Deep learning for accelerated and robust MRI reconstruction

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and archi...

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Veröffentlicht in:Magma (New York, N.Y.) Jg. 37; H. 3; S. 335 - 368
Hauptverfasser: Heckel, Reinhard, Jacob, Mathews, Chaudhari, Akshay, Perlman, Or, Shimron, Efrat
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.07.2024
Schlagworte:
Basic Science - Reconstruction algorithms and artificial intelligence
Biomedical Engineering and Bioengineering
Computer Appl. in Life Sciences
Health Informatics
Imaging
Medicine
Medicine & Public Health
Radiology
Review
Solid State Physics
Deep learning
MRI
Image reconstruction
Machine learning
ISSN:1352-8661, 0968-5243, 1352-8661
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Abstract Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.
AbstractList Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.Affiliations [3 and 6] has been split into two different affiliations. Please check if action taken is appropriate and amend if necessary.looks good.
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.
Author Chaudhari, Akshay
Heckel, Reinhard
Shimron, Efrat
Jacob, Mathews
Perlman, Or
Author_xml – sequence: 1
  givenname: Reinhard
  surname: Heckel
  fullname: Heckel, Reinhard
  organization: Department of computer engineering, Technical University of Munich
– sequence: 2
  givenname: Mathews
  surname: Jacob
  fullname: Jacob, Mathews
  organization: Department of Electrical and Computer Engineering, University of Iowa
– sequence: 3
  givenname: Akshay
  surname: Chaudhari
  fullname: Chaudhari, Akshay
  organization: Department of Radiology, Stanford University, Department of Biomedical Data Science, Stanford University
– sequence: 4
  givenname: Or
  surname: Perlman
  fullname: Perlman, Or
  organization: Department of Biomedical Engineering, Tel Aviv University, Sagol School of Neuroscience, Tel Aviv University
– sequence: 5
  givenname: Efrat
  orcidid: 0000-0002-2267-0561
  surname: Shimron
  fullname: Shimron, Efrat
  email: efrat.s@technion.ac.il
  organization: Department of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Department of Biomedical Engineering, Technion-Israel Institute of Technology
BackLink https://www.ncbi.nlm.nih.gov/pubmed/39042206$$D View this record in MEDLINE/PubMed
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ISSN 1352-8661
0968-5243
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Sun Nov 09 12:01:24 EST 2025
Mon Jul 21 06:04:24 EDT 2025
Sat Nov 29 03:00:50 EST 2025
Tue Nov 18 22:24:32 EST 2025
Fri Feb 21 02:38:25 EST 2025
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Issue 3
Keywords Deep learning
MRI
Image reconstruction
Machine learning
Language English
License 2024. The Author(s).
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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PublicationSubtitle Official Journal of the European Society for Magnetic Resonance in Medicine and Biology
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Snippet Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This...
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SubjectTerms Basic Science - Reconstruction algorithms and artificial intelligence
Biomedical Engineering and Bioengineering
Computer Appl. in Life Sciences
Health Informatics
Imaging
Medicine
Medicine & Public Health
Radiology
Review
Solid State Physics
Title Deep learning for accelerated and robust MRI reconstruction
URI https://link.springer.com/article/10.1007/s10334-024-01173-8
https://www.ncbi.nlm.nih.gov/pubmed/39042206
https://www.proquest.com/docview/3083682113
https://pubmed.ncbi.nlm.nih.gov/PMC11316714
Volume 37
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