References
- H. Ha, H. Huh, D.-H. YANG and N. Kim, Quantification of hemodynamic parameters using four-dimensional flow MRI. Journal of the Korean Radiological Society, (2019) 239-258.
- A.B. Fisher, S. Chien, A.I. Barakat and R.M. Nerem, Endothelial cellular response to altered shear stress. American Journal of Physiology-Lung Cellular and Molecular Physiology, 281 (2001) L529-L533. https://doi.org/10.1152/ajplung.2001.281.3.L529
- D.N. Ku, D.P. Giddens, C.K. Zarins and S. Glagov, Pulsatile flow and atherosclerosis in the human carotid bifurcation. Positive correlation between plaque location and low oscillating shear stress. Arteriosclerosis: An Official Journal of the American Heart Association, Inc., 5 (1985) 293-302. https://doi.org/10.1161/01.ATV.5.3.293
- A.J. Barker, M. Markl, J. Burk, R. Lorenz, J. Bock, S. Bauer, J. Schulz-Menger and F. von Knobelsdorff-Brenkenhoff, Bicuspid aortic valve is associated with altered wall shear stress in the ascending aorta. Circulation: Cardiovascular Imaging, 5 (2012) 457-466.
- M.M. Bissell, A.T. Hess, L. Biasiolli, S.J. Glaze, M. Loudon, A. Pitcher, A. Davis, B. Prendergast, M. Markl and A.J. Barker, Aortic dilation in bicuspid aortic valve disease: flow pattern is a major contributor and differs with valve fusion type. Circulation: Cardiovascular Imaging, 6 (2013) 499-507. https://doi.org/10.1161/CIRCIMAGING.113.000528
- M.E.T.A.H. May, Nature Versus Nurture in Bicuspid Aortic Valve Aortopathy. Circulation, 129 (2014) 622-624. https://doi.org/10.1161/CIRCULATIONAHA.113.007282
- M. Markl, P.J. Kilner and T. Ebbers, Comprehensive 4D velocity mapping of the heart and great vessels by cardiovascular magnetic resonance. Journal of Cardiovascular Magnetic Resonance, 13 (2011) 7.
- A. Harloff, A. Nussbaumer, S. Bauer, A.F. Stalder, A. Frydrychowicz, C. Weiller, J. Hennig and M. Markl, In vivo assessment of wall shear stress in the atherosclerotic aorta using flow-sensitive 4D MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 63 (2010) 1529-1536. https://doi.org/10.1002/mrm.22383
- H. Ha, G.B. Kim, J. Kweon, S.J. Lee, Y.-H. Kim, D.H. Lee, D.H. Yang and N. Kim, Hemodynamic measurement using four-dimensional phase-contrast MRI: quantification of hemodynamic parameters and clinical applications. Korean journal of radiology, 17 (2016) 445.
- O. Ronneberger, P. Fischer and T. Brox, U-net: Convolutional networks for biomedical image segmentation. Medical image computing and computer-assisted intervention-MICCAI 2015, (2015) 234-241.
- M. Raissi, P. Perdikaris and G.E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378 (2019) 686-707. https://doi.org/10.1016/j.jcp.2018.10.045
- A. Amini, E. Vagnoni, A. Favrel, K. Yamaishi, A. Muller and F. Avellan, Upper part-load instability in a reduced-scale Francis turbine: an experimental study. Experiments in Fluids, 64 (2023) 110.
- M.S. Iliescu, G.D. Ciocan and F. Avellan, Analysis of the cavitating draft tube vortex in a Francis turbine using particle image velocimetry measurements in two-phase flow. (2008).
- C. Nicolet, Hydroacoustic modelling and numerical simulation of unsteady operation of hydroelectric systems. 2007, Epfl.