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- Publisher Website: 10.1016/j.media.2020.101663
- Scopus: eid_2-s2.0-85080046671
- PMID: 32120269
- WOS: WOS:000534353000006
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Article: Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains
Title | Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains |
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Authors | |
Keywords | Image synthesis Magnetic resonance imaging (MRI) Spatial and wavelet domains |
Issue Date | 2020 |
Citation | Medical Image Analysis, 2020, v. 62, article no. 101663 How to Cite? |
Abstract | Ultra-high field 7T MRI scanners, while producing images with exceptional anatomical details, are cost prohibitive and hence highly inaccessible. In this paper, we introduce a novel deep learning network that fuses complementary information from spatial and wavelet domains to synthesize 7T T1-weighted images from their 3T counterparts. Our deep learning network leverages wavelet transformation to facilitate effective multi-scale reconstruction, taking into account both low-frequency tissue contrast and high-frequency anatomical details. Our network utilizes a novel wavelet-based affine transformation (WAT) layer, which modulates feature maps from the spatial domain with information from the wavelet domain. Extensive experimental results demonstrate the capability of the proposed method in synthesizing high-quality 7T images with better tissue contrast and greater details, outperforming state-of-the-art methods. |
Persistent Identifier | http://hdl.handle.net/10722/325468 |
ISSN | 2023 Impact Factor: 10.7 2023 SCImago Journal Rankings: 4.112 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Qu, Liangqiong | - |
dc.contributor.author | Zhang, Yongqin | - |
dc.contributor.author | Wang, Shuai | - |
dc.contributor.author | Yap, Pew Thian | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2023-02-27T07:33:33Z | - |
dc.date.available | 2023-02-27T07:33:33Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Medical Image Analysis, 2020, v. 62, article no. 101663 | - |
dc.identifier.issn | 1361-8415 | - |
dc.identifier.uri | http://hdl.handle.net/10722/325468 | - |
dc.description.abstract | Ultra-high field 7T MRI scanners, while producing images with exceptional anatomical details, are cost prohibitive and hence highly inaccessible. In this paper, we introduce a novel deep learning network that fuses complementary information from spatial and wavelet domains to synthesize 7T T1-weighted images from their 3T counterparts. Our deep learning network leverages wavelet transformation to facilitate effective multi-scale reconstruction, taking into account both low-frequency tissue contrast and high-frequency anatomical details. Our network utilizes a novel wavelet-based affine transformation (WAT) layer, which modulates feature maps from the spatial domain with information from the wavelet domain. Extensive experimental results demonstrate the capability of the proposed method in synthesizing high-quality 7T images with better tissue contrast and greater details, outperforming state-of-the-art methods. | - |
dc.language | eng | - |
dc.relation.ispartof | Medical Image Analysis | - |
dc.subject | Image synthesis | - |
dc.subject | Magnetic resonance imaging (MRI) | - |
dc.subject | Spatial and wavelet domains | - |
dc.title | Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.media.2020.101663 | - |
dc.identifier.pmid | 32120269 | - |
dc.identifier.scopus | eid_2-s2.0-85080046671 | - |
dc.identifier.volume | 62 | - |
dc.identifier.spage | article no. 101663 | - |
dc.identifier.epage | article no. 101663 | - |
dc.identifier.eissn | 1361-8423 | - |
dc.identifier.isi | WOS:000534353000006 | - |