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High-fidelity patient-specific modeling of cardiovascular flows and hemodynamics is challenging.
Method for the calculation of velocity, rate of flow and viscous drag in arteries when the pressure gradient is known
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Structural identification by extended Kalman filter
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The unscented Kalman filter for nonlinear estimation
E. A. Wan and R. Van Der Merwe · 2000
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The ensemble Kalman filter: Theoretical formulation and practical implementation
G. Evensen · 2003
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Optimal estimation of dynamic systems, Chapman & Hall/CRC, 2004
J. Crassidis and J. Junkins · 2004
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In vitro, time-resolved PIV comparison of the effect of stent design on wall shear stress
J. Charonko, S. Karri, J. Schmieg, S. Prabhu, and P. Vlachos · 2009
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Dynamic mode decomposition of numerical and experimental data
P. J.. Schmid · 2010
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Characterization of volumetric flow rate waveforms at the carotid bifurcations of older adults
Y. Hoi, B. Wasserman, Y. Xie, S. Najjar, L. Ferruci, E. Lakatta, G. Gerstenblith, and D. Steinman · 2010
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Characterization of the transport topology in patient-specific abdominal aortic aneurysm models
A Arzani and S. C. Shadden · 2012
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Bicuspid aortic valve is associated with altered wall shear stress in the ascending aorta
A. J. Barker, M. Markl, J. Bürk, R. Lorenz, J. Bock, S. Bauer, J. Schulz-Menger, and F. von Knobelsdorff-Brenkenhoff · 2012
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Sequential parameter estimation for fluid–structure problems: Application to hemodynamics
C. Bertoglio, P. Moireau, and J. F. Gerbeau · 2012
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Automated solution of differential equations by the finite element method
A. Logg, K. A. Mardal, and G. Wells · 2012
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A methodological paradigm for patient-specific multi-scale CFD simulations: from clinical measurements to parameter estimates for individual analysis
S. Pant, B. Fabrèges, J. Gerbeau, and I. Vignon-Clementel · 2014
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Bayesian inference applied to spatio-temporal reconstruction of flows around a NACA0012 airfoil
L. Romain, L. Chatellier, and L. David · 2014
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A systematic comparison between 1-D and 3-D hemodynamics in compliant arterial models
N. Xiao, J. Alastruey, and C. A. Figueroa · 2014
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Mind the gap: impact of computational fluid dynamics solution strategy on prediction of intracranial aneurysm hemodynamics and rupture status indicators
K. Valen-Sendstad and D. A. Steinman · 2014
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On dynamic mode decomposition: Theory and applications
J. H. Tu, Clarence W. Rowley, D. M. Luchtenburg, S. L. Brunton, and J. N. Kutz · 2014
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Effect of coarctation of the aorta and bicuspid aortic valve on flow dynamics and turbulence in the aorta using particle image velocimetry
Z. Keshavarz-Motamed, J. Garcia, E. Gaillard, N. Maftoon, G. Di Labbio, G. Cloutier, and L. Kadem · 2014
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The optimal hard threshold for singular values is 4 4 / 3 \sqrt{3}
M. Gavish and D. Donoho · 2014
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Narrowing the expertise gap for predicting intracranial aneurysm hemodynamics: impact of solver numerics versus mesh and time-step resolution
M. O. Khan, K. Valen-Sendstad, and D. A. Steinman · 2015
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Identification of weakly coupled multiphysics problems. application to the inverse problem of electrocardiography
J. F. Corrado, C.and Gerbeau and P. Moireau · 2015
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Numerical simulation of real-world flows
T. Hayase · 2015
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A data–driven approximation of the Koopman operator: Extending dynamic mode decomposition
M. Williams, I. Kevrekidis, and C. Rowley · 2015
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Oasis: A high-level/high-performance open source Navier–Stokes solver
M. Mortensen and K. Valen-Sendstad · 2015
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Estimation of inlet flow rates for image-based aneurysm CFD models: where and how to begin?
K. Valen-Sendstad, M. Piccinelli, R. KrishnankuttyRema, and D. Steinman · 2015
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Influence of shear stress magnitude and direction on atherosclerotic plaque composition
R. M. Pedrigi, V. V. Mehta, S. M. Bovens, et al · 2016
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The effect of spatial and temporal resolution of cine phase contrast MRI on wall shear stress and oscillatory shear index assessment
M. Cibis, W. V. Potters, F. J. Gijsen, H. Marquering, P. Van Ooij, J. J. Wentzel, and A. J. Nederveen · 2016
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Data assimilation: methods, algorithms, and applications
M. Asch, M. Bocquet, and M. Nodet · 2016
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Dynamic mode decomposition with control
J. Proctor, S. Brunton, and J. Kutz · 2016
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Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition
S. T. M. Dawson, M. Hemati, M. Williams, and C. Rowley · 2016
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Dynamic mode decomposition: data-driven modeling of complex systems
J N. Kutz, S. Brunton, B. Brunton, and J. Proctor · 2016
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Characterizations and correlations of wall shear stress in aneurysmal flow
A. Arzani and S. C. Shadden · 2016
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Oscillatory wall shear stress is a dominant flow characteristic affecting lesion progression patterns and plaque vulnerability in patients with coronary artery disease
Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising
T. Nonomura, H. Shibata, and R. Takaki · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2019
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Data-driven science and engineering: Machine learning, dynamical systems, and control
S. L. Brunton and J. N. Kutz · 2019
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Enhancing magnetic resonance imaging with computational fluid dynamics
G. Annio, R. Torii, B. Ariff, D. P. O’Regan, V. Muthurangu, A. Ducci, V. Tsang, and G. Burriesci · 2019
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An ensemble Kalman filter approach to parameter estimation for patient-specific cardiovascular flow modeling
D. Canuto, J. L. Pantoja, J. Han, E. P. Dutson, and J. D. Eldredge · 2020
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L. H. Timmins, D. S. Molony, P. Eshtehardi, M. C. McDaniel, J. N. Oshinski, D. P. Giddens, and H. Samady · 2017
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Modal analysis of fluid flows: An overview
K. Taira, S. L. Brunton, S. T. M. Dawson, C. W. Rowley, T. Colonius, B. J. McKeon, O. T. Schmidt, S. Gordeyev, V. Theofilis, and L. S. Ukeiley · 2017
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Wall shear stress exposure time: a Lagrangian measure of near-wall stagnation and concentration in cardiovascular flows
A. Arzani, A. M. Gambaruto, G. Chen, and S. C. Shadden · 2017
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Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations
J. S. Tran, D. E. Schiavazzi, A. B. Ramachandra, A. M. Kahn, and A. L. Marsden · 2017
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Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression
A. Bakhshinejad, A. Baghaie, A. Vali, D. Saloner, V. L. Rayz, and R. M. D’Souza · 2017
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Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
P. Perdikaris, M. Raissi, A. Damianou, N. D. Lawrence, and G. E. Karniadakis · 2017
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Wall shear stress fixed points in cardiovascular fluid mechanics
A. Arzani and S. C. Shadden · 2018
Cited alongside, same era.
A flexible framework for sequential estimation of model parameters in computational hemodynamics
C. J. Arthurs, N. Xiao, P. Moireau, T. Schaeffter, and C. A. Figueroa · 2020
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Hemodynamic data assimilation in a subject-specific circle of willis geometry
F. Gaidzik, S. Pathiraja, S. Saalfeld, D. Stucht, O. Speck, D. Thévenin, and G. Janiga · 2020
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Data assimilation in the latent space of a neural network
M. Amendola, R. Arcucci, L. Mottet, C. Q. Casas, S. Fan, C. Pain, P. Linden, and Y. K. Guo · 2020
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Attention-based convolutional autoencoders for 3D-variational data assimilation
J. Mack, R. Arcucci, M. Molina-Solana, and Y. K. Guo · 2020
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Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning
S. E. Ahmed, S. Pawar, O. San, and A. Rasheed · 2020
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Data-driven pulsatile blood flow physics with dynamic mode decomposition
M. Habibi, S. Dawson, and A. Arzani · 2020
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Time-resolved denoising using model order reduction, dynamic mode decomposition, and Kalman filter and smoother
M. Fathi, A. Baghaie, A. Bakhshinejad, R. Sacho, and R. D’Souza · 2020
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Towards multi-modal data fusion for super-resolution and denoising of 4D-Flow MRI
I. Perez-Raya, M. F. Fathi, A. Baghaie, R. H. Sacho, K. M. Koch, and R. M. D’Souza · 2020
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4D flow with MRI
G. Soulat, P. McCarthy, and M. Markl · 2020
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The story of wall shear stress in coronary artery atherosclerosis: biochemical transport and mechanotransduction
M. Mahmoudi, A. Farghadan, D. R. McConnell, A. J. Barker, J. J. Wentzel, M. J. Budoff, and A. Arzani · 2020
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The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls
J. Seo, D. Schiavazzi, A. Kahn, and A. Marsden · 2020
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Super-resolution and denoising of 4D-Flow MRI using physics-informed deep neural nets
M. F. Fathi, I. Perez-Raya, A. Baghaie, P. Berg, G. Janiga, A. Arzani, and R. M. D’Souza · 2020
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H. Gao, L. Sun, and J. X. Wang · 2020
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Noise reduction of flow MRI measurements using a lattice boltzmann based topology optimisation approach
F. Klemens, S. Schuhmann, R. Balbierer, G. Guthausen, H. Nirschl, G. Thäter, and M. J. Krause · 2020
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Blood flow imaging by optimal matching of computational fluid dynamics to 4D-flow data
J. Töger, M. J. Zahr, N. Aristokleous, K. Markenroth Bloch, M. Carlsson, and P. O. Persson · 2020
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A reduced order deep data assimilation model
C. Casas, R. Arcucci, P. Wu, C. Pain, and Y. Guo · 2020
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Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation
L. M. Paun, M. J. Colebank, M. S. Olufsen, N. A. Hill, and D. Husmeier · 2020
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Data-driven cardiovascular flow modelling: examples and opportunities
A. Arzani and S. Dawson · 2021
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Deep data assimilation: Integrating deep learning with data assimilation
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Dynamic modes of inflow jet in brain aneurysms
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Wall shear stress topological skeleton analysis in cardiovascular flows: Methods and applications
V. Mazzi, U. Morbiducci, K. Calò, G. De Nisco, M. Lodi Rizzini, E. Torta, G. C. A. Caridi, C. Chiastra, and D. Gallo · 2021
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