Self-supervised learning for MRI reconstruction: a review and new perspective.

Journal: Magma (New York, N.Y.)
Published Date: Jun 26, 2025

Abstract

OBJECTIVE: To review the latest developments in self-supervised deep learning (DL) techniques for magnetic resonance imaging (MRI) reconstruction, emphasizing their potential to overcome the limitations of supervised methods dependent on fully sampled k-space data.

Authors

  • Xinzhen Li
    School of Mathematics and Computer Science, Gannan Normal University, China.
  • Jinhong Huang
    School of Mathemtics and Computer Science, Gannan Normal University, China. Electronic address: hjhmaths@163.com.
  • Guanglong Sun
    School of Mathematics and Computer Science, Gannan Normal University, No.1 Shida South Road, Rongjiang New Area, Ganzhou, 341000, Jiangxi, China.
  • Zihan Yang
    Institute of Modern Optics, Nankai University, Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Tianjin, 300350, China.

Keywords

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