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

Authors

Li X,Huang J,Sun G,Yang Z

Affiliations (2)

  • School of Mathematics and Computer Science, Gannan Normal University, No.1 Shida South Road, Rongjiang New Area, Ganzhou, 341000, Jiangxi, China.
  • School of Mathematics and Computer Science, Gannan Normal University, No.1 Shida South Road, Rongjiang New Area, Ganzhou, 341000, Jiangxi, China. [email protected].

Abstract

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. While DL has significantly advanced MRI, supervised approaches require large amounts of fully sampled k-space data for training-a major limitation given the impracticality and expense of acquiring such data clinically. Self-supervised learning has emerged as a promising alternative, enabling model training using only undersampled k-space data, thereby enhancing feasibility and driving research interest. We conducted a comprehensive literature review to synthesize recent progress in self-supervised DL for MRI reconstruction. The analysis focused on methods and architectures designed to improve image quality, reduce scanning time, and address data scarcity challenges, drawing from peer-reviewed publications and technical innovations in the field. Self-supervised DL holds transformative potential for MRI reconstruction, offering solutions to data limitations while maintaining image quality and accelerating scans. Key challenges include robustness across diverse anatomies, standardization of validation, and clinical integration. Future research should prioritize hybrid methodologies, domain-specific adaptations, and rigorous clinical validation. This review consolidates advancements and unresolved issues, providing a foundation for next-generation medical imaging technologies.

Topics

Journal ArticleReview

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.