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Paper Abstract and Keywords
Presentation 2022-07-22 16:50
Deep Learning based Compressed Sensing Image Reconstruction Using Multiple Signal Under-sampling Pattern
Masaki Shibui, Kazuki Yamato, Satoshi Ito (Utsunomiya Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) CS-MRI, which is an application of compressed sensing (CS) to MR image acquisition, can reconstruct MR images from a small number of acquired signals. Recently, it has been reported that deep learning (DL)-based CS reconstruction is fast and produces high-quality images. On the other hand, it is time-consuming to learn and requires additional learning on the case when the signal under-sampling pattern is changed. In this study, we propose a improve method in which high quality images can be obtained regardless of the signal under-sampling patterns in Generic-ADMM-Net.
Keyword (in Japanese) (See Japanese page) 
(in English) Compressed Sensing / Generic-ADMM-Net / Deep learning / / / / /  
Reference Info. ITE Tech. Rep., vol. 46, no. 20, ME2022-71, pp. 27-29, July 2022.
Paper # ME2022-71 
Date of Issue 2022-07-15 (ME) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Committee ME  
Conference Date 2022-07-22 - 2022-07-22 
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 2022-07-ME 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Learning based Compressed Sensing Image Reconstruction Using Multiple Signal Under-sampling Pattern 
Sub Title (in English)  
Keyword(1) Compressed Sensing  
Keyword(2) Generic-ADMM-Net  
Keyword(3) Deep learning  
1st Author's Name Masaki Shibui  
1st Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
2nd Author's Name Kazuki Yamato  
2nd Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
3rd Author's Name Satoshi Ito  
3rd Author's Affiliation Utsunomiya University (Utsunomiya Univ.)
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Speaker Author-1 
Date Time 2022-07-22 16:50:00 
Presentation Time 30 minutes 
Registration for ME 
Paper # ME2022-71 
Volume (vol) vol.46 
Number (no) no.20 
Page pp.27-29 
Date of Issue 2022-07-15 (ME) 

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