Journal article
Deep learning for accelerated and robust MRI reconstruction
Magma (New York, N.Y.), Vol.37(3), pp.335-368
07/23/2024
DOI: 10.1007/s10334-024-01173-8
PMCID: PMC11316714
PMID: 39042206
Abstract
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.Affiliations [3 and 6] has been split into two different affiliations. Please check if action taken is appropriate and amend if necessary.looks good
Details
- Title: Subtitle
- Deep learning for accelerated and robust MRI reconstruction
- Creators
- Reinhard Heckel - Technical University of MunichMathews Jacob - University of IowaAkshay Chaudhari - Stanford UniversityOr Perlman - Tel Aviv UniversityEfrat Shimron - Technion – Israel Institute of Technology
- Resource Type
- Journal article
- Publication Details
- Magma (New York, N.Y.), Vol.37(3), pp.335-368
- DOI
- 10.1007/s10334-024-01173-8
- PMID
- 39042206
- PMCID
- PMC11316714
- ISSN
- 1352-8661
- eISSN
- 1352-8661
- Language
- English
- Electronic publication date
- 07/23/2024
- Academic Unit
- Electrical and Computer Engineering; Iowa Neuroscience Institute
- Record Identifier
- 9984688448502771
Metrics
6 Record Views