DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working…

• BH
  British Heart Foundation

  Award: PG/16/78/32402
• UO
  University of Nottingham

  Award: EP/K503800/1
• NC
  National Centre for the Replacement, Refinement and Reduction of Animals in Research

  Award: NC/L001861/1
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/M020533/1, EP/K503800/1, EP/K503800/1, NC/L001861/1, NC/L001861/1