Criterion

A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution

The human aorta undergoes complex morphologic changes that indicate the evolution of disease. Finite element analysis enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into finite element analysis (FEA) to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing pathologic growth drives morphologic heterogeneity. A shape-size geometric feature space plotting the variance of the shape index versus the inverse square root of aortic surface area (δ𝒮 vs. ) quantitatively demonstrates the simulated breakdown in aortic shape. An increase in δ𝒮 closely parallels the true geometric progression of aortic disease patients.

Narrative Review of Risk Assessment of Abdominal Aortic Aneurysm Rupture Based on Biomechanics-Related Morphology

CLINICAL IMPACT

Studies have shown that the biomechanical indicators based on multi-scale models are more effective in accurately assessing the rupture risk of AAA. To meet the need for clinical monitoring and rapid decision making, the typical morphological parameters associated with AAA rupture and their relationships with the mechanical environment have been summarized, which provide a reference for clinical preoperative risk assessment of AAA.

The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature

Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape is characterized using integrated Gaussian curvature or total curvature. The fluctuation in total curvature (δK) across aortic surfaces captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law defined behavior with rapidly increasing δK forming the hallmark of aortic disease. Divergent δK is seen for highly diseased aortas indicative of impending topologic catastrophe or aortic rupture. We also show that aortic size (surface area or enclosed aortic volume) scales as a generalized cylinder for all shapes. Classification accuracy for predicting aortic disease state (normal, diseased with successful surgery, and diseased with failed surgical outcomes) is 92.8±1.7%. The analysis of δK can be applied on any three-dimensional geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes.

Evaluation of associations between outflow morphology and rupture risk of abdominal aortic aneurysms

PURPOSE

This study aimed to evaluate the association between the outflow morphology and abdominal aortic aneurysm (AAA) rupture risk, to find risk factors for future prediction models.

MATERIALS AND METHODS

We retrospectively analyzed 46 patients with ruptured AAAs and 46 patients with stable AAAs using a 1:1 match for sex, age, and maximum aneurysm diameter. The chi-square test, paired t-test, and Wilcoxon signed-rank test were used to compare variables. Logistic regression was performed to evaluate variables potentially associated with AAA rupture. Receiver operating characteristic curve analysis and the area under the curve (AUC) were used to assess the regression models.

RESULTS

Ruptured AAAs had a shorter proximal aortic neck (median (interquartile range, IQR): 24.0 (9.4-34.2) mm vs. 33.3 (20.0-52.8) mm, p = 0.004), higher tortuosity (median(IQR): 1.35 (1.23-1.49) vs. 1.29 (1.23-1.39), p = 0.036), and smaller minimum luminal area of the right common iliac artery (CIA) (median (IQR): 86.7 (69.9-126.4) mm2 vs. 118.9 (86.3-164.1)mm2, p = 0.001) and left CIA (median(IQR): 92.2 (67.3,125.1) mm2 vs. 110.7 (80.12, 161.1) mm2, p = 0.010) than stable AAA did. Multiple regression analysis demonstrated significant associations of the minimum luminal area of the bilateral CIAs (odds ratio [OR] = 0.996, 95 % confidence interval [CI] 0.991-0.999, p = 0.037), neck length (OR = 0.969, 95 % CI 0.941-0.993, p = 0.017), and aneurysm tortuosity (OR = 1.031, 95 % CI 1.003-1.063, p = 0.038) with ruptured AAAs. The AUC of this regression model was 0.762 (95 % CI 0.664-0.860, p < 0.001).

CONCLUSIONS

The smaller minimum luminal area of the CIA is associated with an increased risk of rupture. This study highlights the potential of utilizing outflow parameters as novel and additional tools in risk assessment. It also provides a compelling rationale to further intensify research in this area.

A Computational Model of Ventricular Dimensions and Hemodynamics in Growing Infants

Previous computer models have successfully predicted cardiac growth and remodeling in adults with pathologies. However, applying these models to infants is complicated by the fact that they also undergo normal, somatic cardiac growth and remodeling. Therefore, we designed a computational model to predict ventricular dimensions and hemodynamics in healthy, growing infants by modifying an adult canine left ventricular growth model. The heart chambers were modeled as time-varying elastances coupled to a circuit model of the circulation. Circulation parameters were allometrically scaled and adjusted for maturation to simulate birth through 3 yrs of age. Ventricular growth was driven by perturbations in myocyte strain. The model successfully matched clinical measurements of pressures, ventricular and atrial volumes, and ventricular thicknesses within two standard deviations of multiple infant studies. To test the model, we input 10th and 90th percentile infant weights. Predicted volumes and thicknesses decreased and increased within normal ranges and pressures were unchanged. When we simulated coarctation of the aorta, systemic blood pressure, left ventricular thickness, and left ventricular volume all increased, following trends in clinical data. Our model enables a greater understanding of somatic and pathological growth in infants with congenital heart defects. Its flexibility and computational efficiency when compared to models employing more complex geometries allow for rapid analysis of pathological mechanisms affecting cardiac growth and hemodynamics.

Prediction of abdominal aortic aneurysm growth by artificial intelligence taking into account clinical, biologic, morphologic, and biomechanical variables

OBJECTIVES

To develop a prediction model that could risk stratify abdominal aortic aneurysms (AAAs) into high and low growth rate groups, using machine learning algorithms based on variables from different pathophysiological fields.

METHODS

A cohort of 40 patients with small AAAs (maximum diameter 32-53 mm) who had at least an initial and a follow-up CT scan (median follow-up 12 months, range 3-36 months) were included. 29 input variables from clinical, biological, morphometric, and biomechanical pathophysiological aspects extracted for predictive modeling. Collected data were used to build two supervised machine learning models. A gradient boosting (XGboost) and a support vector machines (SVM) algorithm were trained with 60% and tested with 40% of the data to predict which AAA would achieve a growth rate higher than the median of our study cohort. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used for the evaluation of the developed algorithms.

RESULTS

XGboost achieved the highest AUC in predicting high compared to low AAA growth rate with an AUC of 81.2% (95% CI from 61.1 to 100%). SVM achieved the second highest performance with an AUC of 68.8% (95% CI from 46.5 to 91%). Based on the best performing algorithm, variable importance was estimated. Diameter-diameter ratio (maximum diameter/neck diameter), Tortuosity from Renal arteries to aortic bifurcation, and maximum thickness of the intraluminal thrombus were found to be the most important factors for model predictions. Other factors were also found to play a significant but less important role.

CONCLUSIONS

A prediction model that can risk stratify AAAs into high and low growth rate groups could be developed by analyzing several factors implicated in the multifactorial pathophysiology of this disease, with the use of machine learning algorithms. Future studies including larger patient cohorts and implementing additional risk markers may aid in the establishment of such methodology during AAA rupture risk estimation.

EVALUATION OF AORTIC TORTUOSITY AS A NEGATIVE PREDICTOR OF ABDOMINAL AORTIC ANEURYSM RUPTURE

OBJECTIVE

Maximal aortic diameter has been used as a key indication to repair an abdominal aortic aneurysm (AAA). Aortic tortuosity has been proposed as another consideration. This study compared the degree of aortic tortuosity in ruptured aneurysms with those who underwent elective repair using CT imaging.

METHODS

A retrospective review of a prospectively maintained database of patients that underwent AAA repair from Dec 2014 to Dec 2019 was undertaken. Patients with ruptured aneurysm (rAAA) were matched to a group of non-ruptured AAA (nrAAA) patients with the same maximal aneurysm diameter and age. The degree of aortic tortuosity, defined as the maximum lateral deviation (mm) from aortic centerline, was measured on preoperative CT scans on coronal views.

RESULTS

Over a 5-year period, 572 AAA cases were identified. The aortic tortuosity of 25 rAAA cases were compared with a matched control group of 31 nrAAA that were selected based on same mean maximum diameter of 8.4cm and similar age. In the rAAA group, the mean age was 74.8 years (84% males). For the nrAAA group, the mean age was 76.3 years (88%) males. The mean aortic tortuosity for the rAAA cases and nrAAA groups were 9.3±7.9 mm and 18.0±11.2 mm, respectively (p<0.01).

CONCLUSIONS

Greater aortic tortuosity was seen in nrAAA cases compared with rAAA cases at the same matched aneurysm size. This suggests that aortic tortuosity may confer a reduced rupture risk. Further studies with larger cohorts are needed to verify this observation.

Combined Curvature and Wall Shear Stress Analysis of Abdominal Aortic Aneurysm: An Analysis of Rupture Risk Factors

PURPOSE

To discuss the risk factors for abdominal aortic aneurysm rupture based on geometric and hemodynamic parameters.

METHODS

We retrospectively reviewed the clinical data of those who were diagnosed with an abdominal aortic aneurysm by computed tomography angiography at our hospital between October 2019 and December 2020. Thirty-five patients were included in the ruptured group (13 patients) and the unruptured group (22 patients). We analyzed the differences and correlations of anatomical factors and hemodynamic parameters between the two groups using computational fluid dynamics based on computed tomography angiography.

RESULTS

There were significant differences in the maximum diameter [(79.847 ± 10.067) mm vs. (52.320 ± 14.682) mm, P < 0.001], curvature [(0.139 ± 0.050) vs. 0.080 (0.123 - 0.068), P = 0.021], and wall shear stress at the site of maximal blood flow impact [0.549(0.839 - 0.492) Pa vs. (1.378 ± 0.255) Pa, P < 0.001] between the ruptured and unruptured groups, respectively. And in the ruptured group, wall shear stress at the rupture site was significantly different from that at the site of maximal blood flow impact [0.025 (0.049 - 0.018) Pa vs. 0.549 (0.839 - 0.492) Pa, P = 0.001]. Then, the maximum diameter and curvature were associated with rupture (maximum diameter: OR: 1.095, P = 0.003; curvature: OR: 1.142E + 10, P = 0.012). Most importantly, curvature is negatively correlated with wall shear stress (r = - 0.366, P = 0.033).

CONCLUSIONS

Both curvature and wall shear stress can evaluate the rupture risk of aneurysm. Also, curvature can be used as the geometric substitution of wall shear stress.

Role of volume in small abdominal aortic aneurysm surveillance

OBJECTIVE

Current management of small abdominal aortic aneurysms (AAAs) primarily involves serial imaging surveillance of maximum transverse diameter (MTD) to estimate rupture risk. Other measurements, such as volume and tortuosity, are less well-studied and may help characterize and predict AAA progression. This study evaluated predictors of AAA volume growth and discusses the role of volume in clinical practice.

METHODS

Subjects from the Non-invasive Treatment of Abdominal Aortic Aneurysm Clinical Trial (baseline AAA MTD, 3.5-5.0 cm) with ≥2 computed tomography scans were included in this study (n = 250). Computed tomography scans were conducted approximately every 6 months over 2 years. MTD, volume, and tortuosity were used to model growth. Univariable and multivariable backwards elimination least squares regressions assessed associations with volume growth.

RESULTS

Baseline MTD accounted for 43% of baseline volume variance (P < .0001). Mean volume growth rate was 10.4 cm³/year (standard deviation, 8.8 cm³/year) (mean volume change +10.4%). Baseline volume accounted for 30% of volume growth variance; MTD accounted for 13% of volume growth variance. More tortuous aneurysms at baseline had significantly larger volume growth rates (difference, 32.8 cm³/year; P < .0001). Univariable analysis identified angiotensin II receptor blocker use (difference, -3.4 cm³/year; P = .02) and history of diabetes mellitus (difference, -2.8 cm³/year; P = .04) to be associated with lower rates of volume growth. Baseline volume, tortuosity index, current tobacco use, and absence of diabetes mellitus remained significantly associated with volume growth in multivariable analysis. AAAs that reached the MTD threshold for repair had a wide range of volumes: 102 cm³ to 142 cm³ in female patients (n = 5) and 105 cm³ to 229 cm³ in male patients (n = 20).

CONCLUSIONS

Baseline AAA volume and MTD were found to be moderately correlated. On average, AAA volume grows about 10% annually. Baseline volume, tortuosity, MTD, current tobacco use, angiotensin II receptor blocker use, and history of diabetes mellitus were predictive of volume growth over time.

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

Rupture risk assessment of abdominal aortic aneurysms (AAAs) by means of quantifying wall stress is a common biomechanical strategy. However, the clinical translation of this approach has been greatly limited due to the complexity associated with the computational tools required for its implementation. Thus, being able to estimate wall stress using nonbiomechanical markers that can be quantified as a direct outcome of clinical image segmentation would be advantageous in improving the potential implementation of said strategy. In the present work, we investigated the use of geometric indices to predict patient-specific 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 AAAs. 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. SAWS estimated from finite element analysis was considered the gold standard for the predictions. 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). With the reduced sets of indices, the NN-based approach exhibited the highest mean goodness-of-fit (for the symptomatic group 0.71 and for the asymptomatic group 0.79) and lowest mean relative error (17% for both groups). In contrast, MARS yielded a mean goodness-of-fit of 0.59 for the symptomatic group and 0.77 for the asymptomatic group, with relative errors of 17% for the symptomatic group and 22% for the asymptomatic group. GAM had a mean goodness-of-fit of 0.70 for the symptomatic group and 0.80 for the asymptomatic group, with relative errors of 16% for the symptomatic group and 20% for the asymptomatic group. GLM did not perform as well as the other algorithms, with a mean goodness-of-fit of 0.53 for the symptomatic group and 0.70 for the asymptomatic group, with relative errors of 19% for the symptomatic group and 23% for the asymptomatic group. Nevertheless, the NN models required a reduced set of 15 and 13 geometric indices to predict SAWS for the symptomatic and asymptomatic AAA groups, respectively. This was in contrast to the reduced set of nine and eight geometric indices required to predict SAWS with the MARS and GAM algorithms for each AAA group, respectively. 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. The performance metrics of NN models are expected to improve with significantly larger group sizes, given the suitability of NN modeling for "big data" applications.

Numerical analysis of stenoses severity and aortic wall mechanics in patients with supravalvular aortic stenosis

Supravalvular aortic stenosis (SVAS) is an aortic malformation characterized by a narrowing of the ascending aorta, resulting in abnormal hemodynamics and pressure drop across the stenosed region. It has been observed that the pressure drops measured from Doppler ultrasound exams often tend to be higher than those obtained from invasive cardiac catheterization. These misleadingly elevated pressure measurements may drive the decision to refer patients for surgical treatment prematurely. Considering this strong clinical association, the purpose of this work is to develop a computational modeling approach using a two-way coupled fluid-structure interaction methodology to determine an accurate prediction of trans-stenotic pressure drop and to further highlight the discrepancy between the SVAS assessment methods. Blood is modeled using Navier-Stokes equations while the aortic wall is simulated by a composite poroelastic structure to represent the three main layers of the arterial wall. The relationship between aortic wall elasticity and the blood flow conditions is examined in varying levels of stenosis, ranging from mild to severe degrees of vessel diameter narrowing. A substantial overestimation of the traditional Doppler pressure drop measurement is observed, especially for severe stenosis levels. The simulation results indicate that elasticity of the aortic wall has a relatively little effect on trans-stenotic pressure drop for the range of mild to moderate SVAS cases, but predicted to have a profound effect for severe SVAS cases. Moreover, significant sensitivity to the pressure drop across the SVAS region from stenosis severity is observed.

Machine Learning-Based Pulse Wave Analysis for Early Detection of Abdominal Aortic Aneurysms Using In Silico Pulse Waves

An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, the early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging only incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs—identified by parameter sensitivity analysis—in an existing database of in silico PWs representative of subjects without AAAs. Then, a machine learning architecture for AAA detection was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties considerably influencing the PWs. However, AAA detection by PW indexes was compromised by other non-AAA related cardiovascular parameters. The proposed machine learning model produced a sensitivity of 86.8 % and a specificity of 86.3 % in early detection of AAA from the photoplethysmogram PW signal measured in the digital artery with added random noise. The number of false positive and negative results increased with increasing age and decreasing AAA size, respectively. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.

A Comparative Study of Biomechanical and Geometrical Attributes of Abdominal Aortic Aneurysms in the Asian and Caucasian Populations

In this work, we provide a quantitative assessment of the biomechanical and geometric features that characterize abdominal aortic aneurysm (AAA) models generated from 19 Asian and 19 Caucasian diameter-matched AAA patients. 3D patient-specific finite element models were generated and used to compute peak wall stress (PWS), 99th percentile wall stress (99th WS), and spatially averaged wall stress (AWS) for each AAA. In addition, 51 global geometric indices were calculated, which quantify the wall thickness, shape, and curvature of each AAA. The indices were correlated with 99th WS (the only biomechanical metric that exhibited significant association with geometric indices) using Spearman's correlation and subsequently with multivariate linear regression using backward elimination. For the Asian AAA group, 99th WS was highly correlated (R2 = 0.77) with three geometric indices, namely tortuosity, intraluminal thrombus volume, and area-averaged Gaussian curvature. Similarly, 99th WS in the Caucasian AAA group was highly correlated (R2 = 0.87) with six geometric indices, namely maximum AAA diameter, distal neck diameter, diameter-height ratio, minimum wall thickness variance, mode of the wall thickness variance, and area-averaged Gaussian curvature. Significant differences were found between the two groups for ten geometric indices; however, no differences were found for any of their respective biomechanical attributes. Assuming maximum AAA diameter as the most predictive metric for wall stress was found to be imprecise: 24% and 28% accuracy for the Asian and Caucasian groups, respectively. This investigation reveals that geometric indices other than maximum AAA diameter can serve as predictors of wall stress, and potentially for assessment of aneurysm rupture risk, in the Asian and Caucasian AAA populations.

On the relative impact of intraluminal thrombus heterogeneity on abdominal aortic aneurysm mechanics

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 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.

Wall Stress and Geometry Measures in Electively Repaired Abdominal Aortic Aneurysms

Abdominal aortic aneurysm (AAA) is a vascular disease characterized by the enlargement of the infrarenal segment of the aorta. A ruptured AAA can cause internal bleeding and carries a high mortality rate, which is why the clinical management of the disease is focused on preventing aneurysm rupture. AAA rupture risk is estimated by the change in maximum diameter over time (i.e., growth rate) or if the diameter reaches a prescribed threshold. The latter is typically 5.5 cm in most clinical centers, at which time surgical intervention is recommended. While a size-based criterion is suitable for most patients who are diagnosed at an early stage of the disease, it is well known that some small AAA rupture or patients become symptomatic prior to a maximum diameter of 5.5 cm. Consequently, the mechanical stress in the aortic wall can also be used as an integral component of a biomechanics-based rupture risk assessment strategy. In this work, we seek to identify geometric characteristics that correlate strongly with wall stress using a sample space of 100 asymptomatic, unruptured, electively repaired AAA models. The segmentation of the clinical images, volume meshing, and quantification of up to 45 geometric measures of each AAA were done using in-house Matlab scripts. Finite element analysis was performed to compute the first principal stress distributions from which three global biomechanical parameters were calculated: peak wall stress, 99th percentile wall stress and spatially averaged wall stress. Following a feature reduction approach consisting of Pearson's correlation matrices with Bonferroni correction and linear regressions, a multivariate stepwise regression analysis was conducted to find the geometric measures most highly correlated with each of the biomechanical parameters. Our findings indicate that wall stress can be predicted by geometric indices with an accuracy of up to 94% when AAA models are generated with uniform wall thickness and up to 67% for patient specific, non-uniform wall thickness AAA. These geometric predictors of wall stress could be used in lieu of complex finite element models as part of a geometry-based protocol for rupture risk assessment.

Finite element analysis in symptomatic and asymptomatic abdominal aortic aneurysms for aortic disease risk stratification

BACKGROUND

Advanced biomechanical models can provide additional information concerning rupture risk in abdominal aortic aneurysms (AAA). Here we evaluated the predictive value of finite element analysis (FEA) to assess AAA rupture risk.

METHODS

In a case-control study, we compared FEA parameters in a group of symptomatic AAA (sAAA) patients, considered as a high risk of rupture group, with FEA parameters in asymptomatic AAA patients (aAAA).

RESULTS

We included 15 sAAA and 28 aAAA patients matched for age- and maximum diameter diagnosed with infrarenal non-ruptured AAA at our center between 2009 and 2013. Mean age was 75±69 years and mean maximum diameter was 77±17 mm. Peak wall stress (PWS) was significantly higher in sAAA patients than in aAAA patients (354.3±139.6 kPa vs. 248.6±81.9 kPa; P=0.001). The C statistic for the ROC curve based on PWS was 0.748 (95% CI: 0.592-0.903; P=0.008). CART analysis classified patients into high and low PWS groups. The high-PWS group (>305.15 kPa; N.=15) had a higher incidence of sAAA (33.3% aAAA, 66.7% sAAA) than the low-PWS-group (≤305.15 kPa; N.=28. 82.1% aAAA, 17.9% sAAA).

CONCLUSIONS

In conclusion, PWS was significantly higher in sAAA patients. Measuring PWS may help estimate the individual rupture risk in patients with AAA, but larger studies are needed to confirm our results.

On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes

This study aims to review retrospectively the records of Asian patients diagnosed with abdominal aortic aneurysm to investigate the potential correlations between clinical and morphological parameters within the context of whether the aneurysms were ruptured or unruptured. A machine-learning-based approach is proposed to predict the rupture status of Asian abdominal aortic aneurysm by comparing four different classifiers trained with clinical and geometrical parameters obtained from computed tomography images. The classifiers were applied on 312 patient data sets obtained from a regulatory-approved database. The data sets included 17 attributes under three classes: unruptured abdominal aortic aneurysm, ruptured abdominal aortic aneurysm, and normal aorta without aneurysm. Four different classification models, namely, Decision trees, Naïve Bayes, logistic regression, and support vector machines were applied to the patient data set. The models were evaluated by 10-fold cross-validation and the classifier performances were assessed with classification accuracy, area under the curve of receiver operator characteristic, and F-measures. Data analysis and evaluation were performed using the Weka machine learning application. The results indicated that Naïve Bayes achieved the best performance among the classifiers with a classification accuracy of 95.2%, an area under the curve of 0.974, and an F-measure of 0.952. The clinical implications of this work can be addressed in two ways. The best classifier can be applied to prospectively acquired data to predict the likelihood of aneurysm rupture. Next, it would be necessary to estimate the attributes implicated in rupture risk beyond just maximum aneurysm diameter.

Geometric surrogates of abdominal aortic aneurysm wall mechanics

The maximum diameter criterion is the most important factor in the clinical management of abdominal aortic aneurysms (AAA). Consequently, interventional repair is recommended when an aneurysm reaches a critical diameter, typically 5.0 cm in the United States. Nevertheless, biomechanical measures of the aneurysmal abdominal aorta have long been implicated in AAA risk of rupture. The purpose of this study is to assess whether other geometric characteristics, in addition to maximum diameter, may be highly correlated with the AAA peak wall stress (PWS). Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis using an isotropic constitutive material for the AAA wall. PWS, evaluated as the spatial maximum of the first principal stress, was calculated at a systolic pressure of 120 mmHg. The models were also used to calculate 47 geometric indices characteristic of the aneurysm geometry. Statistical analyses were conducted using a feature reduction algorithm in which the 47 indices were reduced to 11 based on their statistical significance in differentiating the models in the population (p < 0.05). A subsequent discriminant analysis was performed and 7 of these indices were identified as having no error in discriminating the AAA models with a significant nonlinear regression correlation with PWS. These indices were: D_max (maximum diameter), T (tortuosity), DDr (maximum diameter to neck diameter ratio), S (wall surface area), K_median (median of the Gaussian surface curvature), C_max (maximum lumen compactness), and M_mode (mode of the Mean surface curvature). Therefore, these characteristics of an individual AAA geometry are the highest correlated with the most clinically relevant biomechanical parameter for rupture risk assessment. We conclude that the indices can serve as surrogates of PWS in lieu of a finite element modeling approach for AAA biomechanical evaluation.

Mechanical and geometrical determinants of wall stress in abdominal aortic aneurysms: A computational study

An aortic aneurysm (AA) is a focal dilatation of the aortic wall. Occurrence of AA rupture is an all too common event that is associated with high levels of patient morbidity and mortality. The decision to surgically intervene prior to AA rupture is made with recognition of significant procedural risks, and is primarily based on the maximal diameter and/or growth rate of the AA. Despite established thresholds for intervention, rupture occurs in a notable subset of patients exhibiting sub-critical maximal diameters and/or growth rates. Therefore, a pressing need remains to identify better predictors of rupture risk and ultimately integrate their measurement into clinical decision making. In this study, we use a series of finite element-based computational models that represent a range of plausible AA scenarios, and evaluate the relative sensitivity of wall stress to geometrical and mechanical properties of the aneurysmal tissue. Taken together, our findings encourage an expansion of geometrical parameters considered for rupture risk assessment, and provide perspective on the degree to which tissue mechanical properties may modulate peak stress values within aneurysmal tissue.

The Relationship Between Surface Curvature and Abdominal Aortic Aneurysm Wall Stress

The maximum diameter (MD) criterion is the most important factor when predicting risk of rupture of abdominal aortic aneurysms (AAAs). An elevated wall stress has also been linked to a high risk of aneurysm rupture, yet is an uncommon clinical practice to compute AAA wall stress. The purpose of this study is to assess whether other characteristics of the AAA geometry are statistically correlated with wall stress. Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis (FEA). These models were subsequently used to estimate wall stress and maximum diameter and to evaluate the spatial distributions of wall thickness, cross-sectional diameter, mean curvature, and Gaussian curvature. Data analysis consisted of statistical correlations of the aforementioned geometry metrics with wall stress for the 30 AAA inner and outer wall surfaces. In addition, a linear regression analysis was performed with all the AAA wall surfaces to quantify the relationship of the geometric indices with wall stress. These analyses indicated that while all the geometry metrics have statistically significant correlations with wall stress, the local mean curvature (LMC) exhibits the highest average Pearson's correlation coefficient for both inner and outer wall surfaces. The linear regression analysis revealed coefficients of determination for the outer and inner wall surfaces of 0.712 and 0.516, respectively, with LMC having the largest effect on the linear regression equation with wall stress. This work underscores the importance of evaluating AAA mean wall curvature as a potential surrogate for wall stress.

The Association Between Geometry and Wall Stress in Emergently Repaired Abdominal Aortic Aneurysms

Abdominal aortic aneurysm (AAA) is a prevalent cardiovascular disease characterized by the focal dilation of the aorta, which supplies blood to all the organs and tissues in the systemic circulation. With the AAA increasing in diameter over time, the risk of aneurysm rupture is generally associated with the size of the aneurysm. If diagnosed on time, intervention is recommended to prevent AAA rupture. The criterion to decide on surgical intervention is determined by measuring the maximum diameter of the aneurysm relative to the critical value of 5.5 cm. However, a more reliable approach could be based on understanding the biomechanical behavior of the aneurysmal wall. In addition, geometric features that are proven to be significant predictors of the AAA wall mechanics could be used as surrogates of the AAA biomechanical behavior and, subsequently, of the aneurysm's risk of rupture. The aim of this work is to identify those geometric indices that have a high correlation with AAA wall stress in the population of patients who received an emergent repair of their aneurysm. In-house segmentation and meshing algorithms were used to model 75 AAAs followed by estimation of the spatially distributed wall stress by performing finite element analysis. Fifty-two shape and size geometric indices were calculated for the same models using MATLAB scripting. Hypotheses testing were carried out to identify the indices significantly correlated with wall stress by constructing a Pearson's correlation coefficient matrix. The analyses revealed that 12 indices displayed high correlation with the wall stress, amongst which wall thickness and curvature-based indices exhibited the highest correlations. Stepwise regression analysis of these correlated indices indicated that wall stress can be predicted by the following four indices with an accuracy of 76%: maximum aneurysm diameter, aneurysm sac length, average wall thickness at the maximum diameter cross-section, and the median of the wall thickness variance. The primary outcome of this work emphasizes the use of global measures of size and wall thickness as geometric surrogates of wall stress for emergently repaired AAAs.

Correlation between transversal and orthogonal maximal diameters of abdominal aortic aneurysms and alternative rupture risk predictors

PURPOSE

There is no standard for measuring maximal diameter (Dmax) of abdominal aortic aneurysm (AAA) from computer tomography (CT) images although differences between Dmax evaluated from transversal (axialDmax) or orthogonal (orthoDmax) planes can be large especially for angulated AAAs. Therefore we investigated their correlations with alternative rupture risk indicators as peak wall stress (PWS) and peak wall rupture risk (PWRR) to decide which Dmax is more relevant in AAA rupture risk assessment.

MATERIAL AND METHODS

The Dmax values were measured by a trained radiologist from 70 collected CT scans, and the corresponding PWS and PWRR were evaluated using Finite Element Analysis (FEA). The cohort was ordered according to the difference between axialDmax and orthoDmax (D_a-o) quantifying the aneurysm angulation, and Spearman's correlation coefficients between PWS/PWRR - orthoDmax/axialDmax were calculated.

RESULTS

The calculated correlations PWS/PWRR vs. orthoDmax were substantially higher for angulated AAAs (with D_a-o≥3mm). Under this limit, the correlations were almost the same for both Dmax values. Analysis of AAAs divided into two groups of angulated (n=38) and straight (n=32) cases revealed that both groups are similar in all parameters (orthoDmax, PWS, PWRR) with the exception of axialDmax (p=0.024).

CONCLUSIONS

It was confirmed that orthoDmax is better correlated with the alternative rupture risk predictors PWS and PWRR for angulated AAAs (D_A-O≥3mm) while there is no difference between orthoDmax and axialDmax for straight AAAs (D_A-O<3mm). As angulated AAAs represent a significant portion of cases it can be recommended to use orthoDmax as the only Dmax parameter for AAA rupture risk assessment.

Multi-component model of intramural hematoma

A novel multi-component model is introduced for studying interaction between blood flow and deforming aortic wall with intramural hematoma (IMH). The aortic wall is simulated by a composite structure submodel representing material properties of the three main wall layers. The IMH is described by a poroelasticity submodel which takes into account both the pressure inside hematoma and its deformation. The submodel of the hematoma is fully coupled with the aortic submodel as well as with the submodel of the pulsatile blood flow. Model simulations are used to investigate the relation between the peak wall stress, hematoma thickness and permeability in patients of different age. The results indicate that an increase in hematoma thickness leads to larger wall stress, which is in agreement with clinical data. Further simulations demonstrate that a hematoma with smaller permeability results in larger wall stress, suggesting that blood coagulation in hematoma might increase its mechanical stability. This is in agreement with previous experimental observations of coagulation having a beneficial effect on the condition of a patient with the IMH.

Computational Growth and Remodeling of Abdominal Aortic Aneurysms Constrained by the Spine

Abdominal aortic aneurysms (AAAs) evolve over time, and the vertebral column, which acts as an external barrier, affects their biomechanical properties. Mechanical interaction between AAAs and the spine is believed to alter the geometry, wall stress distribution, and blood flow, although the degree of this interaction may depend on AAAs specific configurations. In this study, we use a growth and remodeling (G&R) model, which is able to trace alterations of the geometry, thus allowing us to computationally investigate the effect of the spine for progression of the AAA. Medical image-based geometry of an aorta is constructed along with the spine surface, which is incorporated into the computational model as a cloud of points. The G&R simulation is initiated by local elastin degradation with different spatial distributions. The AAA-spine interaction is accounted for using a penalty method when the AAA surface meets the spine surface. The simulation results show that, while the radial growth of the AAA wall is prevented on the posterior side due to the spine acting as a constraint, the AAA expands faster on the anterior side, leading to higher curvature and asymmetry in the AAA configuration compared to the simulation excluding the spine. Accordingly, the AAA wall stress increases on the lateral, posterolateral, and the shoulder regions of the anterior side due to the AAA-spine contact. In addition, more collagen is deposited on the regions with a maximum diameter. We show that an image-based computational G&R model not only enhances the prediction of the geometry, wall stress, and strength distributions of AAAs but also provides a framework to account for the interactions between an enlarging AAA and the spine for a better rupture potential assessment and management of AAA patients.

Assessing the potential risk of rupture of abdominal aortic aneurysms

Abdominal aortic aneurysms (AAAs) involve complex interplays between inflammatory and biomechanical factors that can be elucidated with anatomical and functional imaging. Although AAA size has been well-established in the literature to correlate with risk of rupture (and subsequent need for vascular intervention), there are other less-well-known characteristics about AAAs that also contribute to higher risk of rupture. This review focuses on biomechanical, radiological, and epidemiological characteristics of AAAs that are associated with higher rupture risk. For clinicians, knowing and considering a wide variety of risk factors in addition to AAA size is important to initiate early and proper intervention for AAA repair. Although there is no official quantitative risk score of AAA rupture risk that takes other non-size-related variables into account, if clinicians are aware of these other parameters, it is hoped that intervention can be appropriately performed for higher-risk AAAs that have not met the size-threshold for elective repair.

Advancements in identifying biomechanical determinants for abdominal aortic aneurysm rupture

Abdominal aortic aneurysms are a common health problem and currently the need for surgical intervention is determined based on maximum diameter and growth rate criteria. Since these universal variables often fail to predict accurately every abdominal aortic aneurysms evolution, there is a considerable effort in the literature for other markers to be identified towards individualized rupture risk estimations and growth rate predictions. To this effort, biomechanical tools have been extensively used since abdominal aortic aneurysm rupture is in fact a material failure of the diseased arterial wall to compensate the stress acting on it. The peak wall stress, the role of the unique geometry of every individual abdominal aortic aneurysm as well as the mechanical properties and the local strength of the degenerated aneurysmal wall, all confer to rupture risk. In this review article, the assessment of these variables through mechanical testing, advanced imaging and computational modeling is reviewed and the clinical perspective is discussed.

Changes in geometric configuration and biomechanical parameters of a rapidly growing abdominal aortic aneurysm may provide insight in aneurysms natural history and rupture risk

BACKGROUND

Abdominal aortic aneurysms (AAA) are currently being treated based on the maximum diameter criterion which has often been proven insufficient to determine rupture risk in case of every AAA. We analyzed a rare case of an AAA which presented an extremely fast growth focusing on biomechanical determinants that may indicate a high risk profile. The examination of such a case is expected to motivate future research towards patient-specific rupture risk estimations.

METHODS

An initially small AAA (maximum diameter: 4.5 cm) was followed-up and presented a growth of 1 cm in only 6-months of surveillance becoming suitable for surgical repair. Changes of morphometric characteristics regarding AAA, thrombus and lumen volumes, cross-sectional areas, thrombus maximum thickness and eccentricity, and maximum centerline curvature were recorded. Moreover biomechanical variables concerning Peak Wall Stress, AAA surface area exposed to high stress and redistribution of stress during follow-up were also assessed.

RESULTS

Total aneurysm volume increased from 85 to 120 ml which regarded thrombus deposition since lumen volume remained stable. Thrombus deposition was eccentric regarding anterior AAA segment while its thickness increased from 0.3 cm to 1.6 cm. Moreover there was an anterior bulging over time as depicted by an increase in maximum centerline curvature from 0.4 cm-1 to 0.5 cm-1. Peak Wall Stress (PWS) exerted on aneurysm wall did not change significantly over time, slightly decreasing from 22 N/cm2 to 21 N/cm2. At the same time the area under high wall stress remained practically constant (9.9 cm2 at initial vs 9.7 cm2 at final examination) but there was a marked redistribution of wall stress against the posterior aneurysmal wall over time.

CONCLUSION

Aneurysm area under high stress and redistribution of stress against the posterior wall due to changes in geometric configuration and thrombus deposition over time may have implications to aneurysms natural history and rupture risk.

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.

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.

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.

Geometrical factors as predictors of increased growth rate or increased rupture risk in small aortic aneurysms

Abdominal Aortic Aneurysms (AAAs) are focal dilation of the aorta that can lead to excessive enlargement and rupture over time. Current practice suggests intervention when the maximum diameter exceeds 5.5 cm, since in this diameter range the annual rupture risk outweighs the operative mortality. However, small AAA (<5.5 cm), though infrequently, may rupture or produce symptoms. Evidence from large randomized studies of small AAAs support the heterogeneity in patterns of growth and rupture potential among small AAAs. Elevated wall stress values have been implicated in AAAs rupture and rapid enlargement. Additionally, many studies have identified a strong correlation between certain geometric factors and elevated stress values. In this article we discuss the possibility that geometrical factors may have a predictive value to identify those small AAAs that have an increased risk of rupture or growth rate either during initial examination or during follow-up, making them amenable for early repair.

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.

Application of Bioengineering Modalities in Vascular Research: Evaluating the Clinical Gain

Using knowledge gained from bioengineering studies, current vascular research focuses on the delineation of the natural history and risk assessment of clinical vascular entities with significant morbidity and mortality, making the development of new, more accurate predictive criteria a great challenge. Additionally, conclusions derived from computational simulation studies have enabled the improvement and modification of many biotechnology products that are used routinely in the treatment of vascular diseases. This review highlights the promising role of the bioengineering applications in the vascular field.

Computational evaluation of aortic aneurysm rupture risk: what have we learned so far?

In current clinical practice, aneurysm diameter is one of the primary criteria used to decide when to treat a patient with an abdominal aortic aneurysm (AAA). It has been shown that simple association of aneurysm diameter with the probability of rupture is not sufficient, and other parameters may also play a role in causing or predisposing to AAA rupture. Peak wall stress (PWS), intraluminal thrombus (ILT), and AAA wall mechanics are the factors most implicated with rupture risk and have been studied by computational risk evaluation techniques. The objective of this review is to examine these factors that have been found to influence AAA rupture. The prediction rate of rupture among computational models depends on the level of model complexity and the predictive value of the biomechanical parameters used to assess risk, such as PWS, distribution of ILT, wall strength, and the site of rupture. There is a need for simpler geometric analogues, including geometric parameters (e.g., lumen tortuosity and neck length and angulation) that correlate well with PWS, conjugated with clinical risk factors for constructing rupture risk predictive models. Such models should be supported by novel imaging techniques to provide the required patient-specific data and validated through large, prospective clinical trials.

Indicators of survival after open repair of ruptured abdominal aortic aneurysms and an index for predicting aneurysmal rupture potential

BACKGROUND

The aims of this study were to assess variables associated with survival in patients undergoing ruptured abdominal aortic aneurysm (RAAA) repair and to develop an index other than the aneurysmal diameter to predict rupture potential.

METHODS

This study included 43 consecutive patients who underwent open surgery for RAAAs.

RESULTS

The mortality rate was 18.6% (8/43). The ratio between the maximum aneurysmal diameter and the length (along the central axis) from the aneurysmal neck to the point at which the diameter was three-fourth of the maximum aneurysmal diameter was used as an index to predict aneurysmal rupture potential. The index score was 2.7 ± 1.2 in the RAAA and 1.9 ± 0.9 in the EAAA (p = 0.018). For aneurysms of ≤ 6-cm diameter, the index score was 3.0 ± 1.0 in the RAAA and 1.8 ± 0.9 in the EAAA (p = 0.03). All patients in the EAAA except one had an index score of < 2.3 and 6 of the 7 patients with RAAA had a score of > 3.

CONCLUSIONS

The results suggest that patients with AAA having scores of > 3 are at high risk of rupture. This index would be useful for decision making regarding repair of AAA, especially in the borderline cases.

Expanding Current EVAR Indications to Include Small Abdominal Aortic Aneurysms: A Glimpse of the Future

The traditional criterion of maximum transverse diameter is not sufficient to differentiate the small abdominal aortic aneurysms (AAAs) that are either prone to rupture or prone to enlarge rapidly. Wall stress may be a more reliable indicator with respect to these tasks. We review the importance of geometric features in rupture- or growth-predictive models and stress the need for further evaluation and validation of geometric indices. This study may lead to identifying those small AAAs that could justify early endovascular intervention.

Quantitative assessment of abdominal aortic aneurysm geometry

Recent studies have shown that the maximum transverse diameter of an abdominal aortic aneurysm (AAA) and expansion rate are not entirely reliable indicators of rupture potential. We hypothesize that aneurysm morphology and wall thickness are more predictive of rupture risk and can be the deciding factors in the clinical management of the disease. A non-invasive, image-based evaluation of AAA shape was implemented on a retrospective study of 10 ruptured and 66 unruptured aneurysms. Three-dimensional models were generated from segmented, contrast-enhanced computed tomography images. Geometric indices and regional variations in wall thickness were estimated based on novel segmentation algorithms. A model was created using a J48 decision tree algorithm and its performance was assessed using ten-fold cross validation. Feature selection was performed using the χ2-test. The model correctly classified 65 datasets and had an average prediction accuracy of 86.6% (κ=0.37). The highest ranked features were sac length, sac height, volume, surface area, maximum diameter, bulge height, and intra-luminal thrombus volume. Given that individual AAAs have complex shapes with local changes in surface curvature and wall thickness, the assessment of AAA rupture risk should be based on the accurate quantification of aneurysmal sac shape and size.

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.

Identification of rupture locations in patient-specific abdominal aortic aneurysms using experimental and computational techniques

In the event of abdominal aortic aneurysm (AAA) rupture, the outcome is often death. This paper aims to experimentally identify the rupture locations of in vitro AAA models and validate these rupture sites using finite element analysis (FEA). Silicone rubber AAA models were manufactured using two different materials (Sylgard 160 and Sylgard 170, Dow Corning) and imaged using computed tomography (CT). Experimental models were inflated until rupture with high speed photography used to capture the site of rupture. 3D reconstructions from CT scans and subsequent FEA of these models enabled the wall stress and wall thickness to be determined for each of the geometries. Experimental models ruptured at regions of inflection, not at regions of maximum diameter. Rupture pressures (mean+/-SD) for the Sylgard 160 and Sylgard 170 models were 650.6+/-195.1mmHg and 410.7+/-159.9mmHg, respectively. Computational models accurately predicted the locations of rupture. Peak wall stress for the Sylgard 160 and Sylgard 170 models was 2.15+/-0.26MPa at an internal pressure of 650mmHg and 1.69+/-0.38MPa at an internal pressure of 410mmHg, respectively. Mean wall thickness of all models was 2.19+/-0.40mm, with a mean wall thickness at the location of rupture of 1.85+/-0.33 and 1.71+/-0.29mm for the Sylgard 160 and Sylgard 170 materials, respectively. Rupture occurred at the location of peak stress in 80% (16/20) of cases and at high stress regions but not peak stress in 10% (2/20) of cases. 10% (2/20) of models had defects in the AAA wall which moved the rupture location away from regions of elevated stress. The results presented may further contribute to the understanding of AAA biomechanics and ultimately AAA rupture prediction.