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Physics-Informed Neural Networks for Enhancing 4d Flow MRI: Correcting Artifacts and Assimilating Mean Pressure and Reynolds Stresses in Turbulent Blood Flow

https://doi.org/10.2139/ssrn.5010501
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4D flow MRI
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MRI‐based computational fluid dynamics for diagnosis and treatment prediction: Clinical validation study in patients with coarctation of aorta
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Phase‐contrast single‐point imaging with synchronized encoding: a more reliable technique for in vitro flow quantification
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Fundamentals of turbulent flow spectrum imaging
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Haemodynamic imaging of thoracic stent-grafts by computational fluid dynamics (CFD): presentation of a patient-specific method combining magnetic resonance imaging and numerical simulations
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Divergence-free smoothing for volumetric PIV data
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Magnetic resonance velocimetry in high-speed turbulent flows: sources of measurement errors and a new approach for higher accuracy
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Reynolds stress tensor measurements using magnetic resonance velocimetry: expansion of the dynamic measurement range and analysis of systematic measurement errors
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In Vivo Validation of Numerical Prediction for Turbulence Intensity in an Aortic Coarctation
inferredcites →https://doi.org/10.1007/s11517-021-02417-8openalex-snapshot
Optimization of 4D flow MRI velocity field in the aorta with divergence-free smoothing
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Multilayer feedforward networks are universal approximators
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Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
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Transitional flow in intracranial aneurysms – A space and time refinement study below the Kolmogorov scales using Lattice Boltzmann Method
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Numerical and experimental assessment of turbulent kinetic energy in an aortic coarctation
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Turbulence intensity measurements using particle image velocimetry in diseased carotid artery models: Effect of stenosis severity, plaque eccentricity, and ulceration
inferredcites →https://doi.org/10.1016/j.jbiomech.2019.01.016openalex-snapshot
Characterization and estimation of turbulence-related wall shear stress in patient-specific pulsatile blood flow
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Magnetic Resonance Measurement of Turbulent Kinetic Energy for the Estimation of Irreversible Pressure Loss in Aortic Stenosis
inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-snapshot
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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Cerebrovascular super-resolution 4D Flow MRI – Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure
inferredcites →https://doi.org/10.1017/dce.2022.37openalex-snapshot
Mean flow reconstruction of unsteady flows using physics-informed neural networks
inferredcites →https://doi.org/10.1017/s0022112008002188openalex-snapshot
Asymmetry and transition to turbulence in a smooth axisymmetric constriction
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Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves
inferredcites →https://doi.org/10.1038/srep46618openalex-snapshot
Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI
inferredcites →https://doi.org/10.1063/5.0055600openalex-snapshot
Uncovering near-wall blood flow from sparse data with physics-informed neural networks
inferredcites →https://doi.org/10.1063/5.0095270openalex-snapshot
Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations
inferredcites →https://doi.org/10.1063/5.0116218openalex-snapshot
Mean flow data assimilation based on physics-informed neural networks
inferredcites →https://doi.org/10.1088/1361-6501/aca9ebopenalex-snapshot
Enhancement of PIV measurements via physics-informed neural networks
inferredcites →https://doi.org/10.1098/rsif.2012.1031openalex-snapshot
Experimental insights into flow impingement in cerebral aneurysm by stereoscopic particle image velocimetry: transition from a laminar regime
inferredcites →https://doi.org/10.1098/rspa.2023.0798openalex-snapshot
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements
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Machine Learning for Fluid Mechanics
inferredcites →https://doi.org/10.1146/annurev.fluid.29.1.399openalex-snapshot
BLOOD FLOW IN ARTERIES
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Effects of Disturbed Flow on Vascular Endothelium: Pathophysiological Basis and Clinical Perspectives
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Wall Shear Stress – an Important Determinant of Endothelial Cell Function and Structure – in the Arterial System in vivo
inferredcites →https://doi.org/10.1161/circimaging.116.005486openalex-snapshot
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inferredcites →https://doi.org/10.1186/s12968-023-00942-zopenalex-snapshot
4D Flow cardiovascular magnetic resonance consensus statement: 2023 update
inferredcites →https://doi.org/10.1586/14779072.5.5.927openalex-snapshot
In vivowall shear stress measurements using phase-contrast MRI
inferredcites →https://doi.org/10.2514/6.1992-439openalex-snapshot
A one-equation turbulence model for aerodynamic flows
inferredcites →https://doi.org/10.3389/fcvm.2021.769927openalex-snapshot
WSSNet: Aortic Wall Shear Stress Estimation Using Deep Learning on 4D Flow MRI
inferredcites →https://doi.org/10.48550/arxiv.2308.08468openalex-snapshot
An Expert's Guide to Training Physics-informed Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2310.11147openalex-snapshot
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements
inferredcites →https://doi.org/10.5555/3026877.3026899openalex-snapshot
TensorFlow: A system for large-scale machine learning
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