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Paper Abstract and Keywords
Presentation 2020-01-30 10:50
[Short Paper] Study of image quality improvement technique using deep learning for nuclear medicine images
Masaya Momiuchi, Takeshi Hara (Gifu Univ), Tetsuro Katafuchi (Gifu Univ of Medical Science), Masaki Matsusako (St. Luke's Hospital), Hiroshi Fujita (Gifu Univ) MI2019-102
Abstract (in Japanese) (See Japanese page) 
(in English) Spatial resolutions in nuclear medical imaging are not equivalent to ordinary medical images such as CT or MR modalities. Various superresolution approaches have been proposed to improve image resolutions. The purpose of this study was to develop a deep-learning based method by using our unique dataset of 108-paired images of original signal and nuclear images obtained from actual radioactivated medicine with gamma camera systems. PSNR and SSIM were used to evaluated the improvements. As a result, the deep-learning methods achived a better performance than Bicubic interpolated images.
Keyword (in Japanese) (See Japanese page) 
(in English) Super-resolution / Nuclear medicine imaging / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 399, MI2019-102, pp. 165-168, Jan. 2020.
Paper # MI2019-102 
Date of Issue 2020-01-22 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 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)
Download PDF MI2019-102

Conference Information
Committee MI  
Conference Date 2020-01-29 - 2020-01-30 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2020-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study of image quality improvement technique using deep learning for nuclear medicine images 
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Keyword(1) Super-resolution  
Keyword(2) Nuclear medicine imaging  
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1st Author's Name Masaya Momiuchi  
1st Author's Affiliation Gifu University (Gifu Univ)
2nd Author's Name Takeshi Hara  
2nd Author's Affiliation Gifu University (Gifu Univ)
3rd Author's Name Tetsuro Katafuchi  
3rd Author's Affiliation Gifu University of Medical Science (Gifu Univ of Medical Science)
4th Author's Name Masaki Matsusako  
4th Author's Affiliation St. Luke's International Hospital (St. Luke's Hospital)
5th Author's Name Hiroshi Fujita  
5th Author's Affiliation Gifu University (Gifu Univ)
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Speaker Author-1 
Date Time 2020-01-30 10:50:00 
Presentation Time 10 minutes 
Registration for MI 
Paper # MI2019-102 
Volume (vol) vol.119 
Number (no) no.399 
Page pp.165-168 
#Pages 4 
Date of Issue 2020-01-22 (MI) 

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