UM
Fusing multi-scale information in convolution network for MR image super-resolution reconstruction
Liu, Chang; Wu, Xi; Yu, Xi; Tang, YuanYan; Zhang, Jian; Zhou, JiLiu
2018-08-25
Source PublicationBIOMEDICAL ENGINEERING ONLINE
ISSN1475-925X
Volume17
AbstractBackground: Magnetic resonance (MR) images are usually limited by low spatial resolution, which leads to errors in post-processing procedures. Recently, learning-based super-resolution methods, such as sparse coding and super-resolution convolution neural network, have achieved promising reconstruction results in scene images. However, these methods remain insufficient for recovering detailed information from low-resolution MR images due to the limited size of training dataset.
KeywordSuper-resolution reconstruction Multi-scale information fusion Convolution network Magnetic resonance imaging
DOI10.1186/s12938-018-0546-9
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical
WOS IDWOS:000442833200001
PublisherBMC
The Source to ArticleWOS
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Citation statistics
Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Liu, Chang,Wu, Xi,Yu, Xi,et al. Fusing multi-scale information in convolution network for MR image super-resolution reconstruction[J]. BIOMEDICAL ENGINEERING ONLINE,2018,17.
APA Liu, Chang,Wu, Xi,Yu, Xi,Tang, YuanYan,Zhang, Jian,&Zhou, JiLiu.(2018).Fusing multi-scale information in convolution network for MR image super-resolution reconstruction.BIOMEDICAL ENGINEERING ONLINE,17.
MLA Liu, Chang,et al."Fusing multi-scale information in convolution network for MR image super-resolution reconstruction".BIOMEDICAL ENGINEERING ONLINE 17(2018).
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