Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks

TL;DR

This paper reviews deep learning methods for MRI image reconstruction, focusing on three approaches—data-driven, model-driven, and integrated—and discusses challenges and future directions for optimizing these techniques.

Contribution

It provides a comprehensive overview of deep learning-based MRI reconstruction methods, analyzing their structures, commonalities, differences, and potential for further development.

Findings

Analysis of three main deep learning approaches for MRI reconstruction

Discussion of signal processing issues to improve reconstruction quality

Insights into future research directions for optimal network design

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.