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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Vydáno v:Magma (New York, N.Y.) Ročník 37; číslo 3; s. 335 - 368
Hlavní autoři: Heckel, Reinhard, Jacob, Mathews, Chaudhari, Akshay, Perlman, Or, Shimron, Efrat
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.07.2024
Témata:
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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Shrnutí: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.
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ISSN:1352-8661
0968-5243
1352-8661
DOI:10.1007/s10334-024-01173-8