A review of deep learning methods for MRI reconstruction

09/17/2021
by   Arghya Pal, et al.
21

Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI. Given the rapidly growing nature of the field, it is imperative to consolidate and summarize the large number of deep learning methods that have been reported in the literature, to obtain a better understanding of the field in general. This article provides an overview of the recent developments in neural-network based approaches that have been proposed specifically for improving parallel imaging. A general background and introduction to parallel MRI is also given from a classical view of k-space based reconstruction methods. Image domain based techniques that introduce improved regularizers are covered along with k-space based methods which focus on better interpolation strategies using neural networks. While the field is rapidly evolving with thousands of papers published each year, in this review, we attempt to cover broad categories of methods that have shown good performance on publicly available data sets. Limitations and open problems are also discussed and recent efforts for producing open data sets and benchmarks for the community are examined.

READ FULL TEXT

Authors

page 2

page 18

page 21

page 23

page 24

page 26

page 27

page 31

04/01/2019

Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction

Following the success of deep learning in a wide range of applications, ...
12/09/2020

Machine Learning in Magnetic Resonance Imaging: Image Reconstruction

Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, manage...
03/20/2019

Plug and play methods for magnetic resonance imaging

Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that ...
03/23/2022

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging

Physics-driven deep learning methods have emerged as a powerful tool for...
07/26/2019

Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks

Image reconstruction from undersampled k-space data has been playing an ...
04/14/2020

End-to-End Variational Networks for Accelerated MRI Reconstruction

The slow acquisition speed of magnetic resonance imaging (MRI) has led t...
06/24/2020

MRI Image Reconstruction via Learning Optimization using Neural ODEs

We propose to formulate MRI image reconstruction as an optimization prob...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.