  Parallel imaging accelerates MRI data acquisition by acquiring additional
sensitivity information with an array of receiver coils, resulting in fewer
phase encoding steps. Because of fewer data requirements than parallel imaging,
compressed sensing magnetic resonance imaging (CS-MRI) has gained popularity in
the field of medical imaging. Parallel imaging and compressed sensing (CS) both
reduce the amount of data captured in the k-space, which speeds up traditional
MRI acquisition. As acquisition time is inversely proportional to sample count,
forming an image from reduced k-space samples results in faster acquisition but
with aliasing artifacts. For de-aliasing the reconstructed image, this paper
proposes a novel Generative Adversarial Network (GAN) called RECGAN-GR that is
supervised with multi-modal losses. In comparison to existing GAN networks, our
proposed method introduces a novel generator network, RemU-Net, which is
integrated with dual-domain loss functions such as weighted magnitude and phase
loss functions, as well as parallel imaging-based loss, GRAPPA consistency
loss. As refinement learning, a k-space correction block is proposed to make
the GAN network self-resistant to generating unnecessary data, which speeds up
the reconstruction process. Comprehensive results show that the proposed
RECGAN-GR not only improves the PSNR by 4 dB over GAN-based methods but also by
2 dB over conventional state-of-the-art CNN methods available in the literature
for single-coil data. The proposed work significantly improves image quality
for low-retained data, resulting in five to ten times faster acquisition.
