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Paper Abstract and Keywords
Presentation 2025-12-05 09:50
Synthetic CT Generation from MRI using a CycleGAN with Contrastive Loss
Kotaro Kawanami, Masato Tsuneda, Kota Abe (Chiba Univ.), Makoto Saito (Chiba Univ. Hosp.), Takashi Uno, Hiroki Suyari, Yasukuni Mori (Chiba Univ.) MICT2025-36 MI2025-44
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, MR-guided radiotherapy (MRgRT), which performs radiation therapy using only MR images that offer superior tumor visibility, has attracted significant attention.
However, since MR images lack electron density information, CT images are indispensable for calculating the behavior of radiation within the body.
Therefore, this study proposed a model combining contrastive learning and CycleGAN to generate synthetic CT images (pseudo-CT) from MR images.
As an evaluation of the proposed model's performance, the generated synthetic CT images were compared with the ground truth CT images.
Keyword (in Japanese) (See Japanese page) 
(in English) Cycle GAN / deep learning / prostate / radiotherapy treatment planning / synthetic CT / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 270, MI2025-44, pp. 18-21, Dec. 2025.
Paper # MI2025-44 
Date of Issue 2025-11-27 (MICT, 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 MICT2025-36 MI2025-44

Conference Information
Committee MICT MI  
Conference Date 2025-12-04 - 2025-12-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Okayama Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical imaging technology, healthcare and medical information communication technology, etc. 
Paper Information
Registration To MI 
Conference Code 2025-12-MICT-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Synthetic CT Generation from MRI using a CycleGAN with Contrastive Loss 
Sub Title (in English)  
Keyword(1) Cycle GAN  
Keyword(2) deep learning  
Keyword(3) prostate  
Keyword(4) radiotherapy treatment planning  
Keyword(5) synthetic CT  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Kotaro Kawanami  
1st Author's Affiliation Chiba University (Chiba Univ.)
2nd Author's Name Masato Tsuneda  
2nd Author's Affiliation Graduate School of Medicine, Chiba University (Chiba Univ.)
3rd Author's Name Kota Abe  
3rd Author's Affiliation Graduate School of Medicine, Chiba University (Chiba Univ.)
4th Author's Name Makoto Saito  
4th Author's Affiliation Diagnostic Radiology and Radiation Oncology, Chiba University Hospital (Chiba Univ. Hosp.)
5th Author's Name Takashi Uno  
5th Author's Affiliation Graduate School of Medicine, Chiba University (Chiba Univ.)
6th Author's Name Hiroki Suyari  
6th Author's Affiliation Chiba University (Chiba Univ.)
7th Author's Name Yasukuni Mori  
7th Author's Affiliation Chiba University (Chiba Univ.)
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Speaker Author-1 
Date Time 2025-12-05 09:50:00 
Presentation Time 20 minutes 
Registration for MI 
Paper # MICT2025-36, MI2025-44 
Volume (vol) vol.125 
Number (no) no.269(MICT), no.270(MI) 
Page pp.18-21 
#Pages 4 
Date of Issue 2025-11-27 (MICT, MI) 

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