On the public citation graph

Blood flow imaging by optimal matching of computational fluid dynamics to 4D‐flow data

https://doi.org/10.1002/mrm.28269
0 signed · 98 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cnm.3152openalex-snapshot
Variational data assimilation for transient blood flow simulations: Cerebral aneurysms as an illustrative example
inferredcites →https://doi.org/10.1002/jmri.21475openalex-snapshot
Assessment of fluctuating velocities in disturbed cardiovascular blood flow: In vivo feasibility of generalized phase‐contrast MRI
inferredcites →https://doi.org/10.1002/jmri.23610openalex-snapshot
Assessment of the accuracy of MRI wall shear stress estimation using numerical simulations
inferredcites →https://doi.org/10.1002/jmri.23778openalex-snapshot
Variable velocity encoding in a three‐dimensional, three‐directional phase contrast sequence: Evaluation in phantom and volunteers
inferredcites →https://doi.org/10.1002/jmri.23936openalex-snapshot
Venous and arterial flow quantification are equally accurate and precise with parallel imaging compressed sensing 4D phase contrast MRI
inferredcites →https://doi.org/10.1002/jmri.24962openalex-snapshot
4D flow MRI andT1-Mapping: Assessment of altered cardiac hemodynamics and extracellular volume fraction in hypertrophic cardiomyopathy
inferredcites →https://doi.org/10.1002/jmri.25015openalex-snapshot
4D flow MRI can detect subtle right ventricular dysfunction in primary left ventricular disease
inferredcites →https://doi.org/10.1002/mrm.21391openalex-snapshot
Sparse MRI: The application of compressed sensing for rapid MR imaging
inferredcites →https://doi.org/10.1002/mrm.25683openalex-snapshot
Independent validation of four‐dimensional flow MR velocities and vortex ring volume using particle imaging velocimetry and planar laser‐Induced fluorescence
inferredcites →https://doi.org/10.1002/nme.2579openalex-snapshot
Gmsh: A 3‐D finite element mesh generator with built‐in pre‐ and post‐processing facilities
inferredcites →https://doi.org/10.1007/s00246-016-1565-6openalex-snapshot
Decreased Diastolic Ventricular Kinetic Energy in Young Patients with Fontan Circulation Demonstrated by Four-Dimensional Cardiac Magnetic Resonance Imaging
inferredcites →https://doi.org/10.1007/s10107-004-0559-yopenalex-snapshot
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
inferredcites →https://doi.org/10.1007/s10237-015-0720-yopenalex-snapshot
4D subject-specific inverse modeling of the chick embryonic heart outflow tract hemodynamics
inferredcites →https://doi.org/10.1007/s10439-013-0879-2openalex-snapshot
The Impact of MRI-based Inflow for the Hemodynamic Evaluation of Aortic Coarctation
inferredcites →https://doi.org/10.1007/s10439-015-1340-5openalex-snapshot
The Effect of Head Rotation on the Geometry and Hemodynamics of Healthy Vertebral Arteries
inferredcites →https://doi.org/10.1007/s11517-017-1617-yopenalex-snapshot
Minimizing the blood velocity differences between phase-contrast magnetic resonance imaging and computational fluid dynamics simulation in cerebral arteries and aneurysms
inferredcites →https://doi.org/10.1016/j.compbiomed.2019.103507openalex-snapshot
Transient flow prediction in an idealized aneurysm geometry using data assimilation
inferredcites →https://doi.org/10.1016/j.ijnonlinmec.2014.04.005openalex-snapshot
Optimal control in blood flow simulations
inferredcites →https://doi.org/10.1016/j.jbiomech.2012.10.012openalex-snapshot
Inflow boundary conditions for image-based computational hemodynamics: Impact of idealized versus measured velocity profiles in the human aorta
inferredcites →https://doi.org/10.1016/j.jbiomech.2017.05.004openalex-snapshot
Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression
inferredcites →https://doi.org/10.1016/j.jcmg.2014.03.017openalex-snapshot
Pressure Fields by Flow-Sensitive, 4D, Velocity-Encoded CMR in Patients With Aortic Coarctation
inferredcites →https://doi.org/10.1016/j.jcp.2016.09.012openalex-snapshot
An adjoint method for a high-order discretization of deforming domain conservation laws for optimization of flow problems
inferredcites →https://doi.org/10.1016/j.jmr.2010.01.001openalex-snapshot
Reducing data acquisition times in phase-encoded velocity imaging using compressed sensing
inferredcites →https://doi.org/10.1016/j.jtcvs.2016.12.033openalex-snapshot
Patient-specific assessment of hemodynamics by computational fluid dynamics in patients with bicuspid aortopathy
inferredcites →https://doi.org/10.1016/j.medengphy.2014.12.011openalex-snapshot
Is aortic wall shear stress affected by aging? An image-based numerical study with two age groups
inferredcites →https://doi.org/10.1016/j.mri.2014.05.004openalex-snapshot
Highly accelerated aortic 4D flow MR imaging with variable-density random undersampling
inferredcites →https://doi.org/10.1016/j.neuroimage.2006.01.015openalex-snapshot
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
inferredcites →https://doi.org/10.1016/s0021-7824(99)00032-xopenalex-snapshot
Incompressible Limit for Solutionsof the Isentropic Navier–Stokes Equationswith Dirichlet Boundary Conditions
inferredcites →https://doi.org/10.1017/s0022112097008410openalex-snapshot
A universal time scale for vortex ring formation
inferredcites →https://doi.org/10.1038/srep22021openalex-snapshot
Vortex ring behavior provides the epigenetic blueprint for the human heart
inferredcites →https://doi.org/10.1063/1.5011960openalex-snapshot
Computational study of aortic hemodynamics for patients with an abnormal aortic valve: The importance of secondary flow at the ascending aorta inlet
inferredcites →https://doi.org/10.1073/pnas.0600520103openalex-snapshot
Optimal vortex formation as an index of cardiac health
inferredcites →https://doi.org/10.1080/10255842.2017.1334770openalex-snapshot
Uncertainty propagation of phase contrast-MRI derived inlet boundary conditions in computational hemodynamics models of thoracic aorta
inferredcites →https://doi.org/10.1080/10976640701544530openalex-snapshot
Transit of Blood Flow Through the Human Left Ventricle Mapped by Cardiovascular Magnetic Resonance
inferredcites →https://doi.org/10.1098/rsfs.2012.0094openalex-snapshot
Impact of blood rheology on wall shear stress in a model of the middle cerebral artery
inferredcites →https://doi.org/10.1109/isbi.2015.7163941openalex-snapshot
Using fourier velocity encoded MRI data to guide CFD simulations
inferredcites →https://doi.org/10.1109/tbme.2018.2880606openalex-snapshot
Optimal B-Spline Mapping of Flow Imaging Data for Imposing Patient-Specific Velocity Profiles in Computational Hemodynamics
inferredcites →https://doi.org/10.1111/cgf.12368openalex-snapshot
4D MRI Flow Coupled to Physics‐Based Fluid Simulation for Blood‐Flow Visualization
inferredcites →https://doi.org/10.1115/1.4001886openalex-snapshot
Outflow Conditions for Image-Based Hemodynamic Models of the Carotid Bifurcation: Implications for Indicators of Abnormal Flow
inferredcites →https://doi.org/10.1115/1.4023382openalex-snapshot
Variability of Computational Fluid Dynamics Solutions for Pressure and Flow in a Giant Aneurysm: The ASME 2012 Summer Bioengineering Conference CFD Challenge
inferredcites →https://doi.org/10.1115/1.4031794openalex-snapshot
The Computational Fluid Dynamics Rupture Challenge 2013—Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms
inferredcites →https://doi.org/10.1115/1.4037857openalex-snapshot
Impact of Patient-Specific Inflow Velocity Profile on Hemodynamics of the Thoracic Aorta
inferredcites →https://doi.org/10.1137/070692108openalex-snapshot
Newton-GMRES Preconditioning for Discontinuous Galerkin Discretizations of the Navier–Stokes Equations
inferredcites →https://doi.org/10.1137/18m1220996openalex-snapshot
An Efficient, Globally Convergent Method for Optimization Under Uncertainty Using Adaptive Model Reduction and Sparse Grids
inferredcites →https://doi.org/10.1186/1532-429x-17-s1-o4openalex-snapshot
Disturbed diastolic left ventricular inflow vortex ring formation in patients with corrected atrioventricular septal defect: quantitative three-dimensional vortex core analysis from 4DFlow MRI
inferredcites →https://doi.org/10.1186/s12938-015-0104-7openalex-snapshot
Computational fluid dynamics simulations of blood flow regularized by 3D phase contrast MRI
inferredcites →https://doi.org/10.1186/s12938-018-0497-1openalex-snapshot
The effect of inlet and outlet boundary conditions in image-based CFD modeling of aortic flow
inferredcites →https://doi.org/10.1186/s12968-015-0122-4openalex-snapshot
Characterization and quantification of dynamic eccentric regurgitation of the left atrioventricular valve after atrioventricular septal defect correction with 4D Flow cardiovascular magnetic resonance and retrospective valve tracking
inferredcites →https://doi.org/10.1186/s12968-015-0174-5openalex-snapshot
4D flow cardiovascular magnetic resonance consensus statement
inferredcites →https://doi.org/10.1186/s12968-015-0211-4openalex-snapshot
Left ventricular fluid kinetic energy time curves in heart failure from cardiovascular magnetic resonance 4D flow data
inferredcites →https://doi.org/10.1201/9781482234213openalex-snapshot
Numerical Heat Transfer and Fluid Flow
inferredcites →https://doi.org/10.2214/ajr.11.6969openalex-snapshot
Rapid Pediatric Cardiac Assessment of Flow and Ventricular Volume With Compressed Sensing Parallel Imaging Volumetric Cine Phase-Contrast MRI
inferredcites →https://doi.org/10.2312/vcbm.20161272openalex-snapshot
Temporal Interpolation of 4D PC-MRI Blood-flow Measurements Using Bidirectional Physics-based Fluid Simulation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4700974openalex-extracted
Input Parameterized Physics Informed Neural Network for Advanced 4d Flow MRI Processing
inferred← citeshttps://doi.org/10.1002/admt.202100986openalex-snapshot
Evaluation of Energy Harvesting from Different Sections of the Aortic Artery with Several Energy Harvester Materials
inferred← citeshttps://doi.org/10.1002/cnm.3613openalex-snapshot
Inverse problems in blood flow modeling: A review
inferred← citeshttps://doi.org/10.1002/jmri.28303openalex-snapshot
Comparison of 2D and 4D Flow MRI in Neonates Without General Anesthesia
inferred← citeshttps://doi.org/10.1007/978-3-030-92087-6_34openalex-snapshot
Magnetic Resonance Imaging-Based 4D Flow: The Role of Artificial Intelligence
inferred← citeshttps://doi.org/10.1007/978-3-030-92087-6_35openalex-snapshot
Magnetic Resonance Imaging-Based Coronary Flow: The Role of Artificial Intelligence
inferred← citeshttps://doi.org/10.1007/978-3-031-49401-7_23openalex-snapshot
Flux MRI: Accelerating with Aid of Physical Models
inferred← citeshttps://doi.org/10.1007/s00062-020-00959-2openalex-snapshot
Hemodynamic Data Assimilation in a Subject-specific Circle of Willis Geometry
inferred← citeshttps://doi.org/10.1007/s00247-020-04885-wopenalex-snapshot
Additional value and new insights by four-dimensional flow magnetic resonance imaging in congenital heart disease: application in neonates and young children
inferred← citeshttps://doi.org/10.1007/s00366-022-01723-5openalex-snapshot
Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study
inferred← citeshttps://doi.org/10.1007/s10237-021-01474-0openalex-snapshot
Phase offset between arterial pulsations and subarachnoid space pressure fluctuations are unlikely to drive periarterial cerebrospinal fluid flow
inferred← citeshttps://doi.org/10.1007/s10439-024-03606-wopenalex-snapshot
Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks
inferred← citeshttps://doi.org/10.1007/s13239-020-00511-wopenalex-snapshot
Modeling and Reducing the Effect of Geometric Uncertainties in Intracranial Aneurysms with Polynomial Chaos Expansion, Data Decomposition, and 4D-Flow MRI
inferred← citeshttps://doi.org/10.1016/j.amc.2024.129266openalex-snapshot
Time-dependent strategy for improving aortic blood flow simulations with boundary control and data assimilation
inferred← citeshttps://doi.org/10.1016/j.cmpb.2024.108057openalex-snapshot
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI
inferred← citeshttps://doi.org/10.1016/j.cmpb.2025.108861openalex-snapshot
A practical strategy for data assimilation of cerebral intra-aneurysmal flows using a variational method with boundary control of velocity
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2021.104566openalex-snapshot
Integrating multi-fidelity blood flow data with reduced-order data assimilation
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110600openalex-snapshot
Input parameterized physics informed neural networks for de noising, super-resolution, and imaging artifact mitigation in time resolved three dimensional phase-contrast magnetic resonance imaging
inferred← citeshttps://doi.org/10.1016/j.jbiomech.2023.111759openalex-snapshot
Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics
inferred← citeshttps://doi.org/10.1016/j.jcp.2022.111262openalex-snapshot
Iterated Kalman methodology for inverse problems
inferred← citeshttps://doi.org/10.1016/j.nic.2025.05.002openalex-snapshot
Predicting Cerebral Aneurysm Rupture
inferred← citeshttps://doi.org/10.1016/j.rx.2024.05.007openalex-snapshot
Aplicaciones clínicas del flujo 4D por RM
inferred← citeshttps://doi.org/10.1016/j.rxeng.2024.05.008openalex-snapshot
Clinical applications of four-dimensional flow magnetic resonance imaging
inferred← citeshttps://doi.org/10.1038/s41467-024-54712-1openalex-snapshot
Conditional neural field latent diffusion model for generating spatiotemporal turbulence
inferred← citeshttps://doi.org/10.1038/s41598-021-89636-zopenalex-snapshot
Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data
inferred← citeshttps://doi.org/10.1038/s41598-022-20121-xopenalex-snapshot
Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves
inferred← citeshttps://doi.org/10.1063/5.0054312openalex-snapshot
Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
inferred← citeshttps://doi.org/10.1098/rsif.2021.0751openalex-snapshot
A multi-modality approach for enhancing 4D flow magnetic resonance imaging via sparse representation
inferred← citeshttps://doi.org/10.1109/c2i456876.2022.10051376openalex-snapshot
Comparative Analysis of Finite Element Method Solvers in Computational Blood Flow Investigations
inferred← citeshttps://doi.org/10.1109/tip.2022.3228172openalex-snapshot
Physics-Informed Compressed Sensing for PC-MRI: An Inverse Navier-Stokes Problem
inferred← citeshttps://doi.org/10.1109/tip.2024.3470553openalex-snapshot
Segmentation-Free Velocity Field Super-Resolution on 4D Flow MRI
inferred← citeshttps://doi.org/10.1109/tmi.2021.3086331openalex-snapshot
Divergence-Free Constrained Phase Unwrapping and Denoising for 4D Flow MRI Using Weighted Least-Squares
inferred← citeshttps://doi.org/10.1109/tmi.2026.3676987openalex-snapshot
Deep Learning for Temporal Super-Resolution 4D Flow MRI
inferred← citeshttps://doi.org/10.1115/1.4065165openalex-snapshot
Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning
inferred← citeshttps://doi.org/10.1186/s12968-021-00824-2openalex-snapshot
MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
inferred← citeshttps://doi.org/10.21037/cdt-2019-aidcd-16openalex-snapshot
Advanced imaging in the diagnosis of cardiovascular diseases: the “ongoing” future
inferred← citeshttps://doi.org/10.2463/mrms.rev.2021-0137openalex-snapshot
4D Flow MR Imaging of the Left Atrium: What is Non-physiological Blood Flow in the Cardiac System?
inferred← citeshttps://doi.org/10.3174/ajnr.a6603openalex-snapshot
High Spatiotemporal Resolution 4D Flow MRI of Intracranial Aneurysms at 7T in 10 Minutes
inferred← citeshttps://doi.org/10.3389/fcvm.2022.1052068openalex-snapshot
Advances in machine learning applications for cardiovascular 4D flow MRI
inferred← citeshttps://doi.org/10.3389/frai.2022.928181openalex-snapshot
SRflow: Deep learning based super-resolution of 4D-flow MRI data
inferred← citeshttps://doi.org/10.3390/app12083954openalex-snapshot
Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications
inferred← citeshttps://doi.org/10.3390/diagnostics10070473openalex-snapshot
Hemodynamic Changes in the Carotid Artery after Infusion of Normal Saline Using Computational Fluid Dynamics
inferred← citeshttps://doi.org/10.55095/cesradiol2022/029openalex-snapshot
Experience with the use and processing of data from magnetic resonance flow measurements with the 4D Flow sequence
inferred← citeshttps://doi.org/10.6084/m9.figshare.17120075openalex-snapshot
Additional file 3 of MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
inferred← citeshttps://doi.org/10.6084/m9.figshare.17120087openalex-snapshot
Additional file 4 of MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.