Compressed Sensing Reconstruction
Compressed Sensing Reconstruction is a mathematical reconstruction framework in neuroimaging physics that enables accurate image recovery from highly undersampled k-space data by exploiting signal sparsity and nonlinear optimization, distinguishing it from conventional reconstruction methods reliant on full Nyquist-sampled data.
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Publications per year
2017–2026
925
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Cs Reconstruction
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David L. Donoho · IEEE Transactions on Information Theory · 2006 · 23.1K citations
The Split Bregman Method for L1-Regularized Problems
Tom Goldstein, Stanley Osher · SIAM Journal on Imaging Sciences · 2009 · 4.3K citations
Compressive Sensing [Lecture Notes]
Richard G. Baraniuk · IEEE Signal Processing Magazine · 2007 · 4.2K citations
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Stanford University
Stanford, United States
44 h-index
199 publications
64.4K citations

Rice University
Houston, United States
37 h-index
129 publications
56.4K citations
Duke University
Durham, United States
28 h-index
123 publications
20.5K citations

Chinese Academy of Sciences
Beijing, China
27 h-index
160 publications
10.2K citations
University of Wisconsin–Madison
Madison, United States
27 h-index
94 publications
14.8K citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
26 h-index
142 publications
8.8K citations
Technion – Israel Institute of Technology
Haifa, Israel
25 h-index
63 publications
9.8K citations

University of British Columbia
Vancouver, Canada
24 h-index
78 publications
4.8K citations
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MRM · 119 publications · 9.9K citations
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67 publications · 12.8K citations
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49 publications · 36.2K citations
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35 publications · 7.7K citations
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Repository · 82 publications · 2.5K citations
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