Deep Learning-based Reconstruction
Deep Learning-Based Reconstruction is a magnetic resonance image reconstruction method that employs deep neural networks to learn the inverse mapping from undersampled or corrupted k-space data to high-quality images, distinguishing itself from traditional reconstruction techniques by leveraging data-driven feature representation and end-to-end optimization.
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Publications
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Authors
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Publications per year
2017–2026
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Understanding deep image representations by inverting them
Aravindh Mahendran, Andrea Vedaldi · 2015 · 1.9K citations
Learning a variational network for reconstruction of accelerated MRI data
Kerstin Hammernik, Teresa Klatzer, Erich Kobler et al. · Magnetic Resonance in Medicine · 2017 · 1.7K citations
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Korea Advanced Institute of Science and Technology, South Korea
12 publications · 1.7K citations
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Chinese Academy of Sciences
Beijing, China
31 h-index
207 publications
12.1K citations
University of Chinese Academy of Sciences
Beijing, China
29 h-index
116 publications
7.7K citations
Tsinghua University
Beijing, China
27 h-index
142 publications
8.3K citations

Beijing Institute of Technology
Beijing, China
26 h-index
102 publications
6K citations
Stanford University
Stanford, United States
24 h-index
139 publications
9.5K citations
Harvard University
Cambridge, United States
23 h-index
122 publications
9.2K citations

Zhejiang University
Hangzhou, China
23 h-index
103 publications
4K citations

Shanghai Jiao Tong University
Shanghai, China
22 h-index
122 publications
4.9K citations
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57 publications · 7.6K citations
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Repository · 112 publications · 3.6K citations
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58 publications · 2.8K citations
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41 publications · 2.1K citations
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33 publications · 2.5K citations
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