  Generative Adversarial Networks (GANs) are powerful tools for reconstructing
Compressed Sensing Magnetic Resonance Imaging (CS-MRI). However most recent
works lack exploration of structure information of MRI images that is crucial
for clinical diagnosis. To tackle this problem, we propose the
Structure-Enhanced GAN (SEGAN) that aims at restoring structure information at
both local and global scale. SEGAN defines a new structure regularization
called Patch Correlation Regularization (PCR) which allows for efficient
extraction of structure information. In addition, to further enhance the
ability to uncover structure information, we propose a novel generator SU-Net
by incorporating multiple-scale convolution filters into each layer. Besides,
we theoretically analyze the convergence of stochastic factors contained in
training process. Experimental results show that SEGAN is able to learn target
structure information and achieves state-of-the-art performance for CS-MRI
reconstruction.
