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Input Parameterized Physics Informed Neural Network for Advanced 4d Flow MRI Processing
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Physics-informed neural networks (PINNs) for fluid mechanics: a review
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SimVascular: An Open Source Pipeline for Cardiovascular Simulation
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Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH): Phase I: Segmentation
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Optimization of eddy-current compensation
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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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Super-resolution and denoising of 4D-Flow MRI using physics-Informed deep neural nets
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Predicting hemodynamic indices in coronary artery aneurysms using response surface method: An application in Kawasaki disease
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Transient flow prediction in an idealized aneurysm geometry using data assimilation
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Integrating multi-fidelity blood flow data with reduced-order data assimilation
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How does hemodynamics affect rupture tissue mechanics in abdominal aortic aneurysm: Focus on wall shear stress derived parameters, time-averaged wall shear stress, oscillatory shear index, endothelial cell activation potential, and relative residence time
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Denoising and spatial resolution enhancement of 4D flow MRI using proper orthogonal decomposition and lasso regularization
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Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks
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Direct numerical simulation of transitional flow in a patient-specific intracranial aneurysm
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The effect of resolution on viscous dissipation measured with 4D flow MRI in patients with Fontan circulation: Evaluation using computational fluid dynamics
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Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression
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Magnetic Resonance Measurement of Turbulent Kinetic Energy for the Estimation of Irreversible Pressure Loss in Aortic Stenosis
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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Transfer learning based multi-fidelity physics informed deep neural network
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TransFlowNet: A physics-constrained Transformer framework for spatio-temporal super-resolution of flow simulations
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Arteriovenous malformations
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Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data
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Application of physics-based flow models in cardiovascular medicine: Current practices and challenges
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Deep Residual Learning for Image Recognition
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MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
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Physics-Informed Compressed Sensing for PC-MRI: An Inverse Navier-Stokes Problem
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A Review of Methods for Correction of Intensity Inhomogeneity in MRI
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Numerical Simulations of Flow in Cerebral Aneurysms: Comparison of CFD Results and In Vivo MRI Measurements
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Cerebral Blood Flow in a Healthy Circle of Willis and Two Intracranial Aneurysms: Computational Fluid Dynamics Versus Four-Dimensional Phase-Contrast Magnetic Resonance Imaging
inferredcites →https://doi.org/10.1115/1.4031794openalex-extracted
The Computational Fluid Dynamics Rupture Challenge 2013—Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms
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Characterizations and Correlations of Wall Shear Stress in Aneurysmal Flow
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Physics Informed Deep Neural Net Inverse Modeling for Estimating Model Parameters in Permeable Porous Media Flows
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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
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Cerebral catheter angiography and its complications
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Patient-Specific Modeling of Cardiovascular Mechanics
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Transstenotic Pressure Gradients: Measurement in Swine—Retrospectively ECG-gated 3D Phase-Contrast MR Angiography versus Endovascular Pressure-sensing Guidewires
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Transcranial Doppler Ultrasound: A Review of the Physical Principles and Major Applications in Critical Care
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Computational fluid dynamics simulations of blood flow regularized by 3D phase contrast MRI
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4D flow cardiovascular magnetic resonance consensus statement
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AneuRisk65: A dataset of three-dimensional cerebral vascular geometries
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A review on the reliability of hemodynamic modeling in intracranial aneurysms: why computational fluid dynamics alone cannot solve the equation
inferredcites →https://doi.org/10.3171/2019.4.focus19232openalex-extracted
The biophysical role of hemodynamics in the pathogenesis of cerebral aneurysm formation and rupture
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Biomechanical Investigation of Disturbed Hemodynamics-Induced Tissue Degeneration in Abdominal Aortic Aneurysms Using Computational and Experimental Techniques
inferredcites →https://doi.org/10.3389/fphy.2020.00138openalex-extracted
4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics
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