A wide multimodal dense U-net for fast magnetic resonance imaging

In this paper, a deep learning method for accelerating magnetic resonance imaging (MRI) is presented, which is able to reconstruct undersampled MR images obtained by reducing the k-space data in the direction of the phase encoding. In particular, we focus on the reconstruction of MR images related to patients affected by multiple sclerosis (MS) and we propose a new multimodal deep learning architecture that is able to exploit the joint information deriving from the combination of different types of MR images and to accelerate the MRI, while providing high quality of the reconstructed image. Experimental results show the performance improvement of the proposed method with respect to existing models in reconstructing images with an MRI acceleration of 4 times.