430 research outputs found

    Influence of stent-induced vessel deformation on hemodynamic feature of bloodstream inside ICA aneurysms

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    One of the effective treatment options for intracranial aneurysms is stent-assisted coiling. Though, previous works have demonstrated that stent usage would result in the deformation of the local vasculature. The effect of simple stent on the blood hemodynamics is still uncertain. In this work, hemodynamic features of the blood stream on four different ICA aneurysm with/without interventional are investigated. To estimate the relative impacts of vessel deformation, four distinctive ICA aneurysm is simulated by the one-way FSI technique. Four hemodynamic factors of aneurysm blood velocity, wall pressure and WSS are compared in the peak systolic stage to disclose the impact of defamation by the stent in two conditions. The stent usage would decrease almost all of the mentioned parameters, except for OSI. Stenting reduces neck inflow rate, while the effect of interventional was not consistent among the aneurysms. The deformation of an aneurysm has a strong influence on the hemodynamics of an aneurysm. This outcome is ignored by most of the preceding investigations, which focused on the pre-interventional state for studying the relationship between hemodynamics and stents. Present results show that the application of stent without coiling would improve most hemodynamic factors, especially when the deformation of the aneurysm is high enough

    Numerical approach for the evaluation of hemodynamic behaviour in peripheral arterial disease : A systematic review

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    Reduced blood flow to the lower extremities causes peripheral arterial disease (PAD), which is caused by atherosclerotic plaque in the arterial wall. If this impairment is not treated, it will result in severe vascular diseases like ulceration and gangrene. Previous research has shown that while evaluating the pathology of the peripheral artery, the assumption of the model geometry significantly impacts the uncertainty of the stenosis area. However, more work needs to be done to understand the interaction between mechanical better and flow conditions in the peripheral artery using a separate computer model of the cardiovascular system. This paper reviews the numerical approach on pre and post-treatment of hemodynamic behavior in peripheral arterial disease (PAD). The goal of this study was to thoroughly examine the most recent developments with the application of computational studies in PAD from 2017 to 2022. While FSI investigation highlights the behavior of both the fluid and structure domains (blood and artery) during the numerical analysis of blood flow, CFD simulations primarily focus on the fluid domain (blood) behavior. Out of 92 research publications, 19 were appropriate for this assignment. This thorough study divides the publications into the categories of CFD, and FSI approaches. The results were then reviewed in accordance with the wall characteristic, analytical method, geometry, viscosity models, and validation. This paper summarizes the parameters of geometrical construction, viscosity models, analysis methods, and wall characteristics taken into consideration by the researchers to identify and simulate the blood flood flow in the stenosis area. These parameters are summarised in this study. Additionally, it could offer systematic data to help future studies produce better computational analyse

    Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke

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    Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA2DS2-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction—known as Virchow’s triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation. Over the past decade, cardiac imaging and image-based biophysical modelling have emerged as powerful tools for reproducing the mechanisms of thrombogenesis. Clinical imaging modalities such as cardiac computed tomography, magnetic resonance and echocardiographic techniques can measure blood flow velocities and identify LA fibrosis (an indicator of endothelial dysfunction), but imaging remains limited in its ability to assess blood coagulation dynamics. In-silico cardiac modelling tools—such as computational fluid dynamics for blood flow, reaction-diffusion-convection equations to mimic the coagulation cascade, and surrogate flow metrics associated with endothelial damage—have grown in prevalence and advanced mechanistic understanding of thrombogenesis. However, neither technique alone can fully elucidate thrombogenicity in AF. In future, combining cardiac imaging with in-silico modelling and integrating machine learning approaches for rapid results directly from imaging data will require development under a rigorous framework of verification and clinical validation, but may pave the way towards enhanced personalised stroke risk stratification in the growing population of AF patients. This Review will focus on the significant progress in these fields

    Computational Model of Nano-Pharmacological Particles for the Clinical Management of Stenotic and Aneurysmatic Coronary Artery in the Human Body

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    This work presents a three-dimensional computational study of nanoparticles (metallic and non-metallic) suspended in blood flowing through a diseased artery with both stenosis and aneurysm. From the perspective of pharmacodynamics and heat transfer, the influence of nanoparticles on hemodynamic indicators was investigated in a diseased artery. The blood was flowing fluid, steady-state, incompressible, homogeneous, and Newtonian, while the artery was a rigid wall. The three-dimensional continuity, Navier-Stokes, and energy equations were solved numerically by using a RAN-based standard k-ω model, which was performed on the ANSYS commercial software package. The influence of different selected nanoparticles (Al2O3, CuO, SiO2, and ZnO), nanoparticle concentration (1.0%-4.0%), and nanoparticle diameters (25 nm - 100 nm) on hemodynamic parameters such as velocity, temperature, turbulence intensity, more particularly skin friction coefficient and Nusselt number of the blood flow on the diseased artery, was also investigated. The streamlines, contours, and plots were adopted to better visualize the blood flow behavior in an artery with stenosis and aneurysm. The numerical results revealed that at a 4.0% nanoparticle concentration, CuO nanoparticles greatly reduced the blood velocity by 1.96% compared to other nanoparticles. About 0.66%-2.05% reduction in the blood velocity could be achieved by increasing the nanoparticle concentration from 1.0% to 4.0%. The SiO2 blood nanofluid showed the best result in augmentation of the Nusselt number by 53.0%. However, the nanoparticle diameter and concentration showed an insignificant effect on the skin friction facto

    La caractérisation du flux artériel hépatique par la technique 4D Flow

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    Objectif : Déterminer la capacité de la séquence IRM 4D flow à mesurer la forme et le flot (débit, vélocité) de l’artère hépatique et de ses branches en trois dimensions. Méthodologie : Un fantôme de l’artère hépatique réaliste qui imite le flux sanguin et les mouvements respiratoires ainsi que 20 volontaires ont été imagés. La précision du 4D flow Cartésien avec navigateur et remplissage de l’espace-k selon la position respiratoire était déterminée in-vitro à quatre résolutions spatiales (0,5 à 1,0 mm isotropique) et fenêtres d’acceptation du navigateur (± 8 et ± 2 mm) avec un scanner IRM à 3T. Deux séquences centrées sur les branches hépatiques et gastroduodénales étaient évaluées in-vivo et comparés au contraste de phase 2D. Résultats : In vitro, l’augmentation de la résolution spatiale diminuait plus l’erreur qu’une fenêtre d’acceptation plus étroite (30.5 à -4.67% vs -6.64 à -4.67% pour le débit). In vivo, les artèreshépatiques et gastroduodénales étaient mieux visualisées avec la séquence de haute résolution (90 vs 71%). Malgré un accord interobservateur similaire (κ = 0.660 et 0.704), la séquence à plus haute résolution avait moins de variabilité pour l’aire, le débit, et la vélocité moyenne. Le 4D flow avait une meilleure cohérence interne entre l’afflux et l’efflux à la bifurcation de l’artère hépatique (1.03 ± 5.05% et 15.69 ± 6.14%) que le contraste de phase 2D (28.77 ± 21.01%). Conclusion : Le 4D flow à haute résolution peut évaluer l’anatomie et l’hémodynamie de l’artère hépatique avec une meilleure précision, visibilité, moindre variabilité et meilleure concordance interne.Objectives: To assess the ability of four-dimensional (4D) flow, an MRI sequence that captures the form and flow of vessels in three dimensions, to measure hepatic arterial hemodynamics. Methods: A dynamic hepatic artery phantom and 20 consecutive volunteers were scanned. The accuracies of Cartesian 4D flow sequences with k-space reordering and navigator gating at four spatial resolutions (0.5- to 1-mm isotropic) and navigator acceptance windows (± 8 to ± 2 mm) were assessed in vitro at 3 T. Two sequences centered on gastroduodenal and hepatic artery branches were assessed in vivo for intra - and interobserver agreement and compared to 2D phase-contrast (0.5-mm in -plane). Results In vitro, higher spatial resolution led to a greater decrease in error than narrower navigator window (30.5 to −4.67% vs−6.64 to −4.67% for flow). In vivo, hepatic and gastroduodenal arteries were visualized more frequently with the higher resolution sequence (90 vs 71%). Despite similar interobserver agreement (κ = 0.660 and 0.704), the higher resolution sequence had lower variability for area, flow, and average velocity. 4D flow had lower differences between inflow and outflow at the hepatic artery bifurcation (11.03 ± 5.05% and 15.69 ± 6.14%) than 2D phase-contrast (28.77 ± 21.01%). Conclusion: High-resolution 4D flow can assess hepatic artery anatomy and hemodynamics with improved accuracy, greater vessel visibility, better interobserver reliability, and internal consistency

    Measuring blood flow and pulsatility with MRI: optimisation, validation and application in cerebral small vessel disease

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    Cerebral small vessel disease (SVD) is the breakdown of the small blood vessels of the brain, leading to many cases of stroke and dementia. The pathophysiology of SVD is largely unknown, although several mechanisms have been suggested. One such mechanism is the role of increased blood flow pulsatility into the brain, caused by vessel stiffening, leading to damage of the microvasculature. Magnetic resonance imaging (MRI) allows us to non-invasively measure blood flow and velocity using a technique called phase contrast-MRI – traditionally used with 2D slices across the vessel(s) of interest. An advanced form of phase-contrast MRI, known as 4D flow, has emerged in recent years that allows for a volume of data to be acquired, containing velocity information in all directions. However, to keep scan times practical when collecting this amount of data, spatiotemporal resolution has to be sacrificed. The main aim of this thesis was to assess 4D flow’s capabilities, including comparing it to the more well-established 2D method in healthy volunteers, patients, and phantom experiments, so as to better understand its role in investigating SVD. Another aim was to learn more about the role of flow and pulsatility in SVD development in patients using data acquired in the longitudinal Mild Stroke Study 3 (MSS3). Firstly, I systematically reviewed studies that have assessed the human brain using 4D flow. Across 61 relevant studies, I found a general consensus for the current use of the technique in this context. I then optimised the Siemens prototype 4D flow sequence (N = 11 healthy volunteers), testing different parameters to find the combination that best balanced scan quality and duration. I then assessed the test-retest repeatability and intra-rater reliability of both 2D and 4D methods (N = 11 healthy volunteers), as well as differences between them. Following this, I performed the same 4D-2D comparison on SVD patients (N = 10). Absolute flow measurements using 4D flow were shown to have moderate repeatability and reliability, while flow pulsatility measurements showed acceptable repeatability and reliability. Furthermore, 2D arterial pulsatility was measured higher than with 4D, while 4D often measured higher flow rates than 2D. 4D flow was shown to be feasible when used on SVD patients, with no noticeable issues caused by potential patient movement. Flow data analysis from the longitudinal SVD study MSS3 showed that intracranial pulsatility is associated with cross-sectional SVD lesion volume but not longitudinal lesion growth, with stronger associations seen in the arteries of the neck compared to the venous sinuses

    Machine learning and reduced order modelling for the simulation of braided stent deployment

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    Endoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel is not easily predictable and medical volumetric images alone may be insufficient to plan the treatment satisfactorily. Therefore, here we propose a fast and accurate machine learning and reduced order modelling framework, based on finite element simulations, to assist practitioners in the planning and interventional stages. It consists of a first classification step to determine a priori whether a simulation will be successful (good conformity between stent and vessel) or not from a clinical perspective, followed by a regression step that provides an approximated solution of the deployed stent configuration. The latter is achieved using a non-intrusive reduced order modelling scheme that combines the proper orthogonal decomposition algorithm and Gaussian process regression. The workflow was validated on an idealized intracranial artery with a saccular aneurysm and the effect of six geometrical and surgical parameters on the outcome of stent deployment was studied. We trained six machine learning models on a dataset of varying size and obtained classifiers with up to 95% accuracy in predicting the deployment outcome. The support vector machine model outperformed the others when considering a small dataset of 50 training cases, with an accuracy of 93% and a specificity of 97%. On the other hand, real-time predictions of the stent deployed configuration were achieved with an average validation error between predicted and high-fidelity results never greater than the spatial resolution of 3D rotational angiography, the imaging technique with the best spatial resolution (0.15 mm). Such accurate predictions can be reached even with a small database of 47 simulations: by increasing the training simulations to 147, the average prediction error is reduced to 0.07 mm. These results are promising as they demonstrate the ability of these techniques to achieve simulations within a few milliseconds while retaining the mechanical realism and predictability of the stent deployed configuration

    Efficient multi-fidelity computation of blood coagulation under flow

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    Clot formation is a crucial process that prevents bleeding, but can lead to severe disorders when imbalanced. This process is regulated by the coagulation cascade, a biochemical network that controls the enzyme thrombin, which converts soluble fibrinogen into the fibrin fibers that constitute clots. Coagulation cascade models are typically complex and involve dozens of partial differential equations (PDEs) representing various chemical species’ transport, reaction kinetics, and diffusion. Solving these PDE systems computationally is challenging, due to their large size and multi-scale nature. We propose a multi-fidelity strategy to increase the efficiency of coagulation cascade simulations. Leveraging the slower dynamics of molecular diffusion, we transform the governing PDEs into ordinary differential equations (ODEs) representing the evolution of species concentrations versus blood residence time. We then Taylor-expand the ODE solution around the zero-diffusivity limit to obtain spatiotemporal maps of species concentrations in terms of the statistical moments of residence time, , and provide the governing PDEs for . This strategy replaces a high-fidelity system of N PDEs representing the coagulation cascade of N chemical species by N ODEs and p PDEs governing the residence time statistical moments. The multi-fidelity order (p) allows balancing accuracy and computational cost providing a speedup of over N/p compared to high-fidelity models. Moreover, this cost becomes independent of the number of chemical species in the large computational meshes typical of the arterial and cardiac chamber simulations. Using a coagulation network with N = 9 and an idealized aneurysm geometry with a pulsatile flow as a benchmark, we demonstrate favorable accuracy for low-order models of p = 1 and p = 2. The thrombin concentration in these models departs from the high-fidelity solution by under 20% (p = 1) and 2% (p = 2) after 20 cardiac cycles. These multi-fidelity models could enable new coagulation analyses in complex flow scenarios and extensive reaction networks. Furthermore, it could be generalized to advance our understanding of other reacting systems affected by flow.MGH, MGV and OF have been partially supported by the Spanish Research Agency and the European Regional Development Fund, under grant number PID2019-107279RB-I00. MGH, MGV, PML, JB and OF have been partially supported by the Comunidad de Madrid and the European Regional Development Fund, under grant number Y2018/BIO-4858 PREFI-CM, and by the Instituto de Salud Carlos III and the European Regional Development Fund, under grant numbers PI15/02211-ISBITAMI and DTS/1900063-ISBIFLOW. AG, EMcV, AK and JCdA have been partially supported by the US National Institutes of Health, under grant 1R01HL160024. JCdA has been partially supported by the US National Insitutes of Health, under grant number 1R01HL158667

    Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields

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    Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy
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