A comparative study of CNN-based super-resolution methods in MRI reconstruction and its beyond

Abstract

The progress of convolution neural network (CNN) based super-resolution has shown its potential in image processing community. Meanwhile, Compressed Sensing MRI (CS-MRI) provides the possibility to accelerate the traditional acquisition process of MRI. In this work, on the basis of decomposing the cascade network to be a series of alternating CNN-based sub-network and data-consistency sub-network, we investigate the performance of the cascade networks in CS-MRI by employing various CNN-based super-resolution methods in the CNN-based sub-network. Furthermore, recognizing that existing methods of exploring dense connection in the CNN-based sub-network are insufficient to utilize the feature information, we propose a dense connected cascade network for more accurate MRI reconstruction. Specifically, the proposed network densely connects both CNN-based sub-network and data-consistency sub-network, thus takes advantage of the data-consistency of k-space data in a densely connected fashion. Experimental results on various MR data demonstrated that the proposed network is superior to current cascade networks in reconstruction quality.