Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks

--Image reconstruction from undersampled k - space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and has also shown potential to significantly speed up MRI reconstruction with reduced measurements. This article gives an overview of deep learning -based image reconstruction methods for MRI. Three types of deep learning -based approaches are reviewed, the data - driven, model - driven and integrated approaches. T he main structure of each network in three approaches is explained and the analysis of common parts of reviewed networks and differences in - between are highlighted. Based on the review, a number of signal processing issues are discussed for maximizing the potential of deep reconstruction for fast MRI. The discussion may facilitate further development of "optimal" network and performance analysis from a theoretical point of view. I. INTRODUCTION Since its inception in the early 70's, magnetic resonance imaging (MRI) has revolutionized radiology and medicine. However, MRI is known to be a slow imaging modality and many techniques have been devel oped to reconstruct the desired image from undersampled measured data to improve the imaging speed [1]. During the past decades, compressed sensing (CS) has become an important strategy for fast MR imaging based on the sparsity prior. However, the iterative solution procedure takes a relatively long time to achieve a high -quality reconstruction, and the selection of the regularization parameter is empirical.