Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks

Abstract

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 also shown potential to significantly speed up MR 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. The 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.

arXiv commenta review paper on deep learning MR reconstruction

References in corpus (4)

• Image reconstruction by domain transform manifold learning
• Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
• MR fingerprinting Deep RecOnstruction NEtwork (DRONE)
• Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease

Cited by in corpus (4)

• Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data
• Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning
• MRI Image Reconstruction via Learning Optimization Using Neural ODEs
• Learning Illumination Patterns for Coded Diffraction Phase Retrieval