  MRI is an inherently slow process, which leads to long scan time for
high-resolution imaging. The speed of acquisition can be increased by ignoring
parts of the data (undersampling). Consequently, this leads to the degradation
of image quality, such as loss of resolution or introduction of image
artefacts. This work aims to reconstruct highly undersampled Cartesian or
radial MR acquisitions, with better resolution and with less to no artefact
compared to conventional techniques like compressed sensing. In recent times,
deep learning has emerged as a very important area of research and has shown
immense potential in solving inverse problems, e.g. MR image reconstruction. In
this paper, a deep learning based MR image reconstruction framework is
proposed, which includes a modified regularised version of ResNet as the
network backbone to remove artefacts from the undersampled image, followed by
data consistency steps that fusions the network output with the data already
available from undersampled k-space in order to further improve reconstruction
quality. The performance of this framework for various undersampling patterns
has also been tested, and it has been observed that the framework is robust to
deal with various sampling patterns, even when mixed together while training,
and results in very high quality reconstruction, in terms of high SSIM (highest
being 0.990$\pm$0.006 for acceleration factor of 3.5), while being compared
with the fully sampled reconstruction. It has been shown that the proposed
framework can successfully reconstruct even for an acceleration factor of 20
for Cartesian (0.968$\pm$0.005) and 17 for radially (0.962$\pm$0.012) sampled
data. Furthermore, it has been shown that the framework preserves brain
pathology during reconstruction while being trained on healthy subjects.
