On growth measurements of abdominal aortic aneurysms using maximally inscribed spheres

Computational investigation of outflow graft variation impact on hemocompatibility profile in LVADs

BACKGROUND

Hemocompatibility-related adverse events (HRAE) occur commonly in patients with left ventricular assist devices (LVADs) and add to morbidity and mortality. It is unclear whether the outflow graft orientation can impact flow conditions leading to HRAE. This study presents a simulation-based approach using exact patient anatomy from medical images to investigate the influence of outflow cannula orientation in modulating flow conditions leading to HRAEs.

METHODS

A 3D model of a proximal aorta and outflow graft was reconstructed from a computed tomography (CT) scan of an LVAD patient and virtually modified to model multiple cannula orientations (n = 10) by varying polar (cranio-caudal) (n = 5) and off-set (anterior-posterior) (n = 2) angles. Time-dependent computational flow simulations were then performed for each anatomical orientation. Qualitative and quantitative hemodynamics metrics of thrombogenicity including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), endothelial cell platelet activation potential (ECAP), particle residence time (PRT), and platelet activation potential (PLAP) were analyzed.

RESULTS

Within the simulations performed, endothelial cell activation potential (ECAP) and particle residence time (PRT) were found to be lowest with a polar angle of 85°, regardless of offset angle. However, polar angles that produced parameters at levels least associated with thrombosis varied when the offset angle was changed from 0° to 12°. For offset angles of 0° and 12° respectively, flow shear was lowest at 65° and 75°, time averaged wall shear stress (TAWSS) was highest at 85° and 35°, and platelet activation potential (PLAP) was lowest at 65° and 45°.

CONCLUSION

This study suggests that computational fluid dynamic modeling based on patient-specific anatomy can be a powerful analytical tool when identifying optimal positioning of an LVAD. Contrary to previous work, our findings suggest that there may be an "ideal" outflow cannula for each individual patient based on a CFD-based hemocompatibility profile.

Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate

OBJECTIVE

ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict the ascending aortic aneurysm growth.

MATERIAL AND METHODS

70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified.

RESULTS

the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth.

CONCLUSION

global shape features might provide an important contribution for predicting the aneurysm growth.

The study on the impact of AAA wall motion on the hemodynamics based on 4D CT image data

Purpose: To analyze the effect of the physiological deformation of the vessel wall on the hemodynamics in the abdominal aortic aneurysm (AAA), this paper compared the hemodynamics in AAA based on the moving boundary (MB) simulation and the rigid wall (RW) simulation.

Method: Patient-specific models were reconstructed to generate mesh based on four-dimensional computed tomography angiography (4D CT) data. The dynamic mesh technique was used to achieve deformation of the vessel wall, surface mesh and volume mesh of the fluid domain were successively remeshed at each time step. Besides, another rigid wall simulation was performed. Hemodynamics obtained from these two simulations were compared.

Results: Flow field and wall shear stress (WSS) distribution are similar. When using the moving boundary method (MBM), mean time-averaged wall shear stress (TAWSS) is lower, mean oscillatory shear index (OSI) and mean relative residence time (RRT) are higher. When using the 10th and 20th percentile values for TAWSS and 80th and 90th percentile values for RRT, the ratios of areas with low TAWSS, high OSI and high RRT to the entire vessel wall are higher than those assuming the vessel as rigid. In addition, one overlapping region of low TAWSS, high OSI and high RRT by using the MBM is consistent with the location of thrombus obtained from the follow-up imaging data.

Conclusion: The hemodynamics results by using the MBM reflect a higher blood retention effect. This paper presents a potential tool to assess the risk of intraluminal thrombus (ILT) formation based on the MBM.

Assessment of shape-based features ability to predict the ascending aortic aneurysm growth

The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.

Deep Learning on Multiphysical Features and Hemodynamic Modeling for Abdominal Aortic Aneurysm Growth Prediction

Prediction of abdominal aortic aneurysm (AAA) growth is of essential importance for the early treatment and surgical intervention of AAA. Capturing key features of vascular growth, such as blood flow and intraluminal thrombus (ILT) accumulation play a crucial role in uncovering the intricated mechanism of vascular adaptation, which can ultimately enhance AAA growth prediction capabilities. However, local correlations between hemodynamic metrics, biological and morphological characteristics, and AAA growth rates present high inter-patient variability that results in that the temporal-spatial biochemical and mechanical processes are still not fully understood. Hence, this study aims to integrate the physics-based knowledge with deep learning with a patch-based convolutional neural network (CNN) approach by incorporating important multiphysical features relating to its pathogenesis for validating its impact on AAA growth prediction. For this task, we observe that the unstructured multiphysical features cannot be directly employed in the kernel-based CNN. To tackle this issue, we propose a parameterization of features to leverage the spatio-temporal relations between multiphysical features. The proposed architecture was tested on different combinations of four features including radius, intraluminal thrombus thickness, time-average wall shear stress, and growth rate from 54 patients with 5-fold cross-validation with two metrics, a root mean squared error (RMSE) and relative error (RE). We conduct extensive experiments on AAA patients, the results show the effect of leveraging multiphysical features and demonstrate the superiority of the presented architecture to previous state-of-the-art methods in AAA growth prediction.

A Finite Element Based Analysis of a Hemodynamics Efficient Flow Stent Suitable for Different Abdominal Aneurysm Shapes

This research aimed to examine the impact of a proposed flow stent (PFS) on different abdominal artery shapes. For that purpose, a finite element-based model using the computational fluid dynamics (CFD) method is developed. The effect of PFS intervention on the hemodynamic efficiency is estimated by all of the significant criteria used for the evaluation of aneurysm occlusion and possible rupture; the flow velocity, pressure, wall shear stress (WSS), and WSS-related indices. Results showed that PFS intervention preserves the effects of high flow rate and decreases irregular flow recirculation in the sac of the aneurysm. The flow velocity decreases inside the aneurysm sac in the range of 55% to 80%. The time-averaged wall shear stress (TAWSS) was reduced from 42% to 53% by FPS deployment. The simulation results implies that PFS could heal an aneurysm efficiently with a mechanism that causes the development of thrombus and ultimately leads to aneurysm resorption.

Association of vortical structures and hemodynamic parameters for regional thrombus accumulation in abdominal aortic aneurysms

The intraluminal thrombus (ILT) has been shown to negatively impact the progression of the abdominal aortic aneurysms (AAAs). The formation of this thrombus layer has been connected to the local flow environment within AAAs, but the specific mechanisms leading to thrombus formation are still not fully understood. Our study investigated the association between vortical structures, near-wall hemodynamic metrics (e.g., time averaged wall shear stress (TAWSS) and oscillatory shear index (OSI)), and ILT accumulation in a longitudinal cohort of 14 AAAs (53 scans total). Vortices and hemodynamic parameters were estimated using hemodynamic simulations performed to each scan of each patient and compared to local 3D changes of ILT thickness between two consecutive scans (ΔILT). Results showed that vortices formed and remained strong and close to the lumen surface in AAAs without an ILT, while in AAAs with ILTs these detached from the lumen surface and dissipated nearby wall region where an increase in ILT thickness was observed. Although low TAWSS was observed in regions with and without ILT accumulation, an inverse correlation between ∆ILT and TAWSS was observed within the regions that experienced a thrombus growth. Our results support the idea that vortical structures might be playing a role modulating ILT accumulation into specific wall regions. Also, it submits the idea that the low TAWSS will be modulating the growth of thrombus within these preferred ILT accumulated regions.

Experimental aortic aneurysm severity and growth depend on topical elastase concentration and lysyl oxidase inhibition

Abdominal aortic aneurysm (AAA) formation and expansion is highly complex and multifactorial, and the improvement of animal models is an important step to enhance our understanding of AAA pathophysiology. In this study, we explore our ability to influence aneurysm growth in a topical elastase plus β-Aminopropionitrile (BAPN) mouse model by varying elastase concentration and by altering the cross-linking capability of the tissue. To do so, we assess both chronic and acute effects of elastase concentration using volumetric ultrasound. Our results suggest that the applied elastase concentration affects initial elastin degradation, as well as long-term vessel expansion. Additionally, we assessed the effects of BAPN by (1) removing it to restore the cross-linking capability of tissue after aneurysm formation and (2) adding it to animals with stable aneurysms to interrupt cross-linking. These results demonstrate that, even after aneurysm formation, lysyl oxidase inhibition remains necessary for continued expansion. Removing BAPN reduces the aneurysm growth rate to near zero, resulting in a stable aneurysm. In contrast, adding BAPN causes a stable aneurysm to expand. Altogether, these results demonstrate the ability of elastase concentration and BAPN to modulate aneurysm growth rate and severity. The findings open several new areas of investigation in a murine model that mimics many aspects of human AAA.

Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms

It remains difficult to predict when which patients with abdominal aortic aneurysm (AAA) will require surgery. The aim was to study the accuracy of geometric and biomechanical analysis of small AAAs to predict reaching the threshold for surgery, diameter growth rate and rupture or symptomatic aneurysm. 189 patients with AAAs of diameters 40–50 mm were included, 161 had undergone two CTAs. Geometric and biomechanical variables were used in prediction modelling. Classifications were evaluated with area under receiver operating characteristic curve (AUC) and regressions with correlation between observed and predicted growth rates. Compared with the baseline clinical diameter, geometric-biomechanical analysis improved prediction of reaching surgical threshold within four years (AUC 0.80 vs 0.85, p = 0.031) and prediction of diameter growth rate (r = 0.17 vs r = 0.38, p = 0.0031), mainly due to the addition of semiautomatic diameter measurements. There was a trend towards increased precision of volume growth rate prediction (r = 0.37 vs r = 0.45, p = 0.081). Lumen diameter and biomechanical indices were the only variables that could predict future rupture or symptomatic AAA (AUCs 0.65–0.67). Enhanced precision of diameter measurements improves the prediction of reaching the surgical threshold and diameter growth rate, while lumen diameter and biomechanical analysis predicts rupture or symptomatic AAA.

Defining a master curve of abdominal aortic aneurysm growth and its potential utility of clinical management

OBJECTIVE

The maximum diameter measurement of an abdominal aortic aneurysm (AAA), which depends on orthogonal and axial cross-sections or maximally inscribed spheres within the AAA, plays a significant role in the clinical decision making process. This study aims to build a total of 21 morphological parameters from longitudinal CT scans and analyze their correlations. Furthermore, this work explores the existence of a "master curve" of AAA growth, and tests which parameters serve to enhance its predictability for clinical use.

METHODS

106 CT scan images from 25 Korean AAA patients were retrospectively obtained. We subsequently computed morphological parameters, growth rates, and pair-wise correlations, and attempted to enhance the predictability of the growth for high-risk aneurysms using non-linear curve fitting and least-square minimization.

RESULTS

An exponential AAA growth model was fitted to the maximum spherical diameter, as the best representative of the growth among all parameters (r-square: 0.94) and correctly predicted to 15 of 16 validation scans based on a 95% confidence interval. AAA volume expansion rates were highly correlated (r=0.75) with thrombus accumulation rates.

CONCLUSIONS

The exponential growth model using spherical diameter provides useful information about progression of aneurysm size and enables AAA growth rate extrapolation during a given surveillance period.

Machine learning approaches to surrogate multifidelity Growth and Remodeling models for efficient abdominal aortic aneurysmal applications

Computational Growth and Remodeling (G&R) models have been widely used to capture the pathological development of arterial diseases and have shown promise for aiding clinical diagnosis, prognosis prediction, and staging classification. However, due to the high complexity of the arterial adaptation mechanism, high-fidelity arterial G&R simulation usually takes hours or even days, which hinders its application in clinical practice. To remedy this problem, we develop a computationally efficient arterial G&R simulation framework that comprehensively combines the physics-based G&R simulations and data-driven machine learning approaches. The proposed framework greatly enhances the computational efficiency of arterial G&R simulations, thereby enabling more time-consuming arterial applications, including personalized parameter estimation and arterial disease progression prediction. In particular, we achieve significant computational cost reduction mainly through two methods: (1) constructing a Multifidelity Surrogate (MFS) to approximate multifidelity G&R simulations by using a cokriging approach and (2) developing a novel iterative optimization algorithm for personalized parameter estimation. The proposed framework is demonstrated by estimating G&R model parameters and predicting individual aneurysm growth using follow-up CT images of Abdominal Aortic Aneurysms (AAAs) from 21 patients. Results show that the personalized parameters are satisfactorily estimated and the growth of AAAs is predicted within the clinically relevant time frame, i.e., less than 2 h, without a loss of accuracy.

Intraluminal thrombus effect on the progression of abdominal aortic aneurysms by using a multistate continuous-time Markov chain model

OBJECTIVE

To investigate the relationship between the characteristics of intraluminal thrombus (ILT) with abdominal aortic aneurysm (AAA) expansion.

METHODS

This retrospective clinical study applied homogeneous multistate continuous-time Markov chain models to longitudinal computed tomography (CT) data from Korean patients with AAA. Four AAA states were considered (early, mild, severe, fatal) and the maximal thickness of the ILT (max_ILT), the fraction of the wall area covered by the ILT (area_frac) and the fraction of ILT volume (vol_frac) were used as covariates.

RESULTS

The analysis reviewed longitudinal CT images from 26 patients. Based on likelihood-ratio statistics, the area_frac was the most significant biomarker and max_ILT was the second most significant. In addition, within AAAs that developed an ILT layer, the analysis found that the AAA expands relatively quickly during the early stage but the rate of expansion reduces once the AAA has reached a larger size.

CONCLUSION

The results recommend surgical intervention when a patient has an area_frac more than 60%. Although this recommendation should be considered with caution given the limited sample size, physicians can use the proposed model as a tool to find such recommendations with their own data.

Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

OBJECTIVE

For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.

METHODS

106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler.

RESULTS

The follow-up diameters were predicted satisfactorily (i.e. the true follow-up diameter was in the 95% prediction interval) for 79% of the scans using the population-based growth model, and 83% of the scans using the patient-specific growth model. Among the evaluated geometric measurements, centerline tortuosity was a significant (p = 0.0002) predictor of growth for AAAs with accelerated and stable expansion rates. Using the enhanced prediction model, 86% of follow-up scans were predicted satisfactorily. The average prediction errors of population-based, patient-specific, and enhanced models were ±2.67, ±2.61 and ± 2.79 mm, respectively.

CONCLUSION

A computational framework using patient-oriented growth models provides useful tools for per-patient basis treatment and enables better prediction of AAA growth.

Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration

Translating recent advances in abdominal aortic aneurysm (AAA) growth and remodeling (G&R) knowledge into a predictive, patient-specific clinical treatment tool requires a major paradigm shift in computational modeling. The objectives of this study are to develop a prediction framework that first calibrates the physical AAA G&R model using patient-specific serial computed tomography (CT) scan images, predicts the expansion of an AAA in the future, and quantifies the associated uncertainty in the prediction. We adopt a Bayesian calibration method to calibrate parameters in the G&R computational model and predict the magnitude of AAA expansion. The proposed Bayesian approach can take different sources of uncertainty; therefore, it is well suited to achieve our aims in predicting the AAA expansion process as well as in computing the propagated uncertainty. We demonstrate how to achieve the proposed aims by solving the formulated Bayesian calibration problems for cases with the synthetic G&R model output data and real medical patient-specific CT data. We compare and discuss the performance of predictions and computation time under different sampling cases of the model output data and patient data, both of which are simulated by the G&R computation. Furthermore, we apply our Bayesian calibration to real patient-specific serial CT data and validate our prediction. The accuracy and efficiency of the proposed method is promising, which appeals to computational and medical communities.

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.

Prediction of Abdominal Aortic Aneurysm Growth Using Dynamical Gaussian Process Implicit Surface

OBJECTIVE

We propose a novel approach to predict the Abdominal Aortic Aneurysm (AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way.

METHODS

We adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time.

RESULTS

A dataset of 7 subjects (4-7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach.

CONCLUSION

We introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion.

SIGNIFICANCE

The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs.

Image-based computational assessment of vascular wall mechanics and hemodynamics in pulmonary arterial hypertension patients

Pulmonary arterial hypertension (PAH) is a disease characterized by an elevated pulmonary arterial (PA) pressure. While several computational hemodynamic models of the pulmonary vasculature have been developed to understand PAH, they are lacking in some aspects, such as the vessel wall deformation and its lack of calibration against measurements in humans. Here, we describe a computational modeling framework that addresses these limitations. Specifically, computational models describing the coupling of hemodynamics and vessel wall mechanics in the pulmonary vasculature of a PAH patient and a normal subject were developed. Model parameters, consisting of linearized stiffness E of the large vessels and Windkessel parameters for each outflow branch, were calibrated against in vivo measurements of pressure, flow and vessel wall deformation obtained, respectively, from right-heart catheterization, phase-contrast and cine magnetic resonance images. Calibrated stiffness E of the proximal PA was 2.0 and 0.5 MPa for the PAH and normal models, respectively. Calibrated total compliance C_T and resistance R_T of the distal vessels were, respectively, 0.32 ml/mmHg and 11.3 mmHg∗min/l for the PAH model, and 2.93 ml/mmHg and 2.6 mmHg∗min/l for the normal model. These results were consistent with previous findings that the pulmonary vasculature is stiffer with more constricted distal vessels in PAH patients. Individual effects on PA pressure due to remodeling of the distal and proximal compartments of the pulmonary vasculature were also investigated in a sensitivity analysis. The analysis suggests that the remodeling of distal vasculature contributes more to the increase in PA pressure than the remodeling of proximal vasculature.

Local Diameter, Wall Stress, and Thrombus Thickness Influence the Local Growth of Abdominal Aortic Aneurysms

Purpose: To investigate the influence of the local diameter, the intraluminal thrombus (ILT) thickness, and wall stress on the local growth rate of abdominal aortic aneurysms. Methods: The infrarenal aortas of 90 asymptomatic abdominal aortic aneurysm (AAA) patients (mean age 70 years; 77 men) were retrospectively reconstructed from at least 2 computed tomography angiography scans (median follow-up of 1 year) and biomechanically analyzed with the finite element method. Each individual AAA model was automatically sliced orthogonally to the lumen centerline and represented by 100 cross sections with corresponding diameters, ILT thicknesses, and wall stresses. The data were grouped according to these parameters for comparison of differences among the variables. Results: Diameter growth was continuously distributed over the entire aneurysm sac, reaching absolute and relative median peaks of 3.06 mm/y and 7.3%/y, respectively. The local growth rate was dependent on the local baseline diameter, the local ILT thickness, and for wall segments not covered by ILT, also on the local wall stress level (all p<0.001). For wall segments that were covered by a thick ILT layer, wall stress did not affect the growth rate (p=0.08). Conclusion: Diameter is not only a strong global predictor but also a local predictor of aneurysm growth. In addition, and independent of the diameter, the ILT thickness and wall stress (for the ILT-free wall) also influence the local growth rate. The high stress sensitivity of nondilated aortic walls suggests that wall stress peaks could initiate AAA formation. In contrast, local diameters and ILT thicknesses determine AAA growth for dilated and ILT-covered aortic walls.

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.

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.