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MRI super-resolution via realistic downsampling with adversarial learning
Bangyan Huang
, Haonan Xiao
, Weiwei Liu
, Yibao Zhang
, Hao Wu
, Weihu Wang
, Yunhuan Yang
, Yidong Yang
, G. Wilson Miller
, Tian Li
,
Jing Cai
Department of Health Technology and Informatics
The Hong Kong Polytechnic University
Research output
:
Journal article publication
›
Journal article
›
Academic research
›
peer-review
20
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Citations (Scopus)
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Keyphrases
Low Contrast
100%
Downsampling
100%
Adversarial Learning
100%
MRI Super-Resolution
100%
Magnetic Resonance Imaging
42%
High-resolution Image
42%
Super-resolution
28%
Gaussian Method
28%
Generative Adversarial Networks
28%
High-resolution Network
28%
K-space
28%
Zero-filling
28%
High-resolution
14%
High-resolution Scanning
14%
Controlled Experiment
14%
Peak Signal to Noise Ratio
14%
Gaussian Blur
14%
Level Difference
14%
Data Construction
14%
Structural Similarity Index
14%
Breath-hold
14%
Deep Learning Methods
14%
Resolution Enhancement
14%
Network Enhancement
14%
Reference-free
14%
Art Performance
14%
Deep Learning Framework
14%
Patch Level
14%
Spatial Quality
14%
Unreality
14%
Learning-based Super-resolution
14%
Liver MR
14%
Computer Science
super resolution
100%
Low Resolution Image
100%
Resolution Image
42%
Generative Adversarial Networks
28%
Deep Learning Method
28%
Learning Framework
14%
Art Performance
14%
Considerable Number
14%
peak signal to noise ratio
14%
Conventional Method
14%
Structural Similarity
14%
Generalizability
14%
Discriminator
14%
Spatial Quality
14%
Earth and Planetary Sciences
Gaussian Method
100%
Similarity Index
33%
State of the Art
33%
Discriminator
33%