Data-driven cardiovascular flow modelling: examples and opportunities

Arzani, Amirhossein; Dawson, Scott T. M.

doi:10.1098/rsif.2020.0802

Cited by 47 publications

(38 citation statements)

References 125 publications

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“…The viscosity of a fluid is associated with its thickness, or its ability to resist deformation in a moving fluid, owing to the frictional interaction of the molecules within the fluid (its easy to move through the air, while harder through water, and a struggle through honey). Blood plasma can be considered as a Newtonian fluid 11 , i.e. constant viscosity with ∼30% thicker properties compared to water (Fig.…”

Section: Bloodmentioning

confidence: 99%

“…10 Corresponding roughly to 170 thousand cardiovascular loops (one loop per minute). 11 A Newtonian fluid like water will strain (deform) linearly over time (shear strain rate, or velocity gradient) in relation to applied shear stresses, where the viscosity determines the degree of this response, i.e. the scalar constant of proportionally between the fluid viscous shear stresses and shear strain rate.…”

Section: Bloodmentioning

confidence: 99%

“…vascular diameter, or boundary layer thickness) or other forcing conditions. These large-scale eddies will deform and eventually breakup into smaller and smaller scales (or higher wavenumbers) by a mechanism called "vortex stretching", transferring their energy to smaller and smaller eddies until they finally dissipate into internal energy (heat) by the action of viscosity at the so-called Kolmogorov scales 11 . This decay-process is also known as the turbulence energy cascade, where the input of energy at the largest scales is equal to the output of energy at the smallest scales (i.e.…”

Section: Turbulence Energy Spectra and Scalesmentioning

confidence: 99%

“…In realistic patient-specific blood flow, the pulsatile nature of the flow does not permit fully- 11 The local Reynolds number based on the local velocity and length scales of these small eddies will approach unity, and hence will start to be dominated by viscous forces. Also, as the eddies get smaller, their vorticity (rotation rate) increases due to angular momentum conservation, resulting in higher velocity gradients.…”

Section: Turbulence Energy Spectra and Scalesmentioning

confidence: 99%

“…After years of advancement, clinical collaborations, and use of modern technologies (e.g. data-driven cardiovascular flow modeling, supercomputing science, uncertainty assessments), CFD now starts to be a serious tool for better understanding of blood flow physiology and development of better tools for personalized therapeutic decision-making [104,228,106,11]. These patient-specific CFD models are usually decoded from MRI images containing anatomical and flow information, often referred to as image-based CFD.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

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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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Section: Bloodmentioning

confidence: 99%

Section: Bloodmentioning

confidence: 99%

Section: Turbulence Energy Spectra and Scalesmentioning

confidence: 99%

Section: Turbulence Energy Spectra and Scalesmentioning

confidence: 99%

Section: Chapter 1 Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Turbulence Descriptors in Arterial Flows : Patient-Specific Computational Hemodynamics

Andersson

2021

Linköping Studies in Science and Technology. Dissertations

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Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries

Vuong,

Bartolf‐Kopp,

Andelovic

et al. 2024

Advanced Science

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Atherosclerosis is the primary cause of cardiovascular disease, resulting in mortality, elevated healthcare costs, diminished productivity, and reduced quality of life for individuals and their communities. This is exacerbated by the limited understanding of its underlying causes and limitations in current therapeutic interventions, highlighting the need for sophisticated models of atherosclerosis. This review critically evaluates the computational and biological models of atherosclerosis, focusing on the study of hemodynamics in atherosclerotic coronary arteries. Computational models account for the geometrical complexities and hemodynamics of the blood vessels and stenoses, but they fail to capture the complex biological processes involved in atherosclerosis. Different in vitro and in vivo biological models can capture aspects of the biological complexity of healthy and stenosed vessels, but rarely mimic the human anatomy and physiological hemodynamics, and require significantly more time, cost, and resources. Therefore, emerging strategies are examined that integrate computational and biological models, and the potential of advances in imaging, biofabrication, and machine learning is explored in developing more effective models of atherosclerosis.

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A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data

Csala,

Amili,

D'Souza

et al. 2024

Numer Methods Biomed Eng

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Experimental blood flow measurement techniques are invaluable for a better understanding of cardiovascular disease formation, progression, and treatment. One of the emerging methods is time‐resolved three‐dimensional phase‐contrast magnetic resonance imaging (4D flow MRI), which enables noninvasive time‐dependent velocity measurements within large vessels. However, several limitations hinder the usability of 4D flow MRI and other experimental methods for quantitative hemodynamics analysis. These mainly include measurement noise, corrupt or missing data, low spatiotemporal resolution, and other artifacts. Traditional filtering is routinely applied for denoising experimental blood flow data without any detailed discussion on why it is preferred over other methods. In this study, filtering is compared to different singular value decomposition (SVD)‐based machine learning and autoencoder‐type deep learning methods for denoising and filling in missing data (imputation). An artificially corrupted and voxelized computational fluid dynamics (CFD) simulation as well as in vitro 4D flow MRI data are used to test the methods. SVD‐based algorithms achieve excellent results for the idealized case but severely struggle when applied to in vitro data. The autoencoders are shown to be versatile and applicable to all investigated cases. For denoising, the in vitro 4D flow MRI data, the denoising autoencoder (DAE), and the Noise2Noise (N2N) autoencoder produced better reconstructions than filtering both qualitatively and quantitatively. Deep learning methods such as N2N can result in noise‐free velocity fields even though they did not use clean data during training. This work presents one of the first comprehensive assessments and comparisons of various classical and modern machine‐learning methods for enhancing corrupt cardiovascular flow data in diseased arteries for both synthetic and experimental test cases.

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Data-driven cardiovascular flow modelling: examples and opportunities

Cited by 47 publications

References 125 publications

Turbulence Descriptors in Arterial Flows : Patient-Specific Computational Hemodynamics

Turbulence Descriptors in Arterial Flows : Patient-Specific Computational Hemodynamics

Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries

A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data

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