  The performance of traditional compressive sensing-based MRI (CS-MRI)
reconstruction is affected by its slow iterative procedure and noise-induced
artefacts. Although many deep learning-based CS-MRI methods have been proposed
to mitigate the problems of traditional methods, they have not been able to
achieve more robust results at higher acceleration factors. Most of the deep
learning-based CS-MRI methods still can not fully mine the information from the
k-space, which leads to unsatisfactory results in the MRI reconstruction. In
this study, we propose a new deep learning-based CS-MRI reconstruction method
to fully utilise the relationship among sequential MRI slices by coupling
Wasserstein Generative Adversarial Networks (WGAN) with Recurrent Neural
Networks. Further development of an attentive unit enables our model to
reconstruct more accurate anatomical structures for the MRI data. By
experimenting on different MRI datasets, we have demonstrated that our method
can not only achieve better results compared to the state-of-the-arts but can
also effectively reduce residual noise generated during the reconstruction
process.
