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Lau V, Xiao L, Zhao Y, Su S, Ding Y, Man C, Wang X, Tsang A, Cao P, Lau GKK, Leung GKK, Leong ATL, Wu EX. Pushing the limits of low-cost ultralow-field MRI by dual-acquisition deep learning 3D superresolution. Magn Reson Med 2023; 90:400-416. [PMID: 37010491 DOI: 10.1002/mrm.29642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/04/2023] [Accepted: 03/06/2023] [Indexed: 04/04/2023]
Abstract
PURPOSE Recent development of ultralow-field (ULF) MRI presents opportunities for low-power, shielding-free, and portable clinical applications at a fraction of the cost. However, its performance remains limited by poor image quality. Here, a computational approach is formulated to advance ULF MR brain imaging through deep learning of large-scale publicly available 3T brain data. METHODS A dual-acquisition 3D superresolution model is developed for ULF brain MRI at 0.055 T. It consists of deep cross-scale feature extraction, attentional fusion of two acquisitions, and reconstruction. Models for T1 -weighted and T2 -weighted imaging were trained with 3D ULF image data sets synthesized from the high-resolution 3T brain data from the Human Connectome Project. They were applied to 0.055T brain MRI with two repetitions and isotropic 3-mm acquisition resolution in healthy volunteers, young and old, as well as patients. RESULTS The proposed approach significantly enhanced image spatial resolution and suppressed noise/artifacts. It yielded high 3D image quality at 0.055 T for the two most common neuroimaging protocols with isotropic 1.5-mm synthetic resolution and total scan time under 20 min. Fine anatomical details were restored with intrasubject reproducibility, intercontrast consistency, and confirmed by 3T MRI. CONCLUSION The proposed dual-acquisition 3D supe-resolution approach advances ULF MRI for quality brain imaging through deep learning of high-field brain data. Such strategy can empower ULF MRI for low-cost brain imaging, especially in point-of-care scenarios or/and in low-income and mid-income countries.
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Affiliation(s)
- Vick Lau
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, SAR, Hong Kong, China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, SAR, Hong Kong, China
| | - Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, SAR, Hong Kong, China
| | - Shi Su
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, SAR, Hong Kong, China
| | - Ye Ding
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, SAR, Hong Kong, China
| | - Christopher Man
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, SAR, Hong Kong, China
| | - Xunda Wang
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, SAR, Hong Kong, China
| | - Anderson Tsang
- Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, SAR, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, LKS Faculty of Medicine, The University of Hong Kong, SAR, Hong Kong, China
| | - Gary K K Lau
- Department of Medicine, LKS Faculty of Medicine, The University of Hong Kong, SAR, Hong Kong, China
| | - Gilberto K K Leung
- Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, SAR, Hong Kong, China
| | - Alex T L Leong
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, SAR, Hong Kong, China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, SAR, Hong Kong, China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, SAR, Hong Kong, China
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