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Paper Abstract and Keywords
Presentation 2022-02-12 10:45
Study on Deep Learning Reconstruction of MRI Complex Images Using Real-value CNN
Itona Fukatsu, Kazuki Yamato, Satoshi Ito (Utsunomiya Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) Application of compressed sensing has been applied to speed up the data acquisition time for MR imaging. In recent years, research on reconstruction method using deep learning has been actively studied to reduce the reconstruction time and to improve the quality of reconstructed images. Since MR images are complex-value images, CNN needs to deal with complex values. In this study, we propose a new method for reconstructing complex-value images using a real-value CNN by introducing symmetrical signal under-sampling.
Keyword (in Japanese) (See Japanese page) 
(in English) MRI / CNN / image reconstruction / high-speed imaging / / / /  
Reference Info. ITE Tech. Rep., vol. 46, no. 4, ME2022-7, pp. 25-27, Feb. 2022.
Paper # ME2022-7 
Date of Issue 2022-02-05 (ME) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Committee ME  
Conference Date 2022-02-12 - 2022-02-12 
Place (in Japanese) (See Japanese page) 
Place (in English) online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ME 
Conference Code 2022-02-ME 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Deep Learning Reconstruction of MRI Complex Images Using Real-value CNN 
Sub Title (in English)  
Keyword(1) MRI  
Keyword(2) CNN  
Keyword(3) image reconstruction  
Keyword(4) high-speed imaging  
1st Author's Name Itona Fukatsu  
1st Author's Affiliation Ustunomiya University (Utsunomiya Univ.)
2nd Author's Name Kazuki Yamato  
2nd Author's Affiliation Ustunomiya University (Utsunomiya Univ.)
3rd Author's Name Satoshi Ito  
3rd Author's Affiliation Ustunomiya University (Utsunomiya Univ.)
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Speaker Author-1 
Date Time 2022-02-12 10:45:00 
Presentation Time 15 minutes 
Registration for ME 
Paper # ME2022-7 
Volume (vol) vol.46 
Number (no) no.4 
Page pp.25-27 
Date of Issue 2022-02-05 (ME) 

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