Super-resolved reconstruction method for spatiotemporally encoded magnetic resonance imaging based on deep neural network

Abstract

Single-shot spatiotemporally-encoded magnetic resonance imaging (SPEN MRI) is a novel ultrafast MRI technology. The SPEN MRI possesses great resistance to inhomogeneous <i>B</i>₀ magnetic field and chemical shift effect. However, it has inherently low spatial resolution, and the super-resolved reconstruction is required to improve the spatial resolution of SPEN MRI image without additional signal acquisition. Several super-resolved reconstruction methods have been proposed, but they all suffer the problems of long iterative solution time and/or aliasing artifacts residue in the reconstructed results. In this paper, a super-resolved reconstruction method is proposed for single-shot SPEN MRI based on deep neural network. In this method the simulation samples are used to train the deep neural network, and then the trained network model is adopted to reconstruct the real sampled signals. Experimental results of numerical simulation, water phantom and in vivo rat brain show that this method can quickly reconstruct a super-resolved SPEN image with no residual aliasing artifacts, and clear texture information. An appropriate number of training samples and an appropriate random noise level for training samples contribute to improving the reconstruction results.