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Paper Abstract and Keywords
Presentation 2019-03-06 13:00
Magnetic Resonance Angiography Image Restoration by Super Resolution based on Deep Learning
Shizen Kitazaki, Masanori Kawakita, Yutaka Jitumatu (Kyushu Univ.), Shigehide Kuhara (Kyorin Univ.), Akio Hiwatashi, Jun'ichi Takeuchi (Kyushu Univ.) IBISML2018-114
Abstract (in Japanese) (See Japanese page) 
(in English) Magnetic Resonance Imaging (MRI) is one of the powerful techniques to acquire in vivo information. However, to obtain a three dimensional fine image of the whole brain, it takes thirty minutes to forty minutes by using a current standard MRI scanner. Thus, to mitigate the inconvenience of the patient, further reduction of imaging time is required. For the past 10 years, Compressed Sensing (CS) has contributed to research for acceleration and high definition of MRI by means of information processing. However, CS has a disadvantage that the computational complexity is the order of cubic of the number of samples. Thus, in the case of high resolution images, it requires a reconstruction time longer than the reduced inspection time. Recently, deep learning based approach has been intensively studied for this problem. In this research, we propose a deep learning method, in which we employ a super-resolution (SR) method based on convolutional neural networks. In our method, the SR processes are applied to various resolution images obtained from the input signal. We report the results of adapting the proposed method to MRA (Magnetic Resonance Angiography) images.
Keyword (in Japanese) (See Japanese page) 
(in English) Magnetic Reasonance Imaging / super-resolution / Deep Neural Network / Convolution Neural Network / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 472, IBISML2018-114, pp. 65-72, March 2019.
Paper # IBISML2018-114 
Date of Issue 2019-02-26 (IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
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All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee IBISML  
Conference Date 2019-03-05 - 2019-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) RIKEN AIP 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning, etc. 
Paper Information
Registration To IBISML 
Conference Code 2019-03-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Magnetic Resonance Angiography Image Restoration by Super Resolution based on Deep Learning 
Sub Title (in English)  
Keyword(1) Magnetic Reasonance Imaging  
Keyword(2) super-resolution  
Keyword(3) Deep Neural Network  
Keyword(4) Convolution Neural Network  
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1st Author's Name Shizen Kitazaki  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Masanori Kawakita  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
3rd Author's Name Yutaka Jitumatu  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
4th Author's Name Shigehide Kuhara  
4th Author's Affiliation Kyorin University (Kyorin Univ.)
5th Author's Name Akio Hiwatashi  
5th Author's Affiliation Kyushu University (Kyushu Univ.)
6th Author's Name Jun'ichi Takeuchi  
6th Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2019-03-06 13:00:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # IBISML2018-114 
Volume (vol) vol.118 
Number (no) no.472 
Page pp.65-72 
#Pages
Date of Issue 2019-02-26 (IBISML) 


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