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Paper Abstract and Keywords
Presentation 2026-03-05 10:10
Viscoelastic Modulus Estimation for Magnetic Resonance Elastography Using Generative Adversarial Network and Physics-Informed Neural Networks
Tatsuru Zushiden (Chiba Univ.), Hiroshi Fujiwara (Kyoto Univ.), Mikio Suga (Chiba Univ.) MI2025-63
Abstract (in Japanese) (See Japanese page) 
(in English) Magnetic Resonance Elastography (MRE) is a technique for non-invasively measuring the viscoelastic properties of biological tissues. In this study, we developed a deep learning method for simultaneous estimation of storage modulus and loss modulus from elastic wave images. We constructed a Physics-Informed Neural Network (PINN) based on a U-Net architecture, incorporating the wave equation into the loss function as a physical constraint. The network was trained using four types of elastic wave images with initial phases differing by π/2 radians, generated from digital phantoms containing spherical inclusions created by the finite element method. Comparative validation with existing methods demonstrated that PINNs exhibit high quantitative accuracy and spatial resolution at boundary regions, enabling estimation while maintaining physical consistency.
Keyword (in Japanese) (See Japanese page) 
(in English) Magnetic Resonance Elastography / Deep Learning / Generative Adversarial Network / Physics-Informed Neural Networks / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 395, MI2025-63, pp. 16-18, March 2026.
Paper # MI2025-63 
Date of Issue 2026-02-26 (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 MI2025-63

Conference Information
Committee MI  
Conference Date 2026-03-05 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging, etc. 
Paper Information
Registration To MI 
Conference Code 2026-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Viscoelastic Modulus Estimation for Magnetic Resonance Elastography Using Generative Adversarial Network and Physics-Informed Neural Networks 
Sub Title (in English)  
Keyword(1) Magnetic Resonance Elastography  
Keyword(2) Deep Learning  
Keyword(3) Generative Adversarial Network  
Keyword(4) Physics-Informed Neural Networks  
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1st Author's Name Tatsuru Zushiden  
1st Author's Affiliation Graduate school of science and Engineering, Chiba University (Chiba Univ.)
2nd Author's Name Hiroshi Fujiwara  
2nd Author's Affiliation Graduate school of Informatics, Kyoto University (Kyoto Univ.)
3rd Author's Name Mikio Suga  
3rd Author's Affiliation Center for Frontier Medical Engineering, Chiba University (Chiba Univ.)
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Speaker Author-1 
Date Time 2026-03-05 10:10:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2025-63 
Volume (vol) vol.125 
Number (no) no.395 
Page pp.16-18 
#Pages 3 
Date of Issue 2026-02-26 (MI) 

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