Semiautomatic vessel wall detection and quantification of wall thickness in computed tomography images of human abdominal aortic aneurysms

Efficient and Accurate 3D Thickness Measurement in Vessel Wall Imaging: Overcoming Limitations of 2D Approaches Using the Laplacian Method

The clinical significance of measuring vessel wall thickness is widely acknowledged. Recent advancements have enabled high-resolution 3D scans of arteries and precise segmentation of their lumens and outer walls; however, most existing methods for assessing vessel wall thickness are 2D. Despite being valuable, reproducibility and accuracy of 2D techniques depend on the extracted 2D slices. Additionally, these methods fail to fully account for variations in wall thickness in all dimensions. Furthermore, most existing approaches are difficult to be extended into 3D and their measurements lack spatial localization and are primarily confined to lumen boundaries. We advocate for a shift in perspective towards recognizing vessel wall thickness measurement as inherently a 3D challenge and propose adapting the Laplacian method as an outstanding alternative. The Laplacian method is implemented using convolutions, ensuring its efficient and rapid execution on deep learning platforms. Experiments using digital phantoms and vessel wall imaging data are conducted to showcase the accuracy, reproducibility, and localization capabilities of the proposed approach. The proposed method produce consistent outcomes that remain independent of centerlines and 2D slices. Notably, this approach is applicable in both 2D and 3D scenarios. It allows for voxel-wise quantification of wall thickness, enabling precise identification of wall volumes exhibiting abnormal wall thickness. Our research highlights the urgency of transitioning to 3D methodologies for vessel wall thickness measurement. Such a transition not only acknowledges the intricate spatial variations of vessel walls, but also opens doors to more accurate, localized, and insightful diagnostic insights.

A quarter of a century biomechanical rupture risk assessment of abdominal aortic aneurysms. Achievements, clinical relevance, and ongoing developments

Abdominal aortic aneurysm (AAA) disease, the local enlargement of the infrarenal aorta, is a serious condition that causes many deaths, especially in men exceeding 65 years of age. Over the past quarter of a century, computational biomechanical models have been developed towards the assessment of AAA risk of rupture, technology that is now on the verge of being integrated within the clinical decision-making process. The modeling of AAA requires a holistic understanding of the clinical problem, in order to set appropriate modeling assumptions and to draw sound conclusions from the simulation results. In this article we summarize and critically discuss the proposed modeling approaches and report the outcome of clinical validation studies for a number of biomechanics-based rupture risk indices. Whilst most of the aspects concerning computational mechanics have already been settled, it is the exploration of the failure properties of the AAA wall and the acquisition of robust input data for simulations that has the greatest potential for the further improvement of this technology.

Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Abdominal aortic aneurysm (AAA) is one of the leading causes of death worldwide. AAAs often remain asymptomatic until they are either close to rupturing or they cause pressure to the spine and/or other organs. Fast progression has been linked to future clinical outcomes. Therefore, a reliable and efficient system to quantify geometric properties and growth will enable better clinical prognoses for aneurysms. Different imaging systems can be used to locate and characterize an aneurysm; computed tomography (CT) is the modality of choice in many clinical centers to monitor later stages of the disease and plan surgical treatment. The lack of accurate and automated techniques to segment the outer wall and lumen of the aneurysm results in either simplified measurements that focus on few salient features or time-consuming segmentation affected by high inter- and intra-operator variability. To overcome these limitations, we propose a model for segmenting AAA tissues automatically by using a trained deep learning-based approach. The model is composed of three different steps starting with the extraction of the aorta and iliac arteries followed by the detection of the lumen and other AAA tissues. The results of the automated segmentation demonstrate very good agreement when compared to manual segmentation performed by an expert.

A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre- and post-operative abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is one of the most common diseases worldwide. 3D segmentation of AAA provides useful information for surgical decisions and follow-up treatment. However, existing segmentation methods are time consuming and not practical in routine use. In this article, the segmentation task will be addressed automatically using a deep learning based approach which has been proved to successfully solve several medical imaging problems with excellent performances. This article therefore proposes a new solution of AAA segmentation using deep learning in a type of 3D convolutional neural network (CNN) architecture that also incorporates coordinate information. The tested CNNs are UNet, AG-DSV-UNet, VNet, ResNetMed and DenseVoxNet. The 3D-CNNs are trained with a dataset of high resolution (256 × 256) non-contrast and post-contrast CT images containing 64 slices from each of 200 patients. The dataset consists of contiguous CT slices without augmentation and no post-processing step. The experiments show that incorporation of coordinate information improves the segmentation results. The best accuracies on non-contrast and contrast-enhanced images have average dice scores of 97.13% and 96.74%, respectively. Transfer learning from a pre-trained network of a pre-operative dataset to post-operative endovascular aneurysm repair (EVAR) was also performed. The segmentation accuracy of post-operative EVAR using transfer learning on non-contrast and contrast-enhanced CT datasets achieved the best dice scores of 94.90% and 95.66%, respectively.

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.

The risk of rupture and abdominal aortic aneurysm morphology: A computational study

Prediction of rupture and optimal timing for abdominal aortic aneurysm (AAA) surgical intervention remain wanting even after decades of clinical, histological, and numerical research. Although studies estimating rupture from AAA geometrical features from CT imaging showed some promising results, they are still not being used in practice. Patient-specific numerical stress analysis introduced too many assumptions about wall structure for the related rupture potential index (RPI) to be considered reliable. Growth and remodeling (G&R) numerical models eliminate some of these assumptions and thus might have the most potential to calculate mural stresses and RPI and increase our understanding of rupture. To recognize numerical models as trustworthy, it is necessary to validate the computed results with results derived from imaging. Elastin degradation function is one of the main factors that determine idealized aneurysm sac shape. Using a hundred different combinations of variables defining AAA geometry or influences AAA stability (elastin degradation function parameters, collagen mechanics, and initial healthy aortic diameters), we investigated the relationship between AAA morphology and RPI and compared numerical results with clinical findings. Good agreement of numerical results with clinical expectations from the literature gives us confidence in the validity of the numerical model. We show that aneurysm morphology significantly influences the stability of aneurysms. Additionally, we propose new parameters, geometrical rupture potential index (GRPI) and normalized aneurysm length (NAL), that might predict rupture of aneurysms without thrombus better than currently used criteria (i.e., maximum diameter and growth rate). These parameters can be computed quickly, without the tedious processing of CT images.

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.

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.

Towards a Clinical Implementation of Measuring the Elastic Modulus of the Aorta from Cardiac Computed Tomography Images

OBJECTIVE

The elasticity of the aortic wall varies depending on age, vessel location, and the presence of aortic diseases. Noninvasive measurement will be a powerful tool to understand the mechanical state of the aorta in a living human body. This study aimed to determine the elastic modulus of the aorta using computed tomography images.

METHODS

We constructed our original formulae based on mechanics of materials. Then, we performed computed tomography scans of a silicon rubber tube by applying four pressure conditions to the lumen. The segment elastic modulus was calculated from the scanned images using our formulae. The actual modulus was measured using a tensile loading test for comparison.

RESULTS

The segment moduli of elasticity from the images were 0.525 [0.524, 0.527], 0.524 [0.520, 0.524], 0.520 [0.515, 0.523], and 0.522 [0.516, 0.532] (unit: MPa, median [25%, 75% quantiles]) for the four pressure conditions, respectively. The corresponding measurements in the tensile test were 0.548 [0.539, 0.566], 0.535 [0.528, 0.553], 0.526 [0.513, 0.543], and 0.523 [0.508, 0.530], respectively. These results indicated errors of 4.2%, 2.1%, 1.1%, and 0.2%, respectively.

CONCLUSION

Our formulae provided good estimations of the segment elastic moduli of a silicon rubber tube under physiological pressure conditions using the computed tomography images.

SIGNIFICANCE

In addition to the elasticity, the formulae provide the strain energy as well. These properties can be better predictors of aortic diseases. The formulae consist of clinical parameters commonly used in medical settings (pressure, diameter, and wall thickness).

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.

Artificial intelligence in abdominal aortic aneurysm

OBJECTIVE

Abdominal aortic aneurysm (AAA) is a life-threatening disease, and the only curative treatment relies on open or endovascular repair. The decision to treat relies on the evaluation of the risk of AAA growth and rupture, which can be difficult to assess in practice. Artificial intelligence (AI) has revealed new insights into the management of cardiovascular diseases, but its application in AAA has so far been poorly described. The aim of this review was to summarize the current knowledge on the potential applications of AI in patients with AAA.

METHODS

A comprehensive literature review was performed. The MEDLINE database was searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy used a combination of keywords and included studies using AI in patients with AAA published between May 2019 and January 2000. Two authors independently screened titles and abstracts and performed data extraction. The search of published literature identified 34 studies with distinct methodologies, aims, and study designs.

RESULTS

AI was used in patients with AAA to improve image segmentation and for quantitative analysis and characterization of AAA morphology, geometry, and fluid dynamics. AI allowed computation of large data sets to identify patterns that may be predictive of AAA growth and rupture. Several predictive and prognostic programs were also developed to assess patients' postoperative outcomes, including mortality and complications after endovascular aneurysm repair.

CONCLUSIONS

AI represents a useful tool in the interpretation and analysis of AAA imaging by enabling automatic quantitative measurements and morphologic characterization. It could be used to help surgeons in preoperative planning. AI-driven data management may lead to the development of computational programs for the prediction of AAA evolution and risk of rupture as well as postoperative outcomes. AI could also be used to better evaluate the indications and types of surgical treatment and to plan the postoperative follow-up. AI represents an attractive tool for decision-making and may facilitate development of personalized therapeutic approaches for patients with AAA.

A Comparative Classification Analysis of Abdominal Aortic Aneurysms by Machine Learning Algorithms

The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tomography angiography images of 100 asymptomatic and 50 symptomatic AAA patients who received an elective or emergent repair, respectively, within 1-6 months of their last follow up. An in-house script developed within the MATLAB computational platform was used to segment the clinical images, calculate 53 descriptors of AAA geometry, and generate volume meshes suitable for finite element analysis (FEA). Using a third party FEA solver, four biomechanical markers were calculated from the wall stress distributions. Eight machine learning algorithms (MLA) were used to develop classification models based on the discriminatory potential of the demographic, geometric, and biomechanical variables. The overall classification performance of the algorithms was assessed by the accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and precision of their predictions. The generalized additive model (GAM) was found to have the highest accuracy (87%), AUC (89%), and sensitivity (78%), and the third highest specificity (92%), in classifying the individual AAA as either asymptomatic or symptomatic. The k-nearest neighbor classifier yielded the highest specificity (96%). GAM used seven markers (six geometric and one biomechanical) to develop the classifier. The maximum transverse dimension, the average wall thickness at the maximum diameter, and the spatially averaged wall stress were found to be the most influential markers in the classification analysis. A second classification analysis revealed that using maximum diameter alone results in a lower accuracy (79%) than using GAM with seven geometric and biomechanical markers. We infer from these results that biomechanical and geometric measures by themselves are not sufficient to discriminate adequately between population samples of asymptomatic and symptomatic AAA, whereas MLA offer a statistical approach to stratification of rupture risk by combining demographic, geometric, and biomechanical attributes of patient-specific AAA.

Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces

Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.

The Effect of Pentagalloyl Glucose on the Wall Mechanics and Inflammatory Activity of Rat Abdominal Aortic Aneurysms

An abdominal aortic aneurysm (AAA) is a permanent localized expansion of the abdominal aorta with mortality rate of up to 90% after rupture. AAA growth is a process of vascular degeneration accompanied by a reduction in wall strength and an increase in inflammatory activity. It is unclear whether this process can be intervened to attenuate AAA growth, and hence, it is of great clinical interest to develop a technique that can stabilize the AAA. The objective of this work is to develop a protocol for future studies to evaluate the effects of drug-based therapies on the mechanics and inflammation in rodent models of AAA. The scope of the study is limited to the use of pentagalloyl glucose (PGG) for aneurysm treatment in the calcium chloride rat AAA model. Peak wall stress (PWS) and matrix metalloproteinase (MMP) activity, which are the biomechanical and biological markers of AAA growth and rupture, were evaluated over 4 weeks in untreated and treated (with PGG) groups. The AAA specimens were mechanically characterized by planar biaxial tensile testing and the data fitted to a five-parameter nonlinear, hyperelastic, anisotropic Holzapfel–Gasser–Ogden (HGO) material model, which was used to perform finite element analysis (FEA) to evaluate PWS. Our results demonstrated that there was a reduction in PWS between pre- and post-AAA induction FEA models in the treatment group compared to the untreated group using either animal-specific or average material properties. However, this reduction was not statistically significant. Conversely, there was a statistically significant reduction in MMP-activated fluorescent signal between pre- and post-AAA induction models in the treated group compared to the untreated group. Therefore, the primary contribution of this work is the quantification of the stabilizing effects of PGG using biomechanical and biological markers of AAA, thus indicating that PGG could be part of a new clinical treatment strategy that will require further investigation.

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.

3D segmentation of exterior wall surface of abdominal aortic aneurysm from CT images using variable neighborhood search

A 3D model of abdominal aortic aneurysm (AAA) can provide useful anatomical information for clinical management and simulation. Thin-slice contiguous computed tomographic (CT) angiography is the best source of medical images for construction of 3D models, which requires segmentation of AAA in the images. Existing methods for segmentation of AAA rely on either manual process or 2D segmentation in each 2D CT slide. However, a traditional manual segmentation is a time consuming process which is not practical for routine use. The construction of a 3D model from 2D segmentation of each CT slice is not a fully satisfactory solution due to rough contours that can occur because of lack of constraints among segmented slices, as well as missed segmentation slices. To overcome such challenges, this paper proposes the 3D segmentation of AAA using the concept of variable neighborhood search by iteratively alternating between two different segmentation techniques in the two different 3D search spaces of voxel intensity and voxel gradient. The segmentation output of each method is used as the initial contour to the other method in each iteration. By alternating between search spaces, the technique can escape local minima that naturally occur in each search space. Also, the 3D search spaces provide more constraints across CT slices, when compared with the 2D search spaces in individual CT slices. The proposed method is evaluated with 10 easy and 10 difficult cases of AAA. The results show that the proposed 3D segmentation technique achieves the outstanding segmentation accuracy with an average dice similarity value (DSC) of 91.88%, when compared to the other methods using the same dataset, which are the 2D proposed method, classical graph cut, distance regularized level set evolution, and registration based geometric active contour with the DSCs of 87.57 ± 4.52%, 72.47 ± 8.11%, 58.50 ± 8.86% and 76.21 ± 10.49%, respectively.

Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures

Abdominal aortic aneurysm (AAA) is an asymptomatic aortic disease with a survival rate of 20% after rupture. It is a vascular degenerative condition different from occlusive arterial diseases. The size of the aneurysm is the most important determining factor in its clinical management. However, other measures of the AAA geometry that are currently not used clinically may also influence its rupture risk. With this in mind, the objectives of this work are to develop an algorithm to calculate the AAA wall thickness and abdominal aortic diameter at planes orthogonal to the vessel centerline, and to quantify the effect of geometric indices derived from this algorithm on the overall classification accuracy of AAA based on whether they were electively or emergently repaired. Such quantification was performed based on a retrospective review of existing medical records of 150 AAA patients (75 electively repaired and 75 emergently repaired). Using an algorithm implemented within the MATLAB computing environment, 10 diameter- and wall thickness-related indices had a significant difference in their means when calculated relative to the AAA centerline compared to calculating them relative to the medial axis. Of these 10 indices, nine were wall thickness-related while the remaining one was the maximum diameter (D_max). D_max calculated with respect to the medial axis is over-estimated for both electively and emergently repaired AAA compared to its counterpart with respect to the centerline. C5.0 decision trees, a machine learning classification algorithm implemented in the R environment, were used to construct a statistical classifier. The decision trees were built by splitting the data into 70% for training and 30% for testing, and the properties of the classifier were estimated based on 1000 random combinations of the 70/30 data split. The ensuing model had average and maximum classification accuracies of 81.0 and 95.6%, respectively, and revealed that the three most significant indices in classifying AAA are, in order of importance: AAA centerline length, L2-norm of the Gaussian curvature, and AAA wall surface area. Therefore, we infer that the aforementioned three geometric indices could be used in a clinical setting to assess the risk of AAA rupture by means of a decision tree classifier. This work provides support for calculating cross-sectional diameters and wall thicknesses relative to the AAA centerline and using size and surface curvature based indices in classification studies of AAA.

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.

Exploring the Biological and Mechanical Properties of Abdominal Aortic Aneurysms Using USPIO MRI and Peak Tissue Stress: A Combined Clinical and Finite Element Study

Inflammation detected through the uptake of ultrasmall superparamagnetic particles of iron oxide (USPIO) on magnetic resonance imaging (MRI) and finite element (FE) modelling of tissue stress both hold potential in the assessment of abdominal aortic aneurysm (AAA) rupture risk. This study aimed to examine the spatial relationship between these two biomarkers. Patients (n = 50) > 40 years with AAA maximum diameters > = 40 mm underwent USPIO-enhanced MRI and computed tomography angiogram (CTA). USPIO uptake was compared with wall stress predictions from CTA-based patient-specific FE models of each aneurysm. Elevated stress was commonly observed in areas vulnerable to rupture (e.g. posterior wall and shoulder). Only 16% of aneurysms exhibited co-localisation of elevated stress and mural USPIO enhancement. Globally, no correlation was observed between stress and other measures of USPIO uptake (i.e. mean or peak). It is suggested that cellular inflammation and stress may represent different but complimentary aspects of AAA disease progression.

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.

Segmentation of lumen and outer wall of abdominal aortic aneurysms from 3D black-blood MRI with a registration based geodesic active contour model

Segmentation of the geometric morphology of abdominal aortic aneurysm is important for interventional planning. However, the segmentation of both the lumen and the outer wall of aneurysm in magnetic resonance (MR) image remains challenging. This study proposes a registration based segmentation methodology for efficiently segmenting MR images of abdominal aortic aneurysms. The proposed methodology first registers the contrast enhanced MR angiography (CE-MRA) and black-blood MR images, and then uses the Hough transform and geometric active contours to extract the vessel lumen by delineating the inner vessel wall directly from the CE-MRA. The proposed registration based geometric active contour is applied to black-blood MR images to generate the outer wall contour. The inner and outer vessel wall are then fused presenting the complete vessel lumen and wall segmentation. The results obtained from 19 cases showed that the proposed registration based geometric active contour model was efficient and comparable to manual segmentation and provided a high segmentation accuracy with an average Dice value reaching 89.79%.

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.

A Methodology for Verifying Abdominal Aortic Aneurysm Wall Stress

An abdominal aortic aneurysm (AAA) is a permanent focal dilatation of the abdominal aorta of at least 1.5 times its normal diameter. Although the criterion of maximum diameter is still used in clinical practice to decide on a timely intervention, numerical studies have demonstrated the importance of other geometric factors. However, the major drawback of numerical studies is that they must be validated experimentally before clinical implementation. This work presents a new methodology to verify wall stress predicted from the numerical studies against the experimental testing. To this end, four AAA phantoms were manufactured using vacuum casting. The geometry of each phantom was subject to microcomputed tomography (μCT) scanning at zero and three other intraluminal pressures: 80, 100, and 120 mm Hg. A zero-pressure geometry algorithm was used to calculate the wall stress in the phantom, while the numerical wall stress was calculated with a finite-element analysis (FEA) solver based on the actual zero-pressure geometry subjected to 80, 100, and 120 mm Hg intraluminal pressure loading. Results demonstrate the moderate accuracy of this methodology with small relative differences in the average wall stress (1.14%). Additionally, the contribution of geometric factors to the wall stress distribution was statistically analyzed for the four phantoms. The results showed a significant correlation between wall thickness and mean curvature (MC) with wall stress.

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

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.

Patient-specific modelling of abdominal aortic aneurysms: The influence of wall thickness on predicted clinical outcomes

Rupture of abdominal aortic aneurysms (AAAs) is linked to aneurysm morphology. This study investigates the influence of patient-specific (PS) AAA wall thickness on predicted clinical outcomes. Eight patients under surveillance for AAAs were selected from the MA(3)RS clinical trial based on the complete absence of intraluminal thrombus. Two finite element (FE) models per patient were constructed; the first incorporated variable wall thickness from CT (PS_wall), and the second employed a 1.9mm uniform wall (Uni_wall). Mean PS wall thickness across all patients was 1.77±0.42mm. Peak wall stress (PWS) for PS_wall and Uni_wall models was 0.6761±0.3406N/mm(2) and 0.4905±0.0850N/mm(2), respectively. In 4 out of 8 patients the Uni_wall underestimated stress by as much as 55%; in the remaining cases it overestimated stress by up to 40%. Rupture risk more than doubled in 3 out of 8 patients when PS_wall was considered. Wall thickness influenced the location and magnitude of PWS as well as its correlation with curvature. Furthermore, the volume of the AAA under elevated stress increased significantly in AAAs with higher rupture risk indices. This highlights the sensitivity of standard rupture risk markers to the specific wall thickness strategy employed.

Biomechanical Rupture Risk Assessment: A Consistent and Objective Decision-Making Tool for Abdominal Aortic Aneurysm Patients

Abdominal aortic aneurysm (AAA) rupture is a local event in the aneurysm wall that naturally demands tools to assess the risk for local wall rupture. Consequently, global parameters like the maximum diameter and its expansion over time can only give very rough risk indications; therefore, they frequently fail to predict individual risk for AAA rupture. In contrast, the Biomechanical Rupture Risk Assessment (BRRA) method investigates the wall's risk for local rupture by quantitatively integrating many known AAA rupture risk factors like female sex, large relative expansion, intraluminal thrombus-related wall weakening, and high blood pressure. The BRRA method is almost 20 years old and has progressed considerably in recent years, it can now potentially enrich the diameter indication for AAA repair. The present paper reviews the current state of the BRRA method by summarizing its key underlying concepts (i.e., geometry modeling, biomechanical simulation, and result interpretation). Specifically, the validity of the underlying model assumptions is critically disused in relation to the intended simulation objective (i.e., a clinical AAA rupture risk assessment). Next, reported clinical BRRA validation studies are summarized, and their clinical relevance is reviewed. The BRRA method is a generic, biomechanics-based approach that provides several interfaces to incorporate information from different research disciplines. As an example, the final section of this review suggests integrating growth aspects to (potentially) further improve BRRA sensitivity and specificity. Despite the fact that no prospective validation studies are reported, a significant and still growing body of validation evidence suggests integrating the BRRA method into the clinical decision-making process (i.e., enriching diameter-based decision-making in AAA patient treatment).

Association of Intraluminal Thrombus, Hemodynamic Forces, and Abdominal Aortic Aneurysm Expansion Using Longitudinal CT Images

While hemodynamic forces and intraluminal thrombus (ILT) are believed to play important roles on abdominal aortic aneurysm (AAA), it has been suggested that hemodynamic forces and ILT also interact with each other, making it a complex problem. There is, however, a pressing need to understand relationships among three factors: hemodynamics, ILT accumulation, and AAA expansion for AAA prognosis. Hence this study used longitudinal computer tomography scans from 14 patients and analyzed the relationship between them. Hemodynamic forces, represented by wall shear stress (WSS), were obtained from computational fluid dynamics; ILT accumulation was described by ILT thickness distribution changes between consecutives scans, and ILT accumulation and AAA expansion rates were estimated from changes in ILT and AAA volume. Results showed that, while low WSS was observed at regions where ILT accumulated, the rate at which ILT accumulated occurred at the same rate as the aneurysm expansion. Comparison between AAAs with and without thrombus showed that aneurysm with ILT recorded lower values of WSS and higher values of AAA expansion than those without thrombus. Findings suggest that low WSS may promote ILT accumulation and submit the idea that by increasing WSS levels ILT accumulation may be prevented.

A finite element updating approach for identification of the anisotropic hyperelastic properties of normal and diseased aortic walls from 4D ultrasound strain imaging

Computational analysis of the biomechanics of the vascular system aims at a better understanding of its physiology and pathophysiology and eventually at diagnostic clinical use. Because of great inter-individual variations, such computational models have to be patient-specific with regard to geometry, material properties and applied loads and boundary conditions. Full-field measurements of heterogeneous displacement or strain fields can be used to improve the reliability of parameter identification based on a reduced number of observed load cases as is usually given in an in vivo setting. Time resolved 3D ultrasound combined with speckle tracking (4D US) is an imaging technique that provides full field information of heterogeneous aortic wall strain distributions in vivo. In a numerical verification experiment, we have shown the feasibility of identifying nonlinear and orthotropic constitutive behaviour based on the observation of just two load cases, even though the load free geometry is unknown, if heterogeneous strain fields are available. Only clinically available 4D US measurements of wall motion and diastolic and systolic blood pressure are required as input for the inverse FE updating approach. Application of the developed inverse approach to 4D US data sets of three aortic wall segments from volunteers of different age and pathology resulted in the reproducible identification of three distinct and (patho-) physiologically reasonable constitutive behaviours. The use of patient-individual material properties in biomechanical modelling of AAAs is a step towards more personalized rupture risk assessment.

Automated Delineation of Vessel Wall and Thrombus Boundaries of Abdominal Aortic Aneurysms Using Multispectral MR Images

A correct patient-specific identification of the abdominal aortic aneurysm is useful for both diagnosis and treatment stages, as it locates the disease and represents its geometry. The actual thickness and shape of the arterial wall and the intraluminal thrombus are of great importance when predicting the rupture of the abdominal aortic aneurysms. The authors describe a novel method for delineating both the internal and external contours of the aortic wall, which allows distinguishing between vessel wall and intraluminal thrombus. The method is based on active shape model and texture statistical information. The method was validated with eight MR patient studies. There was high correspondence between automatic and manual measurements for the vessel wall area. Resulting segmented images presented a mean Dice coefficient with respect to manual segmentations of 0.88 and a mean modified Hausdorff distance of 1.14 mm for the internal face and 0.86 and 1.33 mm for the external face of the arterial wall. Preliminary results of the segmentation show high correspondence between automatic and manual measurements for the vessel wall and thrombus areas. However, since the dataset is small the conclusions cannot be generalized.

Effect of Intraluminal Thrombus Asymmetrical Deposition on Abdominal Aortic Aneurysm Growth Rate

Purpose: To determine the relationship between asymmetrical intraluminal thrombus (ILT) deposition in abdominal aortic aneurysm (AAA) and growth rate and to explore its biomechanical perspective. Methods: Thirty-four patients with AAA underwent at least 2 computed tomography scans during surveillance. The volumes of the AAA (VAAA) and thrombus (VILT) and the maximum thrombus thickness (ILTthick) were computed. Thrombus distribution was evaluated by introducing the asymmetrical thrombus deposition index (ATDI), with positive and negative values (–1<ATDI<1) associated with anterior and posterior ILT deposition, respectively. Finite element analysis was applied to estimate wall stress. Aneurysms were divided into high and low growth rate groups based on the cohort’s median growth rate, and the abovementioned parameters were compared between groups. Results: Most AAAs had asymmetrical anterior thrombus deposition. The high and low growth rate groups did not present significant differences in maximum diameter, VAAA, VILT, or maximum ILTthick. However, the high growth rate group had significantly higher ATDI (p=0.02). The ATDI<0 group (posterior ILT distribution) presented a significantly lower median growth rate compared to that of ATDI≥0 group (anterior or symmetrical ILT deposition; p=0.029). The specificity of an ATDI<0 criterion for identifying AAAs with a growth rate below the cohort median was 89%. The ATDI<0 group had a significantly lower posterior maximum wall stress compared with that of the ATDI≥0 group (p=0.03). Overall peak wall stress did not differ between groups. Conclusion: Posterior thrombus deposition in AAAs is associated with significantly lower growth rate and lower posterior maximum wall stress compared with that of AAAs with anterior thrombus deposition and could potentially indicate a lower rupture risk.

An approach for patient-specific multi-domain vascular mesh generation featuring spatially varying wall thickness modeling

In this work, we present a computationally efficient image-derived volume mesh generation approach for vasculatures that implements spatially varying patient-specific wall thickness with a novel inward extrusion of the wall surface mesh. Multi-domain vascular meshes with arbitrary numbers, locations, and patterns of both iliac bifurcations and thrombi can be obtained without the need to specify features or landmark points as input. In addition, the mesh output is coordinate-frame independent and independent of the image grid resolution with high dimensional accuracy and mesh quality, devoid of errors typically found in off-the-shelf image-based model generation workflows. The absence of deformable template models or Cartesian grid-based methods enables the present approach to be sufficiently robust to handle aneurysmatic geometries with highly irregular shapes, arterial branches nearly parallel to the image plane, and variable wall thickness. The assessment of the methodology was based on i) estimation of the surface reconstruction accuracy, ii) validation of the output mesh using an aneurysm phantom, and iii) benchmarking the volume mesh quality against other frameworks. For the phantom image dataset (pixel size 0.105 mm; slice spacing 0.7 mm; and mean wall thickness 1.401±0.120 mm), the average wall thickness in the mesh was 1.459±0.123 mm. The absolute error in average wall thickness was 0.060±0.036 mm, or about 8.6% of the largest image grid spacing (0.7 mm) and 4.36% of the actual mean wall thickness. Mesh quality metrics and the ability to reproduce regional variations of wall thickness were found superior to similar alternative frameworks.

Local Quantification of Wall Thickness and Intraluminal Thrombus Offer Insight into the Mechanical Properties of the Aneurysmal Aorta

Wall stress is a powerful tool to assist clinical decisions in rupture risk assessment of abdominal aortic aneurysms. Key modeling assumptions that influence wall stress magnitude and distribution are the inclusion or exclusion of the intraluminal thrombus in the model and the assumption of a uniform wall thickness. We employed a combined numerical-experimental approach to test the hypothesis that abdominal aortic aneurysm (AAA) wall tissues with different thickness as well as wall tissues covered by different thrombus thickness, exhibit differences in the mechanical behavior. Ultimate tissue strength was measured from in vitro tensile testing of AAA specimens and material properties of the wall were estimated by fitting the results of the tensile tests to a histo-mechanical constitutive model. Results showed a decrease in tissue strength and collagen stiffness with increasing wall thickness, supporting the hypothesis of wall thickening being mediated by accumulation of non load-bearing components. Additionally, an increase in thrombus deposition resulted in a reduction of elastin content, collagen stiffness and tissue strength. Local wall thickness and thrombus coverage may be used as surrogate measures of local mechanical properties of the tissue, and therefore, are possible candidates to improve the specificity of AAA wall stress and rupture risk evaluations.

Progression of abdominal aortic aneurysm towards rupture: refining clinical risk assessment using a fully coupled fluid-structure interaction method

Rupture of abdominal aortic aneurysm (AAA) is associated with high mortality rates. Risk of rupture is multi-factorial involving AAA geometric configuration, vessel tortuosity, and the presence of intraluminal pathology. Fluid structure interaction (FSI) simulations were conducted in patient based computed tomography scans reconstructed geometries in order to monitor aneurysmal disease progression from normal aortas to non-ruptured and contained ruptured AAA (rAAA), and the AAA risk of rupture was assessed. Three groups of 8 subjects each were studied: 8 normal and 16 pathological (8 non-ruptured and 8 rAAA). The AAA anatomical structures segmented included the blood lumen, intraluminal thrombus (ILT), vessel wall, and embedded calcifications. The vessel wall was described with anisotropic material model that was matched to experimental measurements of AAA tissue specimens. A statistical model for estimating the local wall strength distribution was employed to generate a map of a rupture potential index (RPI), representing the ratio between the local stress and local strength distribution. The FSI simulations followed a clear trend of increasing wall stresses from normal to pathological cases. The maximal stresses were observed in the areas where the ILT was not present, indicating a potential protective effect of the ILT. Statistically significant differences were observed between the peak systolic stress and the peak stress at the mean arterial pressure between the three groups. For the ruptured aneurysms, where the geometry of intact aneurysm was reconstructed, results of the FSI simulations clearly depicted maximum wall stress at the a priori known location of rupture. The RPI mapping indicated several distinct regions of high RPI coinciding with the actual location of rupture. The FSI methodology demonstrates that the aneurysmal disease can be described by numerical simulations, as indicated by a clear trend of increasing aortic wall stresses in the studied groups, (normal aortas, AAAs and rAAAs). Ultimately, the results demonstrate that FSI wall stress mapping and RPI can be used as a tool for predicting the potential rupture of an AAA by predicting the actual rupture location, complementing current clinical practice by offering a predictive diagnostic tool for deciding whether to intervene surgically or spare the patient from an unnecessary risky operation.

Abdominal aortic aneurysm: from clinical imaging to realistic replicas

The goal of this work is to develop a framework for manufacturing nonuniform wall thickness replicas of abdominal aortic aneurysms (AAAs). The methodology was based on the use of computed tomography (CT) images for virtual modeling, additive manufacturing for the initial physical replica, and a vacuum casting process and range of polyurethane resins for the final rubberlike phantom. The average wall thickness of the resulting AAA phantom was compared with the average thickness of the corresponding patient-specific virtual model, obtaining an average dimensional mismatch of 180 μm (11.14%). The material characterization of the artery was determined from uniaxial tensile tests as various combinations of polyurethane resins were chosen due to their similarity with ex vivo AAA mechanical behavior in the physiological stress configuration. The proposed methodology yields AAA phantoms with nonuniform wall thickness using a fast and low-cost process. These replicas may be used in benchtop experiments to validate deformations obtained with numerical simulations using finite element analysis, or to validate optical methods developed to image ex vivo arterial deformations during pressure-inflation testing.

Improving the efficiency of abdominal aortic aneurysm wall stress computations

An abdominal aortic aneurysm is a pathological dilation of the abdominal aorta, which carries a high mortality rate if ruptured. The most commonly used surrogate marker of rupture risk is the maximal transverse diameter of the aneurysm. More recent studies suggest that wall stress from models of patient-specific aneurysm geometries extracted, for instance, from computed tomography images may be a more accurate predictor of rupture risk and an important factor in AAA size progression. However, quantification of wall stress is typically computationally intensive and time-consuming, mainly due to the nonlinear mechanical behavior of the abdominal aortic aneurysm walls. These difficulties have limited the potential of computational models in clinical practice. To facilitate computation of wall stresses, we propose to use a linear approach that ensures equilibrium of wall stresses in the aneurysms. This proposed linear model approach is easy to implement and eliminates the burden of nonlinear computations. To assess the accuracy of our proposed approach to compute wall stresses, results from idealized and patient-specific model simulations were compared to those obtained using conventional approaches and to those of a hypothetical, reference abdominal aortic aneurysm model. For the reference model, wall mechanical properties and the initial unloaded and unstressed configuration were assumed to be known, and the resulting wall stresses were used as reference for comparison. Our proposed linear approach accurately approximates wall stresses for varying model geometries and wall material properties. Our findings suggest that the proposed linear approach could be used as an effective, efficient, easy-to-use clinical tool to estimate patient-specific wall stresses.

Validation of semiautomated and locally resolved aortic wall thickness measurements from computed tomography

OBJECTIVE

Aortic wall thickness (AWT) is important for anatomic description and biomechanical modeling of aneurysmal disease. However, no validated, noninvasive method for measuring AWT exists. We hypothesized that semiautomated image segmentation algorithms applied to computed tomography angiography (CTA) can accurately measure AWT.

METHODS

Aortic samples from 10 patients undergoing open thoracoabdominal aneurysm repair were taken from sites of the proximal or distal anastomosis, or both, yielding 13 samples. Aortic specimens were fixed in formalin, embedded in paraffin, and sectioned. After staining with hematoxylin and eosin and Masson's trichrome, sections were digitally scanned and measured. Patients' preoperative CTA Digital Imaging and Communications in Medicine (DICOM; National Electrical Manufacturers Association, Rosslyn, Va) images were segmented into luminal, inner arterial, and outer arterial surfaces with custom algorithms using active contours, isoline contour detection, and texture analysis. AWT values derived from image data were compared with measurements of corresponding pathologic specimens.

RESULTS

AWT determined by CTA averaged 2.33 ± 0.66 mm (range, 1.52-3.55 mm), and the AWT of pathologic specimens averaged 2.36 ± 0.75 mm (range, 1.51-4.16 mm). The percentage difference between pathologic specimens and CTA-determined AWT was 9.5% ± 4.1% (range, 1.8%-16.7%). The correlation between image-based measurements and pathologic measurements was high (R = 0.935). The 95% limits of agreement computed by Bland-Altman analysis fell within the range of -0.42 and 0.42 mm.

CONCLUSIONS

Semiautomated analysis of CTA images can be used to accurately measure regional and patient-specific AWT, as validated using pathologic ex vivo human aortic specimens. Descriptions and reconstructions of aortic aneurysms that incorporate locally resolved wall thickness are feasible and may improve future attempts at biomechanical analyses.

Fluid-structure interaction modeling of abdominal aortic aneurysms: the impact of patient-specific inflow conditions and fluid/solid coupling

Rupture risk assessment of abdominal aortic aneurysms (AAA) by means of biomechanical analysis is a viable alternative to the traditional clinical practice of using a critical diameter for recommending elective repair. However, an accurate prediction of biomechanical parameters, such as mechanical stress, strain, and shear stress, is possible if the AAA models and boundary conditions are truly patient specific. In this work, we present a complete fluid-structure interaction (FSI) framework for patient-specific AAA passive mechanics assessment that utilizes individualized inflow and outflow boundary conditions. The purpose of the study is two-fold: (1) to develop a novel semiautomated methodology that derives velocity components from phase-contrast magnetic resonance images (PC-MRI) in the infrarenal aorta and successfully apply it as an inflow boundary condition for a patient-specific fully coupled FSI analysis and (2) to apply a one-way-coupled FSI analysis and test its efficiency compared to transient computational solid stress and fully coupled FSI analyses for the estimation of AAA biomechanical parameters. For a fully coupled FSI simulation, our results indicate that an inlet velocity profile modeled with three patient-specific velocity components and a velocity profile modeled with only the axial velocity component yield nearly identical maximum principal stress (σ1), maximum principal strain (ε1), and wall shear stress (WSS) distributions. An inlet Womersley velocity profile leads to a 5% difference in peak σ1, 3% in peak ε1, and 14% in peak WSS compared to the three-component inlet velocity profile in the fully coupled FSI analysis. The peak wall stress and strain were found to be in phase with the systolic inlet flow rate, therefore indicating the necessity to capture the patient-specific hemodynamics by means of FSI modeling. The proposed one-way-coupled FSI approach showed potential for reasonably accurate biomechanical assessment with less computational effort, leading to differences in peak σ1, ε1, and WSS of 14%, 4%, and 18%, respectively, compared to the axial component inlet velocity profile in the fully coupled FSI analysis. The transient computational solid stress approach yielded significantly higher differences in these parameters and is not recommended for accurate assessment of AAA wall passive mechanics. This work demonstrates the influence of the flow dynamics resulting from patient-specific inflow boundary conditions on AAA biomechanical assessment and describes methods to evaluate it through fully coupled and one-way-coupled fluid-structure interaction analysis.

The importance of patient-specific regionally varying wall thickness in abdominal aortic aneurysm biomechanics

Abdominal aortic aneurysm (AAA) is a vascular condition where the use of a biomechanics-based assessment for patient-specific risk assessment is a promising approach for clinical management of the disease. Among various factors that affect such assessment, AAA wall thickness is expected to be an important factor. However, regionally varying patient-specific wall thickness has not been incorporated as a modeling feature in AAA biomechanics. To the best our knowledge, the present work is the first to incorporate patient-specific variable wall thickness without an underlying empirical assumption on its distribution for AAA wall mechanics estimation. In this work, we present a novel method for incorporating regionally varying wall thickness (the "PSNUT" modeling strategy) in AAA finite element modeling and the application of this method to a diameter-matched cohort of 28 AAA geometries to assess differences in wall mechanics originating from the conventional assumption of a uniform wall thickness. For the latter, we used both a literature-derived population average wall thickness (1.5 mm; the "UT" strategy) as well as the spatial average of our patient-specific variable wall thickness (the "PSUT" strategy). For the three different wall thickness modeling strategies, wall mechanics were assessed by four biomechanical parameters: the spatial maxima of the first principal stress, strain, strain-energy density, and displacement. A statistical analysis was performed to address the hypothesis that the use of any uniform wall thickness model resulted in significantly different biomechanical parameters compared to a patient-specific regionally varying wall thickness model. Statistically significant differences were obtained with the UT modeling strategy compared to the PSNUT strategy for the spatial maxima of the first principal stress (p = 0.002), strain (p = 0.0005), and strain-energy density (p = 7.83 e-5) but not for displacement (p = 0.773). Likewise, significant differences were obtained comparing the PSUT modeling strategy with the PSNUT strategy for the spatial maxima of the first principal stress (p = 9.68 e-7), strain (p = 1.03 e-8), strain-energy density (p = 9.94 e-8), and displacement (p = 0.0059). No significant differences were obtained comparing the UT and PSUT strategies for the spatial maxima of the first principal stress (p = 0.285), strain (p = 0.152), strain-energy density (p = 0.222), and displacement (p = 0.0981). This work strongly recommends the use of patient-specific regionally varying wall thickness derived from the segmentation of abdominal computed tomography (CT) scans if the AAA finite element analysis is focused on estimating peak biomechanical parameters, such as stress, strain, and strain-energy density.

Review: the role of biomechanical modeling in the rupture risk assessment for abdominal aortic aneurysms

AAA disease is a serious condition and a multidisciplinary approach including biomechanics is needed to better understand and more effectively treat this disease. A rupture risk assessment is central to the management of AAA patients, and biomechanical simulation is a powerful tool to assist clinical decisions. Central to such a simulation approach is a need for robust and physiologically relevant models. Vascular tissue senses and responds actively to changes in its mechanical environment, a crucial tissue property that might also improve the biomechanical AAA rupture risk assessment. Specifically, constitutive modeling should not only focus on the (passive) interaction of structural components within the vascular wall, but also how cells dynamically maintain such a structure. In this article, after specifying the objectives of an AAA rupture risk assessment, the histology and mechanical properties of AAA tissue, with emphasis on the wall, are reviewed. Then a histomechanical constitutive description of the AAA wall is introduced that specifically accounts for collagen turnover. A test case simulation clearly emphasizes the need for constitutive descriptions that remodels with respect to the mechanical loading state. Finally, remarks regarding modeling of realistic clinical problems and possible future trends conclude the article.

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.