  Deep learning networks have shown promising results in fast magnetic
resonance imaging (MRI) reconstruction. In our work, we develop deep networks
to further improve the quantitative and the perceptual quality of
reconstruction. To begin with, we propose reconsynergynet (RSN), a network that
combines the complementary benefits of independently operating on both the
image and the Fourier domain. For a single-coil acquisition, we introduce deep
cascade RSN (DC-RSN), a cascade of RSN blocks interleaved with data fidelity
(DF) units. Secondly, we improve the structure recovery of DC-RSN for T2
weighted Imaging (T2WI) through assistance of T1 weighted imaging (T1WI), a
sequence with short acquisition time. T1 assistance is provided to DC-RSN
through a gradient of log feature (GOLF) fusion. Furthermore, we propose
perceptual refinement network (PRN) to refine the reconstructions for better
visual information fidelity (VIF), a metric highly correlated to radiologists
opinion on the image quality. Lastly, for multi-coil acquisition, we propose
variable splitting RSN (VS-RSN), a deep cascade of blocks, each block
containing RSN, multi-coil DF unit, and a weighted average module. We
extensively validate our models DC-RSN and VS-RSN for single-coil and
multi-coil acquisitions and report the state-of-the-art performance. We obtain
a SSIM of 0.768, 0.923, 0.878 for knee single-coil-4x, multi-coil-4x, and
multi-coil-8x in fastMRI. We also conduct experiments to demonstrate the
efficacy of GOLF based T1 assistance and PRN.
