Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network

Chang Gao(University of California, Los Angeles), Peng Hu(Phoenix Children's Hospital), Brian M. Dale(Siemens Healthcare (United States)), Xiaodong Zhong(Siemens Healthcare (United States)), Dominik Nickel(Massachusetts Institute of Technology), Chuthaporn Surawech(Chulalongkorn University), Ely Felker(University of California, Los Angeles), J. Paul Finn(University of California, Los Angeles), Holden H. Wu(University of California, Los Angeles), Vahid Ghodrati(University of California, Los Angeles), Thomas Vahle(Siemens Healthineers (Germany)), Qi Miao(University of California, Los Angeles), Shu‐Fu Shih(University of California, Los Angeles), Yongkai Liu(Université de Montpellier)
Magnetic Resonance Imaging
October 18, 2022
10.1016/j.mri.2022.10.010
Cited by 9

Related Papers

<scp>ME‐Net</scp>: <scp>Multi‐encoder</scp> net framework for brain tumor segmentation
|International Journal of Imaging Systems and Technology|2021|122
Automatic Prostate Zonal Segmentation Using Fully Convolutional Network With Feature Pyramid Attention
|IEEE Access|2019|93
3D PBV-Net: An automated prostate MRI data segmentation method
|Computers in Biology and Medicine|2020|85
Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation
|IEEE Access|2020|80
Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model
|Stroke|2023|70