Conventional Magnetic Resonance Imaging (MRI) is a prolonged procedure. Therefore, it’s beneficial to reduce scan time as it improves patient experience and reduces scanning cost. While many approaches have been proposed for obtaining high quality reconstruction images using under-sampled k-space data, deep learning has started to show promising results when compared with conventional methods. In this paper, we propose a Variational Feedback Network (VFN) for accelerated MRI reconstruction. Specifically, we extend the previously proposed variational network with recurrent neural network (RNN). Quantitative and qualitative evaluations demonstrate that our proposed model performs superiorly against other compared methods on MRI reconstruction.
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