A novel formulation for the study of the ascending aortic fluid dynamics with in vivo data

Capellini, Katia; Gasparotti, Emanuele; Cella, Ubaldo; Costa, Emiliano; Fanni, Benigno Marco; Groth, Corrado; Porziani, Stefano; Biancolini, Marco Evangelos; Celi, Simona

doi:10.1016/j.medengphy.2020.09.005

Cited by 27 publications

(17 citation statements)

References 59 publications

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“…However, on a patient-specific basis, many factors can increase or decrease the risk of dissection. Hemodynamics and the interactions between the blood and the aortic wall are ones of these causes and computational approaches aim at making patient-specific predictions of these factors and of their role in aTAA rupture (Capellini et al, 2020 ; De Nisco et al, 2020 ). The current trend is personalized medicine, where patient-specific parameters are introduced at different levels including morphology, fluid dynamics conditions and tissue mechanics (Condemi et al, 2017 ; Boccadifuoco et al, 2018a ; Fanni et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Fully-Coupled FSI Computational Analyses in the Ascending Thoracic Aorta Using Patient-Specific Conditions and Anisotropic Material Properties

et al. 2021

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Computational hemodynamics has become increasingly important within the context of precision medicine, providing major insight in cardiovascular pathologies. However, finding appropriate compromise between speed and accuracy remains challenging in computational hemodynamics for an extensive use in decision making. For example, in the ascending thoracic aorta, interactions between the blood and the aortic wall must be taken into account for the sake of accuracy, but these fluid structure interactions (FSI) induce significant computational costs, especially when the tissue exhibits a hyperelastic and anisotropic response. The objective of the current study is to use the Small On Large (SOL) theory to linearize the anisotropic hyperelastic behavior in order to propose a reduced-order model for FSI simulations of the aorta. The SOL method is tested for fully-coupled FSI simulations in a patient-specific aortic geometry presenting an Ascending Thoracic Aortic Aneurysm (aTAA). The same model is also simulated with a fully-coupled FSI with non-linear material behavior, without SOL linearization. Eventually, the results and computational times with and without the SOL are compared. The SOL approach is demonstrated to provide a significant reduction of computational costs for FSI analysis in the aTAA, and the results in terms of stress state distribution are comparable. The method is implemented in ANSYS and will be further evaluated for clinical applications.

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Section: Introductionmentioning

confidence: 99%

Fully-Coupled FSI Computational Analyses in the Ascending Thoracic Aorta Using Patient-Specific Conditions and Anisotropic Material Properties

et al. 2021

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“…Indeed, the patient-specific accuracy of FSI simulations requires reliable estimation of the structural constitutive parameters (e.g., vessel wall stiffness), which is nowadays rather difficult to achieve in clinics or the adoption of RBF-based strategies. 9 Another possible limitation of the study could be that no additional control planes were acquired at the supra-aortic branches. Our approach did not impose any constraints at the level of BCA, LCCA, and LSA.…”

Section: Discussionmentioning

confidence: 99%

Effects of Uncertainty of Outlet Boundary Conditions in a Patient-Specific Case of Aortic Coarctation

et al. 2021

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Computational Fluid Dynamics (CFD) simulations of blood flow are widely used to compute a variety of hemodynamic indicators such as velocity, time-varying wall shear stress, pressure drop, and energy losses. One of the major advances of this approach is that it is non-invasive. The accuracy of the cardiovascular simulations depends directly on the level of certainty on input parameters due to the modelling assumptions or computational settings. Physiologically suitable boundary conditions at the inlet and outlet of the computational domain are needed to perform a patient-specific CFD analysis. These conditions are often affected by uncertainties, whose impact can be quantified through a stochastic approach. A methodology based on a full propagation of the uncertainty from clinical data to model results is proposed here. It was possible to estimate the confidence associated with model predictions, differently than by deterministic simulations. We evaluated the effect of using three-element Windkessel models as the outflow boundary conditions of a patient-specific aortic coarctation model. A parameter was introduced to calibrate the resistances of the Windkessel model at the outlets. The generalized Polynomial Chaos method was adopted to perform the stochastic analysis, starting from a few deterministic simulations. Our results show that the uncertainty of the input parameter gave a remarkable variability on the volume flow rate waveform at the systolic peak simulating the conditions before the treatment. The same uncertain parameter had a slighter effect on other quantities of interest, such as the pressure gradient. Furthermore, the results highlight that the fine-tuning of Windkessel resistances is not necessary to simulate the post-stenting scenario.

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“…This low-to-high rank data reconstruction between these two modalities needs to be assessed carefully to attain proper BCs for the purpose of the study [232,11]. A promising modeling improvement would be to incorporate the measured lumen wall dynamics, which have been successfully implemented in cardiovascular flow simulations using high-resolution CT data [127,33], as well as for much coarser MRI data [127]. CT-based CFD modeling has the benefit of attaining a well-resolved vessel geometry, which has shown to be the most crucial modeling aspect influencing the flow predictions [147], but lacks any patient-specific flow information.…”

Section: Chapter 6 Outlookmentioning

confidence: 99%

Turbulence Descriptors in Arterial Flows : Patient-Specific Computational Hemodynamics

Andersson

2021

Linköping Studies in Science and Technology. Dissertations

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At this very moment, there are literally millions of people who suffer from various types of cardiovascular diseases (CVDs), many of whom will experience reduced quality of life or premature lift expectancy. The detailed underlying pathogenic processes behind many of these disorders are not well understood, but were abnormal dynamics of the blood flow (hemodynamics) are believed to play an important role, especially atypical flow-mediated frictional forces on the intraluminal wall (i.e. the wall shear stress, WSS). Under normal physiological conditions, the flow is relatively stable and regular (smooth and laminar), which helps to maintain critical vascular functions. When these flows encounter various unfavorable anatomical obstructions, the flow can become highly unstable and irregular (turbulent), giving rise to abnormal fluctuating hemodynamic forces, which increase the bloodstream pressure losses, can damage the cells within the blood, as well as impair essential structural and functional regulatory mechanisms. Over a prolonged time, these disturbed flow conditions may promote severe pathological responses and are therefore essential to foresee as early as possible.Clinical measurements of blood flow characteristics are often performed non-invasively by modalities such as ultrasound and magnetic resonance imaging (MRI). High-fidelity MRI techniques may be used to attain a general view of the overall large-scale flow features in the heart and larger vessels but cannot be used for estimating small-scale flow variations nor capture the WSS characteristics. Since the era of modern computers, fluid motion can now also be predicted by computational fluid dynamics (CFD)simulations, which can provide discrete mathematical approximations of the flow field with much higher details (resolution) and accuracy compared to other modalities. CFD simulations rely on the same fundamental principles as weather forecasts, the physical laws of fluid motion, and thus can not only be used to assess the current flow state but also to predict (foresee) important outcome scenarios in e.g. intervention planning. To enable blood flow simulations within certain cardiovascular segments, these CFD models are usually reconstructed from MRI-based anatomical and flow image-data. Today, patient-specific computational hemodynamics are essentially only performed within the research field, where much emphasis is dedicated towards understanding normal/abnormal blood flow physiology, developing better individual-based diagnostics/treatments, and evaluating the results reliability/generality in order to approach clinical applicability.In this thesis, advanced CFD methods were adopted to simulate realistic patient-specific turbulent hemodynamics in constricted arteries reconstructed from MRI data. The main focus was to investigate novel, comprehensive ways to characterize these abnormal flow conditions, in the pursuit of better clinical decision-making tools; from more in-depth analyzes of various turbulence-related tensor characteristics to descrip...

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A novel formulation for the study of the ascending aortic fluid dynamics with in vivo data

Cited by 27 publications

References 59 publications

Fully-Coupled FSI Computational Analyses in the Ascending Thoracic Aorta Using Patient-Specific Conditions and Anisotropic Material Properties

Fully-Coupled FSI Computational Analyses in the Ascending Thoracic Aorta Using Patient-Specific Conditions and Anisotropic Material Properties

Effects of Uncertainty of Outlet Boundary Conditions in a Patient-Specific Case of Aortic Coarctation

Turbulence Descriptors in Arterial Flows : Patient-Specific Computational Hemodynamics

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