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.

1.7K

Publications

112.5K

Citations

7.1K

Authors

1.5K

Institutions

Publications per year

2017–2026

1.6K

Authors

7.1K

  1. XY
    Xin Yuan

    Westlake University, China

    18 publications · 1.5K citations

    15

    h-index

  2. DN
    Dominik Nickel

    Siemens Healthineers (Germany), Germany

    13 publications · 551 citations

    13

    h-index

  3. GW
    Ge Wang

    Rensselaer Polytechnic Institute, United States

    15 publications · 1.9K citations

    13

    h-index

  4. JM
    John M. Pauly

    Stanford University, United States

    12 publications · 972 citations

    11

    h-index

  5. JC
    Jong Chul Ye

    Korea Advanced Institute of Science and Technology, South Korea

    12 publications · 1.7K citations

    11

    h-index

1–5 of 7.1K

Venues

  1. IEEE Transactions on Medical Imaging

    57 publications · 7.6K citations

    37

    h-index

  2. arXiv (Cornell University)

    Repository · 112 publications · 3.6K citations

    31

    h-index

  3. Optics Express

    58 publications · 2.8K citations

    30

    h-index

  4. European Radiology

    41 publications · 2.1K citations

    25

    h-index

  5. 25

    h-index

1–5