Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

TL;DR

This paper reviews recent deep learning techniques for enhancing parallel MRI reconstruction, focusing on neural network applications in image and k-space domains, and discusses open challenges and community benchmarks.

Contribution

It provides a comprehensive overview of machine learning approaches for parallel MRI, highlighting recent advances and open problems in the field.

Findings

Deep learning improves MRI reconstruction quality.

Neural networks enhance k-space interpolation strategies.

Open datasets and benchmarks are emerging for community use.

Abstract

Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deep learning techniques from computer vision and image processing have been successfully applied to non-linear image reconstruction in the spirit of compressed sensing for both low dose computed tomography and accelerated MRI. The additional integration of multi-coil information to recover missing k-space lines in the MRI reconstruction process, is still studied less frequently, even though it is the de-facto standard for currently used accelerated MR acquisitions. This manuscript provides an overview of the recent machine learning approaches that have been proposed specifically for improving parallel imaging. A general background introduction to parallel MRI is given that is structured around the classical view of image space and k-space based methods. Both linear and non-linear methods are covered, followed by a discussion of recent efforts to further improve parallel imaging using machine learning, and specifically using artificial neural networks. Image-domain based techniques that introduce improved regularizers are covered as well as k-space based methods, where the focus is on better interpolation strategies using neural networks. Issues and open problems are discussed as well as recent efforts for producing open datasets and benchmarks for the community.