Deep learning for fast low-field MRI acquisitions
Abstract Low-field (LF) MRI research currently gains momentum from its potential to offer reduced costs and reduced footprints translating into wider accessibility. However, the impeded signal-to-noise ratio inherent to lower magnetic fields can have a significant impact on acquisition times that ch...
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Format: | Article |
Language: | English |
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Nature Portfolio
2022-07-01
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Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-022-14039-7 |
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author | Reina Ayde Tobias Senft Najat Salameh Mathieu Sarracanie |
author_facet | Reina Ayde Tobias Senft Najat Salameh Mathieu Sarracanie |
author_sort | Reina Ayde |
collection | DOAJ |
description | Abstract Low-field (LF) MRI research currently gains momentum from its potential to offer reduced costs and reduced footprints translating into wider accessibility. However, the impeded signal-to-noise ratio inherent to lower magnetic fields can have a significant impact on acquisition times that challenges LF clinical relevance. Undersampling is an effective way to speed up acquisitions in MRI, and recent work has shown encouraging results when combined with deep learning (DL). Yet, training DL models generally requires large databases that are not yet available at LF regimes. Here, we demonstrate the capability of Residual U-net combined with data augmentation to reconstruct magnitude and phase information of undersampled LF MRI scans at 0.1 T with a limited training dataset (n = 10). The model performance was first evaluated in a retrospective study for different acceleration rates and sampling patterns. Ultimately, the DL approach was validated on prospectively acquired, fivefold undersampled LF data. With varying performances associated to the adopted sampling scheme, our results show that the approach investigated can preserve the global structure and the details sharpness in the reconstructed magnitude and phase images. Overall, promising results could be obtained on acquired LF MR images that may bring this research closer to clinical implementation. |
first_indexed | 2024-12-11T15:32:24Z |
format | Article |
id | doaj.art-7ea6e75f44c24f2a970fdd245624b61d |
institution | Directory Open Access Journal |
issn | 2045-2322 |
language | English |
last_indexed | 2024-12-11T15:32:24Z |
publishDate | 2022-07-01 |
publisher | Nature Portfolio |
record_format | Article |
series | Scientific Reports |
spelling | doaj.art-7ea6e75f44c24f2a970fdd245624b61d2022-12-22T01:00:02ZengNature PortfolioScientific Reports2045-23222022-07-0112111310.1038/s41598-022-14039-7Deep learning for fast low-field MRI acquisitionsReina Ayde0Tobias Senft1Najat Salameh2Mathieu Sarracanie3Center for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of BaselCenter for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of BaselCenter for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of BaselCenter for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of BaselAbstract Low-field (LF) MRI research currently gains momentum from its potential to offer reduced costs and reduced footprints translating into wider accessibility. However, the impeded signal-to-noise ratio inherent to lower magnetic fields can have a significant impact on acquisition times that challenges LF clinical relevance. Undersampling is an effective way to speed up acquisitions in MRI, and recent work has shown encouraging results when combined with deep learning (DL). Yet, training DL models generally requires large databases that are not yet available at LF regimes. Here, we demonstrate the capability of Residual U-net combined with data augmentation to reconstruct magnitude and phase information of undersampled LF MRI scans at 0.1 T with a limited training dataset (n = 10). The model performance was first evaluated in a retrospective study for different acceleration rates and sampling patterns. Ultimately, the DL approach was validated on prospectively acquired, fivefold undersampled LF data. With varying performances associated to the adopted sampling scheme, our results show that the approach investigated can preserve the global structure and the details sharpness in the reconstructed magnitude and phase images. Overall, promising results could be obtained on acquired LF MR images that may bring this research closer to clinical implementation.https://doi.org/10.1038/s41598-022-14039-7 |
spellingShingle | Reina Ayde Tobias Senft Najat Salameh Mathieu Sarracanie Deep learning for fast low-field MRI acquisitions Scientific Reports |
title | Deep learning for fast low-field MRI acquisitions |
title_full | Deep learning for fast low-field MRI acquisitions |
title_fullStr | Deep learning for fast low-field MRI acquisitions |
title_full_unstemmed | Deep learning for fast low-field MRI acquisitions |
title_short | Deep learning for fast low-field MRI acquisitions |
title_sort | deep learning for fast low field mri acquisitions |
url | https://doi.org/10.1038/s41598-022-14039-7 |
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