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Compressed sensing revolutionized magnetic resonance imaging (MRI) reconstruction, achieving FDA approval for clinical use. This review surveys key models and optimization algorithms, including advanced data-adaptive regularizers for MRI.

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Area of Science:

  • Medical Imaging
  • Computational Science
  • Signal Processing

Background:

  • Compressed sensing methods significantly advanced magnetic resonance (MR) image reconstruction.
  • These techniques have seen widespread research and development over the past decade.
  • Compressed sensing has achieved clinical success, evidenced by U.S. Food and Drug Administration (FDA) approval.

Purpose of the Study:

  • To review key models and optimization algorithms for MR image reconstruction.
  • To cover methods with FDA approval and emerging data-adaptive regularizer techniques.
  • To consolidate a survey of algorithms exploiting system model and regularizer structures in MRI.

Main Methods:

  • Review of established compressed sensing models for MRI.
  • Analysis of optimization algorithms used in MR image reconstruction.
  • Inclusion of recent research on data-adaptive regularizers.

Main Results:

  • Identification of critical models and algorithms in compressed sensing for MRI.
  • Highlighting the transition of compressed sensing from research to clinical application.
  • Categorization of algorithms based on their exploitation of MRI system properties.

Conclusions:

  • Compressed sensing represents a major advancement and clinical success in MRI.
  • The field continues to evolve with new data-adaptive regularization methods.
  • This review provides a comprehensive overview of algorithms for MR image reconstruction.