Deep-learning-based super-resolution for accelerating chemical exchange saturation transfer MRI
© 2024 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd..
Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high-resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high-resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep-learning-based super-resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR-CEST, to reduce the acquisition time for CEST MRI by reconstructing high-resolution images from fast low-resolution acquisitions. This is achieved by first pretraining the DLSR-CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST datasets to fine-tune the weights. Using the trained DLSR-CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal-to-noise ratio and structural similarity index measure metrics at all downsampling factors (2-8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR-CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z-spectrum information. Therefore, our DLSR-CEST demonstrated a robust reconstruction of high-resolution CEST source images from fast low-resolution acquisitions, thereby improving the spatial resolution and preserving most Z-spectrum information.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
NMR in biomedicine - (2024) vom: 15. März, Seite e5130 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Pemmasani Prabakaran, Rohith Saai [VerfasserIn] |
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| Links: |
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| Themen: |
Acquisition time |
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| Anmerkungen: |
Date Revised 16.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1002/nbm.5130 |
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NLM369814312 |
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| 520 | |a Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high-resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high-resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep-learning-based super-resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR-CEST, to reduce the acquisition time for CEST MRI by reconstructing high-resolution images from fast low-resolution acquisitions. This is achieved by first pretraining the DLSR-CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST datasets to fine-tune the weights. Using the trained DLSR-CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal-to-noise ratio and structural similarity index measure metrics at all downsampling factors (2-8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR-CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z-spectrum information. Therefore, our DLSR-CEST demonstrated a robust reconstruction of high-resolution CEST source images from fast low-resolution acquisitions, thereby improving the spatial resolution and preserving most Z-spectrum information | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a amide CEST (amideCEST) | |
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| 650 | 4 | |a chemical exchange saturation transfer (CEST) | |
| 650 | 4 | |a deep-learning-based super-resolution (DLSR) | |
| 650 | 4 | |a relayed nuclear Overhauser effect (rNOE) | |
| 700 | 1 | |a Park, Se Weon |e verfasserin |4 aut | |
| 700 | 1 | |a Lai, Joseph H C |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Kexin |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Jiadi |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Zilin |e verfasserin |4 aut | |
| 700 | 1 | |a Ilyas, Abdul-Mojeed Olabisi |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Huabing |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Jianpan |e verfasserin |4 aut | |
| 700 | 1 | |a Chan, Kannie W Y |e verfasserin |4 aut | |
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