  Compressive sensing magnetic resonance imaging (CS-MRI) accelerates the
acquisition of MR images by breaking the Nyquist sampling limit. In this work,
a novel generative adversarial network (GAN) based framework for CS-MRI
reconstruction is proposed. Leveraging a combination of patch-based
discriminator and structural similarity index based loss, our model focuses on
preserving high frequency content as well as fine textural details in the
reconstructed image. Dense and residual connections have been incorporated in a
U-net based generator architecture to allow easier transfer of information as
well as variable network length. We show that our algorithm outperforms
state-of-the-art methods in terms of quality of reconstruction and robustness
to noise. Also, the reconstruction time, which is of the order of milliseconds,
makes it highly suitable for real-time clinical use.
