Unsupervised Representation Learning for 3-D Magnetic Resonance Imaging Superresolution With Degradation Adaptation-Reference-Cited by-同舟云学术

Unsupervised Representation Learning for 3-D Magnetic Resonance Imaging Superresolution With Degradation Adaptation

Published:2024-09 Issue:9 Volume:5 Page:4660-4674
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Liu Jianan¹^ORCID,Li Hao²^ORCID,Huang Tao³^ORCID,Ahn Euijoon³^ORCID,Han Kang⁴^ORCID,Razi Adeel⁵^ORCID,Xiang Wei⁴^ORCID,Kim Jinman⁶^ORCID,Feng David Dagan⁶^ORCID

Affiliation:

1. Vitalent Consulting, Gothenburg, Sweden

2. Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany

3. College of Science and Engineering, James Cook University, Cairns, Australia

4. School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia

5. Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia

6. School of Computer Science, University of Sydney, Sydney, NSW, Australia

Funder

The Wellcome Centre for Human Neuroimaging

CIFAR Azrieli Global Scholar in the Brain, Mind, and Consciousness Program

Australian Research Council

Australian National Health and Medical Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9078688/10673734/10521849.pdf?arnumber=10521849

Reference56 articles.

1. Accelerating magnetic resonance imaging via deep learning

2. Channel Splitting Network for Single MR Image Super-Resolution

3. MR Image Super-Resolution with Squeeze and Excitation Reasoning Attention Network

4. Brain MRI super-resolution using deep 3D convolutional networks

5. Multiscale brain MRI super-resolution using deep 3D convolutional networks