Artificial intelligence in cardiac magnetic resonance fingerprinting
Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.1009131/full |
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| author | Carlos Velasco Thomas J. Fletcher René M. Botnar René M. Botnar René M. Botnar Claudia Prieto Claudia Prieto Claudia Prieto |
| author_facet | Carlos Velasco Thomas J. Fletcher René M. Botnar René M. Botnar René M. Botnar Claudia Prieto Claudia Prieto Claudia Prieto |
| author_sort | Carlos Velasco |
| collection | DOAJ |
| description | Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction. |
| first_indexed | 2024-04-11T09:59:24Z |
| format | Article |
| id | doaj.art-4e3ed586ff5545c5b64dfbb21e3b163d |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-11T09:59:24Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-4e3ed586ff5545c5b64dfbb21e3b163d2022-12-22T04:30:27ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-09-01910.3389/fcvm.2022.10091311009131Artificial intelligence in cardiac magnetic resonance fingerprintingCarlos Velasco0Thomas J. Fletcher1René M. Botnar2René M. Botnar3René M. Botnar4Claudia Prieto5Claudia Prieto6Claudia Prieto7School of Biomedical Engineering and Imaging Sciences, King's College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United KingdomInstitute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, ChileMillennium Institute for Intelligent Healthcare Engineering, Santiago, ChileSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, United KingdomInstitute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, ChileMillennium Institute for Intelligent Healthcare Engineering, Santiago, ChileMagnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.https://www.frontiersin.org/articles/10.3389/fcvm.2022.1009131/fullmagnetic resonance fingerprinting (MRF)artificial intelligence (AI)cardiac MRFmultiparametric imagingcardiac magnetic resonance (CMR) |
| spellingShingle | Carlos Velasco Thomas J. Fletcher René M. Botnar René M. Botnar René M. Botnar Claudia Prieto Claudia Prieto Claudia Prieto Artificial intelligence in cardiac magnetic resonance fingerprinting Frontiers in Cardiovascular Medicine magnetic resonance fingerprinting (MRF) artificial intelligence (AI) cardiac MRF multiparametric imaging cardiac magnetic resonance (CMR) |
| title | Artificial intelligence in cardiac magnetic resonance fingerprinting |
| title_full | Artificial intelligence in cardiac magnetic resonance fingerprinting |
| title_fullStr | Artificial intelligence in cardiac magnetic resonance fingerprinting |
| title_full_unstemmed | Artificial intelligence in cardiac magnetic resonance fingerprinting |
| title_short | Artificial intelligence in cardiac magnetic resonance fingerprinting |
| title_sort | artificial intelligence in cardiac magnetic resonance fingerprinting |
| topic | magnetic resonance fingerprinting (MRF) artificial intelligence (AI) cardiac MRF multiparametric imaging cardiac magnetic resonance (CMR) |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.1009131/full |
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