Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks Image reconstruction from undersampled kspace data has been playing an important role in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated tremendous success in various fields and also shown potential in significantly accelerating MRI reconstruction with fewer measurements. This article provides an overview of deeplearningbased image reconstruction methods for MRI. Two types of deeplearningbased approaches are reviewed, those that are based on unrolled algorithms and those that are not, and the main structures of both are explained. Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed, which may facilitate further development of the networks and performance analysis from a theoretical point of view.

DeepLearning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues Following the success of deep learning in a wide range of applications, neural networkbased machinelearning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deeplearning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for both lowdose computed tomography and accelerated MRI. The additional integration of multicoil information to recover missing kspace lines in the MRI reconstruction process is studied less frequently, even though it is the de facto standard for the currently used accelerated MR acquisitions. This article provides an overview of the recent machinelearning approaches that have been proposed specifically for improving parallel imaging. A general background introduction to parallel MRI is given and structured around the classical view of image and kspacebased methods. Linear and nonlinear methods are covered, followed by a discussion of the recent efforts to further improve parallel imaging using machine learning and, specifically, artificial neural networks. Image domainbased techniques that introduce improved regularizers are covered as well as kspacebased methods, where the focus is on better interpolation strategies using neural networks. Issues and open problems are discussed and recent efforts for producing open data sets and benchmarks for the community are examined.