Morphological and Biomechanical Features in Abdominal Aortic Aneurysm with Long and Short Neck-Case-Control Study in 64 Abdominal Aortic Aneurysms.

BACKGROUND: Both, open and endovascular, procedures are related to higher complication rate in abdominal aortic aneurysm (AAA) with shorter neck. Previous study showed that long-neck AAA might have lower risk of rupture. Estimation of biomechanical forces in AAA improves rupture risk assessment. The aim of this study was to compare morphological features and biomechanical forces in the short- and long-neck AAA with threshold of 15 mm. METHODS: Digital Imaging and Communication in Medicine images of 64 aneurysms were prospectively collected and analyzed in a case-control study. Using commercially available software, Peak wall Stress (PWS) and Rupture Risk Equivalent Diameter (RRED) were determined. Difference between the maximal aneurysm diameter (MAD) and RRED was calculated and expressed as an absolute and relative (percentage of the MAD) value. In addition, volume of intraluminal thrombus (ILT) was calculated and expressed relative to AAA volume. RESULTS: Study included 64 AAA divided in group with long (36, 56.25%), and short (28, 43.75%) neck. There was no correlation between neck length and MAD, PWS, and RRED (P = 0.646, P = 0.421, and P = 0.405, respectively). Relative ILT volume was greater in the short-neck aneurysms (P = 0.033). Relative difference between RRED and MAD was -4% and -14.8% in short- and long-neck aneurysms, respectively (P = 0.029). The difference between RRED and MAD was positive in 14/28 patients (50%) with short neck and in 6/35 patients (17.14%) with long neck (P = 0.011). CONCLUSIONS: Based on our biomechanical analysis, in AAA with neck longer than 15 mm rupture risk might be lower than the risk estimated by its diameter. It might be explained with lower relative volume of ILT.

Machine Learning Approach for Predicting Wall Shear Distribution for Abdominal Aortic Aneurysm and Carotid Bifurcation Models.

Computer simulations based on the finite element method represent powerful tools for modeling blood flow through arteries. However, due to its computational complexity, this approach may be inappropriate when results are needed quickly. In order to reduce computational time, in this paper, we proposed an alternative machine learning based approach for calculation of wall shear stress (WSS) distribution, which may play an important role in mechanisms related to initiation and development of atherosclerosis. In order to capture relationships between geometric parameters, blood density, dynamic viscosity and velocity, and WSS distribution of geometrically parameterized abdominal aortic aneurysm (AAA) and carotid bifurcation models, we proposed multivariate linear regression, multilayer perceptron neural network and Gaussian conditional random fields (GCRF). Results obtained in this paper show that machine learning approaches can successfully predict WSS distribution at different cardiac cycle time points. Even though all proposed methods showed high potential for WSS prediction, GCRF achieved the highest coefficient of determination (0.930-0.948 for AAA model and 0.946-0.954 for carotid bifurcation model) demonstrating benefits of accounting for spatial correlation. The proposed approach can be used as an alternative method for real time calculation of WSS distribution.

Computational modeling of shear forces and experimental validation of endothelial cell responses in an orbital well shaker system.

Vascular endothelial cells are continuously exposed to hemodynamic shear stress. Intensity and type of shear stress are highly relevant to vascular physiology and pathology. Here, we modeled shear stress distribution in a tissue culture well (R = 17.5 mm, fill volume 2 ml) under orbital translation using computational fluid dynamics with the finite element method. Free surface distribution, wall shear stress, inclination angle, drag force, and oscillatory index on the bottom surface were modeled. Obtained results predict nonuniform shear stress distribution during cycle, with higher oscillatory shear index, higher drag force values, higher circular component, and larger inclination angle of the shear stress at the periphery of the well compared with the center of the well. The oscillatory index, inclination angle, and drag force are new quantitative parameters modeled in this system, which provide a better understanding of the hydrodynamic conditions experienced and reflect the pulsatile character of blood flow in vivo. Validation experiments revealed that endothelial cells at the well periphery aligned under flow and increased Kruppel-like Factor 4 (KLF-4), cyclooxygenase-2 (COX-2) expression and endothelial nitric oxide synthase (eNOS) phosphorylation. In contrast, endothelial cells at the center of the well did not show clear directional alignment, did not induce the expression of KLF-4 and COX-2 nor increased eNOS phosphorylation. In conclusion, this improved computational modeling predicts that the orbital shaker model generates different hydrodynamic conditions at the periphery versus the center of the well eliciting divergent endothelial cell responses. The possibility of generating different hydrodynamic conditions in the same well makes this model highly attractive to study responses of distinct regions of the same endothelial monolayer to different types of shear stresses thereby better reflecting in vivo conditions.

Numerical simulation of blood flow and plaque progression in carotid-carotid bypass patient specific case.

This study describes computer simulation of blood flow and plaque progression pattern in a patient who underwent surgical treatment for infected carotid prosthetic tube graft using carotid-carotid cross-over bypass. The 3D blood flow is governed by the Navier-Stokes equations, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modelled by the convection-diffusion equation. Low-density lipoprotein (LDL) transport in lumen of the vessel is described by Kedem-Katchalsky equations. The inflammatory process is solved using three additional reaction-diffusion partial differential equations. Calculation based on a computer simulation showed that flow distribution in the left carotid artery (CA) was around 40-50% of the total flow in the right common CA. Also, the left CA had higher pressure gradient after surgical intervention. Plaque progression simulation predicted development of the atherosclerotic plaque in the position of the right common CA and the left internal CA. A novel way of atherosclerotic plaque progression modelling using computer simulation shows a potential clinical benefit with significant impact on the treatment strategy optimization.

ARTreat Project: three-dimensional numerical simulation of plaque formation and development in the arteries.

Atherosclerosis is a progressive disease characterized by the accumulation of lipids and fibrous elements in arteries. It is characterized by dysfunction of endothelium and vasculitis, and accumulation of lipid, cholesterol, and cell elements inside blood vessel wall. In this study, a continuum-based approach for plaque formation and development in 3-D is presented. The blood flow is simulated by the 3-D Navier-Stokes equations, together with the continuity equation while low-density lipoprotein (LDL) transport in lumen of the vessel is coupled with Kedem-Katchalsky equations. The inflammatory process was solved using three additional reaction-diffusion partial differential equations. Transport of labeled LDL was fitted with our experiment on the rabbit animal model. Matching with histological data for LDL localization was achieved. Also, 3-D model of the straight artery with initial mild constriction of 30% plaque for formation and development is presented.

Patient-specific prediction of coronary plaque growth from CTA angiography: a multiscale model for plaque formation and progression.

Computational fluid dynamics methods based on in vivo 3-D vessel reconstructions have recently been identified the influence of wall shear stress on endothelial cells as well as on vascular smooth muscle cells, resulting in different events such as flow mediated vasodilatation, atherosclerosis, and vascular remodeling. Development of image-based modeling technologies for simulating patient-specific local blood flows is introducing a novel approach to risk prediction for coronary plaque growth and progression. In this study, we developed 3-D model of plaque formation and progression that was tested in a set of patients who underwent coronary computed tomography angiography (CTA) for anginal symptoms. The 3-D blood flow is described by the Navier-Stokes equations, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modeled by a convection-diffusion equation. The low density lipoprotein (LDL) transports in lumen of the vessel and through the vessel tissue (which has a mass consumption term) are coupled by Kedem-Katchalsky equations. The inflammatory process is modeled using three additional reaction-diffusion partial differential equations. A full 3-D model was created. It includes blood flow and LDL concentration, as well as plaque formation and progression. Furthermore, features potentially affecting plaque growth, such as patient risk score, circulating biomarkers, localization and composition of the initial plaque, and coronary vasodilating capability were also investigated. The proof of concept of the model effectiveness was assessed by repetition of CTA, six months after.