Dual-domain convolutional neural networks for improving structural information in 3 T MRI
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Yongqin | - |
dc.contributor.author | Yap, Pew-Thian | - |
dc.contributor.author | Qu, Liangqiong | - |
dc.contributor.author | Cheng, Jie-Zhi | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-08-31T22:41:26Z | - |
dc.date.available | 2021-08-31T22:41:26Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 0730-725X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/61365 | - |
dc.description.abstract | We propose a novel dual-domain convolutional neural network framework to improve structural information of routine 3 T images. We introduce a parameter-efficient butterfly network that involves two complementary domains: a spatial domain and a frequency domain. The butterfly network allows the interaction of these two domains in learning the complex mapping from 3 T to 7 T images. We verified the efficacy of the dual-domain strategy and butterfly network using 3 T and 7 T image pairs. Experimental results demonstrate that the proposed framework generates synthetic 7 T-like images and achieves performance superior to state-of-the-art methods. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.subject | IMAGE SUPERRESOLUTION | - |
dc.subject | 7T-LIKE IMAGES | - |
dc.subject | RECONSTRUCTION | - |
dc.subject | REGISTRATION | - |
dc.subject | ENHANCEMENT | - |
dc.title | Dual-domain convolutional neural networks for improving structural information in 3 T MRI | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1016/j.mri.2019.05.023 | - |
dc.identifier.scopusid | 2-s2.0-85067286440 | - |
dc.identifier.wosid | 000502191300011 | - |
dc.identifier.bibliographicCitation | MAGNETIC RESONANCE IMAGING, v.64, pp.90 - 100 | - |
dc.relation.isPartOf | MAGNETIC RESONANCE IMAGING | - |
dc.citation.title | MAGNETIC RESONANCE IMAGING | - |
dc.citation.volume | 64 | - |
dc.citation.startPage | 90 | - |
dc.citation.endPage | 100 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | IMAGE SUPERRESOLUTION | - |
dc.subject.keywordPlus | 7T-LIKE IMAGES | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | REGISTRATION | - |
dc.subject.keywordPlus | ENHANCEMENT | - |
dc.subject.keywordAuthor | Image synthesis | - |
dc.subject.keywordAuthor | Image super-resolution | - |
dc.subject.keywordAuthor | Magnetic resonance imaging | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
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