Data-driven Motion Correction

Data-Driven Motion Correction is a motion correction methodology that derives corrective parameters directly from the acquired data itself, without reliance on external tracking devices or prior models, by leveraging inherent statistical or structural patterns within the data to estimate and compensate for motion-induced distortions.

23

Publications

857

Citations

108

Authors

54

Institutions

Publications per year

2017–2026

13

Authors

108

  1. DD
    Doumit Daou

    Assistance Publique – Hôpitaux de Paris, France

    2 publications · 32 citations

    2

    h-index

  2. EM
    Enette Mae Revilla

    Yale University, United States

    2 publications · 65 citations

    2

    h-index

  3. RS
    R. Sabbah

    Université Paris Cité, France

    2 publications · 32 citations

    2

    h-index

  4. MN
    Mika Naganawa

    Yale University, United States

    2 publications · 65 citations

    2

    h-index

  5. YL
    Yihuan Lu

    Yale University, United States

    2 publications · 65 citations

    2

    h-index

1–5 of 108

Venues

  1. Journal of Nuclear Cardiology

    JNC · 3 publications · 55 citations

    3

    h-index

  2. Journal of Nuclear Medicine

    3 publications · 109 citations

    3

    h-index

  3. Medical Physics

    2 publications · 42 citations

    2

    h-index

  4. IEEE Transactions on Medical Imaging

    2 publications · 105 citations

    2

    h-index

  5. NeuroImage

    2 publications · 125 citations

    2

    h-index

1–5