4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid Dynamics

15 April 2020

Papers citing "4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid Dynamics"

4 / 4 papers shown

Title
CPT-Interp: Continuous sPatial and Temporal Motion Modeling for 4D Medical Image Interpolation Xia Li Runzhao Yang Xiangtai Li A. Lomax Ye Zhang Joachim Buhmann MedIm 32 0 0 24 May 2024
Physics-informed compressed sensing for PC-MRI: an inverse Navier-Stokes problem Alexandros Kontogiannis M. Juniper 26 10 0 04 Jul 2022
Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks Y. Yasuda R. Onishi 17 5 0 22 Feb 2022
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi Jose Caballero Ferenc Huszár J. Totz Andrew P. Aitken Rob Bishop Daniel Rueckert Zehan Wang SupR 195 5,175 0 16 Sep 2016