University of Oulu

Montalt-Tordera, J., Muthurangu, V., Hauptmann, A., & Steeden, J. A. (2021). Machine learning in Magnetic Resonance Imaging: Image reconstruction. Physica Medica, 83, 79–87. https://doi.org/10.1016/j.ejmp.2021.02.020

Machine learning in Magnetic Resonance Imaging : image reconstruction

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Author: Montalt-Tordera, Javier1; Muthurangu, Vivek1; Hauptmann, Andreas2,3;
Organizations: 1UCL Centre for Cardiovascular Imaging, University College London, London WC1N 1EH, United Kingdom
2University of Oulu, Research Unit of Mathematical Sciences, Oulu, Finland
3Department of Computer Science, University College London, London WC1E 6BT, United Kingdom
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe202103197889
Language: English
Published: Elsevier, 2021
Publish Date: 2022-03-13
Description:

Abstract

Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction.

A wide range of approaches have been proposed, which can be applied in k-space and/or image-space.

Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation.

In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends.

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Series: Physica medica
ISSN: 1120-1797
ISSN-E: 1724-191X
ISSN-L: 1120-1797
Volume: 83
Pages: 79 - 87
DOI: 10.1016/j.ejmp.2021.02.020
OADOI: https://oadoi.org/10.1016/j.ejmp.2021.02.020
Type of Publication: A2 Review article in a scientific journal
Field of Science: 113 Computer and information sciences
217 Medical engineering
Subjects:
Copyright information: © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http:/creativecommons.org/licenses/by-nc-nd/4.0/
  https://creativecommons.org/licenses/by-nc-nd/4.0/