Patient-specific Abdominal Aortic Aneurysm Research Articles

On the Importance of Including Cohesive Zone Models in Modelling Mixed-Mode Aneurysm Rupture.

The precise mechanism of rupture in abdominal aortic aneurysms (AAAs) has not yet been uncovered. The phenomenological failure criterion of the coefficient of proportionality between von Mises stress and tissue strength does not account for any mechanistic foundation of tissue fracture. Experimental studies have shown that arterial failure is a stepwise process of fibrous delamination (mode II) and kinking (mode I) between layers. Such a mechanism has not previously been considered for AAA rupture. In the current study we consider both von Mises stress in the wall, in addition to interlayer tractions and delamination using cohesive zone models. Firstly, we present a parametric investigation of the influence of a range of AAA anatomical features on the likelihood of elevated interlayer traction and delamination. We observe in several cases that the location of peak von Mises stress and tangential traction coincide. Our simulations also reveal however, that peak von Mises and intramural tractions are not coincident for aneurysms with Length/Radius less than 2 (short high-curvature aneurysms) and for aneurysms with symmetric intraluminal thrombus (ILT). For an aneurysm with (L/R = 2.0), the peak moves slightly towards the origin while the peak is near the peak bulge with a separation distance of ~ 17mm. Additionally, we present three patient-specific AAA models derived directly from CT scans, which also illustrate that the location of von Mises stress does not correlate with the point of interlayer delamination. This study suggests that incorporating cohesive zone models into clinical based FE analyses may capture a greater proportion of ruptures in-silico.

Integrated particle image velocimetry and fluid–structure interaction analysis for patient-specific abdominal aortic aneurysm studies

BackgroundUnderstanding the hemodynamics of an abdominal aortic aneurysm (AAA) is crucial for risk assessment and treatment planning. This study introduces a low-cost, patient-specific in vitro AAA model to investigate hemodynamics using particle image velocimetry (PIV) and flow-simulating circuit, validated through fluid–structure interaction (FSI) simulations.MethodsIn this study, 3D printing was employed to manufacture a flexible patient-specific AAA phantom using a lost-core casting technique. A pulsatile flow circuit was constructed using off-the-shelf components. A particle image velocimetry (PIV) setup was built using an affordable laser source and global shutter camera, and finally, the flow field inside the AAA was analyzed using open-source software. Fluid–structure interaction (FSI) simulations were performed to enhance our understanding of the flow field, and the results were validated by PIV analysis. Both steady-state and transient flow conditions were investigated.ResultsOur experimental setup replicated physiological conditions, analyzing arterial wall deformations and flow characteristics within the aneurysm. Under constant flow, peak wall deformations and flow velocities showed deviations within − 12% to + 27% and − 7% to + 5%, respectively, compared to FSI simulations. Pulsatile flow conditions further demonstrated a strong correlation (Pearson coefficient 0.85) in flow velocities and vectors throughout the cardiac cycle. Transient phenomena, particularly the formation and progression of vortex structures during systole, were consistently depicted between experimental and numerical models.ConclusionsBy bridging high-fidelity experimental observations with comprehensive computational analyses, this study underscores the potential of integrated methodologies in enhancing our understanding of AAA pathophysiology. The convergence of realistic AAA phantoms, precise PIV measurements at affordable cost point, and validated FSI models heralds a new paradigm in vascular research, with significant implications for personalized medicine and bioengineering innovations.

Fabrication of deformable patient-specific AAA models by material casting techniques.

Abdominal Aortic Aneurysm (AAA) is a balloon-like dilatation that can be life-threatening if not treated. Fabricating patient-specific AAA models can be beneficial for in-vitro investigations of hemodynamics, as well as for pre-surgical planning and training, testing the effectiveness of different interventions, or developing new surgical procedures. The current direct additive manufacturing techniques cannot simultaneously ensure the flexibility and transparency of models required by some applications. Therefore, casting techniques are presented to overcome these limitations and make the manufactured models suitable for in-vitro hemodynamic investigations, such as particle image velocimetry (PIV) measurements or medical imaging. Two complex patient-specific AAA geometries were considered, and the related 3D models were fabricated through material casting. In particular, two casting approaches, i.e. lost molds and lost core casting, were investigated and tested to manufacture the deformable AAA models. The manufactured models were acquired by magnetic resonance, computed tomography (CT), ultrasound imaging, and PIV. In particular, CT scans were segmented to generate a volumetric reconstruction for each manufactured model that was compared to a reference model to assess the accuracy of the manufacturing process. Both lost molds and lost core casting techniques were successful in the manufacturing of the models. The lost molds casting allowed a high-level surface finish in the final 3D model. In this first case, the average signed distance between the manufactured model and the reference was () mm. However, this approach was more expensive and time-consuming. On the other hand, the lost core casting was more affordable and allowed the reuse of the external molds to fabricate multiple copies of the same AAA model. In this second case, the average signed distance between the manufactured model and the reference was () mm. However, the final model's surface finish quality was poorer compared to the model obtained by lost molds casting as the sealing of the outer molds was not as firm as the other casting technique. Both lost molds and lost core casting techniques can be used for manufacturing patient-specific deformable AAA models suitable for hemodynamic investigations, including medical imaging and PIV.

Intraluminal Thrombus Characteristics in AAA Patients: Non-Invasive Diagnosis Using CFD.

Abdominal aortic aneurysms (AAA) continue to pose a high mortality risk despite advances in medical imaging and surgery. Intraluminal thrombus (ILT) is detected in most AAAs and may critically impact their development. Therefore, understanding ILT deposition and growth is of practical importance. To assist in managing these patients, the scientific community has been researching the relationship between intraluminal thrombus (ILT) and hemodynamic parameters wall shear stress (WSS) derivatives. This study analyzed three patient-specific AAA models reconstructed from CT scans using computational fluid dynamics (CFD) simulations and a pulsatile non-Newtonian blood flow model. The co-localization and relationship between WSS-based hemodynamic parameters and ILT deposition were examined. The results show that ILT tends to occur in regions of low velocity and time-averaged WSS (TAWSS) and high oscillation shear index (OSI), endothelial cell activation potential (ECAP), and relative residence time (RRT) values. ILT deposition areas were found in regions of low TAWSS and high OSI independently of the nature of flow near the wall characterized by transversal WSS (TransWSS). A new approach is suggested which is based on the estimation of CFD-based WSS indices specifically in the thinnest and thickest ILT areas of AAA patients; this approach is promising and supports the effectiveness of CFD as a decision-making tool for clinicians. Further research with a larger patient cohort and follow-up data are needed to confirm these findings.

The Effect of Blood Rheology and Inlet Boundary Conditions on Realistic Abdominal Aortic Aneurysms under Pulsatile Flow Conditions.

The effects of non-Newtonian rheology and boundary conditions on various pathophysiologies have been studied quite extensively in the literature. The majority of results present qualitative and/or quantitative conclusions that are not thoroughly assessed from a statistical perspective. The finite volume method was employed for the numerical simulation of seven patient-specific abdominal aortic aneurysms. For each case, five rheological models and three inlet velocity boundary conditions were considered. Outlier- and heteroscedasticity-robust ANOVA tests assessed the simultaneous effect of rheological specifications and boundary conditions on fourteen variables that capture important characteristics of vascular flows. The selection of inlet velocity profiles appears as a more critical factor relative to rheological specifications, especially regarding differences in the oscillatory characteristics of computed flows. Response variables that relate to the average tangential force on the wall over the entire cycle do not differ significantly across alternative factor levels, as long as one focuses on non-Newtonian specifications. The two factors, namely blood rheological models and inlet velocity boundary condition, exert additive effects on variables that characterize vascular flows, with negligible interaction effects. Regarding thrombus-prone conditions, the Plug inlet profile offers an advantageous hemodynamic configuration with respect to the other two profiles.

The Impact of a Limited Field-of-View on Computed Hemodynamics in Abdominal Aortic Aneurysms: Evaluating the Feasibility of Completing Ultrasound Segmentations with Parametric Geometries

To improve abdominal aortic aneurysm (AAA) rupture risk assessment, a large, longitudinal study on AAA hemodynamics and biomechanics is necessary, using personalized fluid-structure interaction (FSI) modeling. 3-dimensional, time-resolved ultrasound (3D+t US) is the preferred image modality to obtain the patient-specific AAA geometry for such a study, since it is safe, affordable and provides temporal information. However, the 3D+t US field-of-view (FOV) is limited and therefore often fails to capture the inlet and aorto-iliac bifurcation geometry. In this study, a framework was developed to add parametric inlet and bifurcation geometries to the abdominal aortic aneurysm geometry by employing dataset statistics and parameters of the AAA geometry. The impact of replacing the patient-specific inlet and bifurcation geometries, acquired using computed tomography (CT) scans, by parametric geometries was evaluated by examining the differences in hemodynamics (systolic and time-averaged wall shear stress and oscillatory shear index) in the aneurysm region. The results show that the inlet geometry has a larger effect on the AAA hemodynamics (median differences of 7.5 to 18.8%) than the bifurcation geometry (median differences all below 1%). Therefore, it is not feasible to replace the patient-specific inlet geometry by a generic one. Future studies should investigate the possibilities of extending the proximal FOV of 3D+t US. However, this study did show the feasibility of adding a parametric bifurcation geometry to the aneurysm geometry. After extending the proximal FOV, the obtained framework can be used to extract AAA geometries from 3D+t US for FSI simulations, despite the absence of the bifurcation geometry.

Analyzing the contribution of vasa vasorum in oxygenation of the aneurysmal wall: A computational study

The mechanisms of abdominal aortic aneurysm (AAA) formation and rupture are controversial in the literature. While the intraluminal thrombus (ILT) plays a crucial role in reducing oxygen flux to the tissue and therefore decreasing the aortic wall strength, other physiological parameters such as the vasa vasorum (VV) oxygen flow and its consumption contribute to altered oxygenation responses of the arterial tissue as well. The goal of this research is to analyse the importance of the aforementioned parameters on oxygen delivery to the aneurysmal wall in a patient-specific AAA. Numerical simulations of coupled blood flow and mass transport with varying levels of VV concentration and oxygen reaction rate coefficient are performed. The hypoperfusion of the adventitial VV and high oxygen consumption are observed to have critical effects on reducing aneurysmal tissue oxygen supply and can therefore exacerbate localized oxygen deprivation.

Spatiotemporal Registration of 3-D Multi-perspective Ultrasound Images of Abdominal Aortic Aneurysms.

Methods for patient-specific abdominal aortic aneurysm (AAA) progression monitoring and rupture risk assessment are widely investigated. Three-dimensional ultrasound can visualize the AAA's complex geometry and displacement fields. However, ultrasound has a limited field of view and low frame rate (i.e., 3-8 Hz). This article describes an approach to enhance the temporal resolution and the field of view. First, the frame rate was increased for each data set by sequencing multiple blood pulse cycles into one cycle. The sequencing method uses the original frame rate and the estimated pulse wave rate obtained from AAA distension curves. Second, the temporal registration was applied to multi-perspective acquisitions of the same AAA. Third, the field of view was increased through spatial registration and fusion using an image feature-based phase-only correlation method and a wavelet transform, respectively. Temporal sequencing was fully correct in aortic phantoms and was successful in 51 of 62 AAA patients, yielding a factor 5 frame rate increase. Spatial registration of proximal and distal ultrasound acquisitions was successful in 32 of 37 different AAA patients, based on the comparison between the fused ultrasound and computed tomography segmentation (95th percentile Haussdorf distances and similarity indices of 4.2 ± 1.7 mm and 0.92 ± 0.02 mm, respectively). Furthermore, the field of view was enlarged by 9%-49%.

Analyzing the Effects of Multi-Layered Porous Intraluminal Thrombus on Oxygen Flow in Abdominal Aortic Aneurysms

Determination of abdominal aortic aneurysm (AAA) rupture risk involves the accurate prediction of mechanical stresses acting on the arterial tissue, as well as the wall strength which has a correlation with oxygen supply within the aneurysmal wall. Our laboratory has previously reported the significance of an intraluminal thrombus (ILT) presence and morphology on localized oxygen deprivation by assuming a uniform consistency of ILT. The aim of this work is to investigate the effects of ILT structural composition on oxygen flow by adopting a multilayered porous framework and comparing a two-layer ILT model with one-layer models. Three-dimensional idealized and patient-specific AAA geometries are generated. Numerical simulations of coupled fluid flow and oxygen transport between blood, arterial wall, and ILT are performed, and spatial variations of oxygen concentrations within the AAA are obtained. A parametric study is conducted, and ILT permeability and oxygen diffusivity parameters are individually varied within a physiological range. A gradient of permeability is also defined to represent the heterogenous structure of ILT. Results for oxygen measures as well as filtration velocities are obtained, and it is found that the presence of any ILT reduces and redistributes the concentrations in the aortic wall markedly. Moreover, it is found that the integration of a porous ILT significantly affects the oxygen transport in AAA and the concentrations are linked to ILT’s permeability values. Regardless of the ILT stratification, maximum variation in wall oxygen concentrations is higher in models with lower permeability, while the concentrations are not sensitive to the value of the diffusion coefficient. Based on the observations, we infer that average one-layer parameters for ILT material characteristics can be used to reasonably estimate the wall oxygen concentrations in aneurysm models.

Influence of the flow field and vortex structure of patient-specific abdominal aortic aneurysm with intraluminal thrombus on the arterial wall

ABSTRACT In this study, numerical simulations and experiments were conducted on patient-specific normal abdominal aorta and abdominal aortic aneurysm (AAA) with intraluminal thrombus (ILT) models, which were reconstructed from computed tomography angiography (CTA) images published online. A scale-adaptive simulation (SAS) of the abdominal aortic aneurysm with a posterior eccentric thrombus was performed and the reliability of the numerical simulation was verified by 2D particle image velocimetry (PIV) experimental results. The distribution of wall shear stress (WSS), oscillatory shear index (OSI), and relative residence time (RRT) of the normal abdominal aorta and AAA were compared and discussed systematically according to the numerical simulation results under rest and exercise conditions. Numerical results indicated that ILT deposition positions were accompanied by low WSS, high OSI, and high RRT regions and were characterized by small vortex groups, which was due to the fragmentation of large vortex structures. The distribution law of the AAA wall parameters has an obvious correlation with the nearby vortex structure formation, development, fragmentation and dissipation. It was also found that high TAWSS, low OSI, and low RRT regions present in the middle and lower aneurysm walls under exercise condition reduce the risk of ILT formation and development.

Image-Based Numerical Investigation in an Impending Abdominal Aneurysm Rupture

Blood flow dynamics plays a crucial role in the growth and rupture of abdominal aortic aneurysms. The aim of this study was to analyze the possibility of predicting aneurysmal rupture by numerical investigations based on diagnostic images. The blood flow dynamics was analyzed in a patient-specific abdominal aortic aneurysm, reconstructed from CT images of an aneurysm while it was rupturing. The patient-specific geometry was virtually repaired in order to obtain a non-ruptured model representative of the geometry immediately preceding the rupture. To reproduce physiological conditions, numerical simulations were performed under pulsatile flow conditions, and blood was modelled as a non-Newtonian fluid, using the Carreau rheological model. Hemodynamic parameters that influence the rupture of the aneurysm were investigated, and their possible association with vascular disease was discussed. The results of the numerical simulations indicated regions of slow recirculation and low values of Time Averaged Wall Shear Stress (TAWSS) in the region of rupture. Unlike literature results, a high Oscillatory Shear Index (OSI) was not clearly found in this region. Nevertheless, just in the region where the rupture will occur, high values of Endothelial Cell Activation Potential index (ECAP) were found. This index is therefore extremely significant for assessing the vulnerability of the aortic wall and locating the critical rupture region.

Prediction of wall stress and oxygen flow in patient-specific abdominal aortic aneurysms: the role of intraluminal thrombus.

In this study, the biomechanical role of intraluminal thrombus (ILT) in an abdominal aortic aneurysm (AAA) is investigated. The implications of ILT in AAA are controversial in literature. Previous studies have demonstrated that ILT provides a biomechanical advantage by decreasing wall stress, whereas other studies have associated ILT with inhibiting oxygen transport and inducing aortic wall weakening. Therefore, we sought to explore the connection between ILT, mechanical stresses, and oxygen flow in different geometries of patient-specific aneurysms with varying ILT morphologies. The objective is to investigate the extent to which ILT influences the prediction of aneurysmal wall stresses that are associated with rupture, as well as oxygen concentrations to measure tissue oxygen deprivation. Three patient-specific AAA geometries are considered, and two models, one with ILT and one without ILT, are created for each patient to assess the effect of ILT presence. A fluid-structure interaction approach is used to couple the blood flow, wall deformation, and oxygen mass transport. Results are presented for hemodynamics patterns, wall stress measures, and oxygen metrics within the arterial wall. While ILT is found to reduce wall stress, simulations confirm that ILT decreases oxygen transport within the tissue significantly, leading to wall hypoxia.

Numerical investigation of abdominal aortic aneurysm hemodynamics using the reduced unified continuum formulation for vascular fluid-structure interaction

We recently demonstrated the reduction of the unified continuum and variational multiscale formulation to a computationally efficient fluid-structure interaction (FSI) formulation via three modeling assumptions pertaining to the vascular wall. Similar to the coupled momentum method introduced by Figueroa et al., the resulting semi-discrete formulation yields a monolithically coupled FSI system posed in an Eulerian frame of reference with only a minor modification of the fluid boundary integral. To achieve uniform second-order temporal accuracy and user-controlled high-frequency algorithmic damping, we adopt the generalized-α method for uniform temporal discretization of the entire coupled system. In conjunction with a fully consistent, segregated predictor multi-corrector algorithm preserving the block structure of the incompressible Navier-Stokes equations in the implicit solver’s associated linear system, a three-level nested block preconditioner is adopted for improved representation of the Schur complement. In this work, we apply our reduced unified continuum formulation to an appropriately prestressed patient-specific abdominal aortic aneurysm and investigate the effects of varying spatial distributions of wall properties on hemodynamic and vascular wall quantities of interest.

Low-cost digital tool kit for planning and sizing with 3D printing of abdominal aortic aneurysms for endovascular aortic repair: A Latinoamerican experience

Introduction and objectives3D printing technology has recently been used to improve diagnosis and treatment of complex surgical cardiovascular diseases. This technology allows better appreciation of complex 3D anatomical and pathological conditions. The aim of the present study is to show a Latin American experience with a low-cost digital toolkit for planning and sizing with 3D printing of abdominal aortic aneurysms (AAA) for endovascular aortic repair (EVAR) and preoperative simulation, combining 2D and 3D technologies. MethodsProspective descriptive study. All patients that underwent EVAR between January and May 2021 were included. The EVAR procedures were planned using the low-cost digital tool kit with 3D printing. ResultsThe 3D printed models had fair transparency, absence of flexibility and no discrepancy between the computer-aided design. The models allowed external inspection but not the insertion of stent grafts due to the lack of lumen. The model had lower cost (25 USD per model), shorter manufacturing time (22h 31min), and an acceptable level of accuracy (2.61% error) compared to other methods. DiscussionUsing low-cost digital tool kits for planning and sizing with 3D printing, produces a robust physical model of a patient-specific AAA to aid endovascular aneurysm repair education and planning in a timely and cost-effective manner. ConclusionsOverall, with the levels of transparency and flexibility provided by the 3D printed part, the patient-specific AAA model could potentially be used for surgical planning for individual patients but not for surgeons practicing endovascular aneurysm repair stent graft.

Greedy maximin distance sampling based model order reduction of prestressed and parametrized abdominal aortic aneurysms

This work proposes a framework for projection-based model order reduction (MOR) of computational models aiming at a mechanical analysis of abdominal aortic aneurysms (AAAs). The underlying full-order model (FOM) is patient-specific, stationary and nonlinear. The quantities of interest are the von Mises stress and the von Mises strain field in the AAA wall, which result from loading the structure to the level of diastolic blood pressure at a fixed, imaged geometry (prestressing stage) and subsequent loading to the level of systolic blood pressure with associated deformation of the structure (deformation stage). Prestressing is performed with the modified updated Lagrangian formulation (MULF) approach. The proposed framework aims at a reduction of the computational cost in a many-query context resulting from model uncertainties in two material and one geometric parameter. We apply projection-based MOR to the MULF prestressing stage, which has not been presented to date. Additionally, we propose a reduced-order basis construction technique combining the concept of subspace angles and greedy maximin distance sampling. To further achieve computational speedup, the reduced-order model (ROM) is equipped with the energy-conserving mesh sampling and weighting hyper reduction method. Accuracy of the ROM is numerically tested in terms of the quantities of interest within given bounds of the parameter domain and performance of the proposed ROM in the many-query context is demonstrated by comparing ROM and FOM statistics built from Monte Carlo sampling for three different patient-specific AAAs.

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.

Statistical techniques for predicting rupture risk in abdominal aortic aneurysms: A contribution based on bootstrap.

The morphometry of abdominal aortic aneurysms (AAA) has been recognized as one of the main factors that may predispose them to rupture. The need to quantify the morphometry of AAA on a patient-specific basis constitutes a valuable tool for assisting in rupture risk prediction. Previous results of this research group have determined the correlations between hemodynamic stresses and aneurysm morphometry by means of the Pearson coefficient. The present work aims to find how the AAA morphology correlates with the hemodynamic stresses acting on the arterial wall. To do so, the potential of the bootstrap technique has been explored. Bootstrap works appropriately in applications where few data are available (13 patient-specific AAA models were simulated). The methodology developed can be considered a contribution to predicting the hemodynamic stresses from the size and shape indices. The present work explores the use of a specific statistical technique (the bootstrap technique) to predict, based on morphological correlations, the patient-specific aneurysm rupture risk, provide greater understanding of this complex phenomenon that can bring about improvements in the clinical management of aneurysmatic patients. The results obtained using the bootstrap technique have greater reliability and robustness than those obtained by regression analysis using the Pearson coefficient, thus allowing to obtain more reliable results from the characteristics of the samples used, such as their small size and high variability. Additionally, it could be an indicator that other indices, such as AAA length, deformation rate, saccular index, and asymmetry, are important.

A semi-implicit characteristic-based polynomial pressure projection for FEM to solve incompressible flows

PurposeThe purpose of this paper is to investigate a novel stabilization scheme to handle convection and pressure oscillation in the process of solving incompressible laminar flows by finite element method (FEM).Design/methodology/approachThe semi-implicit stabilization scheme, characteristic-based polynomial pressure projection (CBP3) consists of the Characteristic-Galerkin method and polynomial pressure projection. Theoretically, the proposed scheme works for any type of element using equal-order approximation for velocity and pressure. In this work, linear 3-node triangular and 4-node tetrahedral elements are the focus, which are the simplest but most difficult elements for pressure stabilizations.FindingsThe present paper proposes a new scheme, which can stabilize FEM solution for flows of both low and relatively high Reynolds numbers. And the influence of stabilization parameters of the CBP3 scheme has also been investigated.Research limitations/implicationsThe research in this work is limited to the laminar incompressible flow.Practical implicationsThe verification and validation of the CBP3 scheme are conducted by several 2 D and 3 D numerical examples. The scheme could be used to deal with more practical fluid problems.Social implicationsThe application of scheme to study complex hemodynamics of patient-specific abdominal aortic aneurysm is also presented, which demonstrates its potential to solve bio-flows.Originality/valueThe paper simulated 2 D and 3 D numerical examples with superior results compared to existing results and experiments. The novel CBP3 scheme is verified to be very effective in handling convection and pressure oscillation.

Drug delivery performance of nanocarriers based on adhesion and interaction for abdominal aortic aneurysm treatment

Targeted drug delivery using nanocarriers (NCs) is one of the novel techniques that has recently been used to improve drug delivery to the Abdominal aortic aneurysm (AAA) disease. The purpose of this study is to evaluate the surface density of NCs (SDNC) adhered via ligand-receptor binding to the inner wall of AAA. For this purpose, fluid–structure interaction (FSI) analysis was first performed for the patient-specific and ideal AAA models. Then, by injecting NCs into the aortic artery, the values of SDNC adhered to and interacted with AAA wall were obtained. Two types of NCs, liposomes, and solid particles in four different diameters, were used to investigate the effect of the diameter and the type of NCs on the drug delivery. Additionally, the effect of the number of the injected NCs to the artery on the values of SDNC adhered to and interacted with AAA wall was investigated. The simulation results showed that the interaction and adhesion values of SDNC for Liposome nanoparticles were higher than the ones for the solid particles. Furthermore, as the diameter of NCs increases, the values of SDNC adhered to AAA wall increase, but the values of SDNC interacted with the inner wall of AAA decrease. In the low number of inserted NCs in the artery (1000 NCs), the interaction and adhesion values of SDNC were very slight, and by increasing the number of NCs inserted into the artery, the drug delivery was improved. By examining different AAA models, it was found that the complexity of the shape of AAA has a minor effect on the pattern of increase or decrease of the values of SDNC adhered to and interacted with AAA wall.This study's findings can improve the understanding of NCs design and propose the appropriate amount of their injection into various AAA models.

Predictors of Abdominal Aortic Aneurysm Risks.

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