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.

2.5K

Publications

224K

Citations

6.5K

Authors

1.6K

Institutions

Publications per year

2017–2026

925

Variants

Cs Reconstruction

Authors

6.5K

  1. RG
    Richard G. Baraniuk

    Rice University, United States

    36 publications · 14.4K citations

    27

    h-index

  2. XY
    Xin Yuan

    Westlake University, China

    26 publications · 2.3K citations

    20

    h-index

  3. YC
    Yonina C. Eldar

    Technion – Israel Institute of Technology, Israel

    23 publications · 4.5K citations

    19

    h-index

  4. DJ
    David J. Brady

    Duke University, United States

    18 publications · 2.8K citations

    17

    h-index

  5. RO
    Ricardo Otazo

    New York University, United States

    17 publications · 2.2K citations

    16

    h-index

1–5 of 6.5K

Venues

  1. Magnetic Resonance in Medicine

    MRM · 119 publications · 9.9K citations

    44

    h-index

  2. IEEE Transactions on Signal Processing

    67 publications · 12.8K citations

    42

    h-index

  3. IEEE Transactions on Information Theory

    49 publications · 36.2K citations

    37

    h-index

  4. IEEE Transactions on Medical Imaging

    35 publications · 7.7K citations

    28

    h-index

  5. arXiv (Cornell University)

    Repository · 82 publications · 2.5K citations

    26

    h-index

1–5