High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI
Choroid plexus (CP) is an important brain structure that produces cerebrospinal fluid (CSF). CP perfusion has been studied using multi-delay arterial spin labeling (MD-ASL) in adults but not in pediatric populations due to the challenge of small CP size in children. Here we present a high resolution...
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Elsevier
2025-03-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925000722 |
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| author | Qinyang Shou Chenyang Zhao Xingfeng Shao Megan M Herting Danny JJ Wang |
| author_facet | Qinyang Shou Chenyang Zhao Xingfeng Shao Megan M Herting Danny JJ Wang |
| author_sort | Qinyang Shou |
| collection | DOAJ |
| description | Choroid plexus (CP) is an important brain structure that produces cerebrospinal fluid (CSF). CP perfusion has been studied using multi-delay arterial spin labeling (MD-ASL) in adults but not in pediatric populations due to the challenge of small CP size in children. Here we present a high resolution (iso2 mm) MDASL protocol with 10-minute scan time and performed test-retest scans on 21 typically developing children aged 8 to 17 years. We further proposed a Transformer-based deep learning (DL) model with k-space weighted image average (KWIA) denoised images as reference for training the model. The performance of the model was evaluated by the SNR, bias and repeatability of the fitted perfusion parameters of the CP and gray matter. The proposed method was compared to several benchmark methods including KWIA, joint denoising and reconstruction with total generalized variation (TGV) regularization, as well as another self-supervised method termed Noise2Void. The results show that the proposed Transformer model with KWIA reference can effectively denoise multi-delay ASL images, not only improving the SNR for perfusion images of each delay, but also improving the SNR for the fitted perfusion maps for visualizing and quantifying CP perfusion in children. This may facilitate the use of MDASL in neurodevelopmental studies to characterize the development of CP and glymphatic system. |
| format | Article |
| id | doaj-art-1e67a89f5df94041988433521c0af3e8 |
| institution | Kabale University |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-1e67a89f5df94041988433521c0af3e82025-02-02T05:26:52ZengElsevierNeuroImage1095-95722025-03-01308121070High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRIQinyang Shou0Chenyang Zhao1Xingfeng Shao2Megan M Herting3Danny JJ Wang4Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United StatesLaboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United StatesLaboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United StatesDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, United StatesLaboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States; Corresponding author.Choroid plexus (CP) is an important brain structure that produces cerebrospinal fluid (CSF). CP perfusion has been studied using multi-delay arterial spin labeling (MD-ASL) in adults but not in pediatric populations due to the challenge of small CP size in children. Here we present a high resolution (iso2 mm) MDASL protocol with 10-minute scan time and performed test-retest scans on 21 typically developing children aged 8 to 17 years. We further proposed a Transformer-based deep learning (DL) model with k-space weighted image average (KWIA) denoised images as reference for training the model. The performance of the model was evaluated by the SNR, bias and repeatability of the fitted perfusion parameters of the CP and gray matter. The proposed method was compared to several benchmark methods including KWIA, joint denoising and reconstruction with total generalized variation (TGV) regularization, as well as another self-supervised method termed Noise2Void. The results show that the proposed Transformer model with KWIA reference can effectively denoise multi-delay ASL images, not only improving the SNR for perfusion images of each delay, but also improving the SNR for the fitted perfusion maps for visualizing and quantifying CP perfusion in children. This may facilitate the use of MDASL in neurodevelopmental studies to characterize the development of CP and glymphatic system.http://www.sciencedirect.com/science/article/pii/S1053811925000722N/A |
| spellingShingle | Qinyang Shou Chenyang Zhao Xingfeng Shao Megan M Herting Danny JJ Wang High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI NeuroImage N/A |
| title | High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI |
| title_full | High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI |
| title_fullStr | High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI |
| title_full_unstemmed | High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI |
| title_short | High resolution multi-delay arterial spin labeling with self-supervised deep learning denoising for pediatric choroid plexus perfusion MRI |
| title_sort | high resolution multi delay arterial spin labeling with self supervised deep learning denoising for pediatric choroid plexus perfusion mri |
| topic | N/A |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925000722 |
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