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Paper Abstract and Keywords
Presentation 2026-03-05 14:07
[Short Paper] Improving the Accuracy of MRI Image Reconstruction Using Cold Diffusion with the Introduction of a Deep Diffusion Image Prior
Shunsuke Fukutomi, Tatsuya Yokota, Hidekata Hontani (NITech) MI2025-74
Abstract (in Japanese) (See Japanese page) 
(in English) This study aims to improve the accuracy of accelerated MRI reconstruction using diffusion models. We propose a novel reconstruction method thats integrates Deep Diffusion Image Prior(DDIP) into a Cold Diffusion-based accelerated MRI reconstruction framework. In accelerated MRI, k-sapce undersampling leads to signal loss, which results in structured artifacts in reconstructed images and degrades image quality. Here, conventional diffusion-based reconstruction methods do not explicity address the removal of such artifacts, making selective artifact suppression difficult. Also, diffusion-based reconstruction methods rely on learned prior distribution and reconstruction accuracy may degrade. This limitation is particularly critical for images containing abnormal regions, where the availability of training data is limited and the learned distribution may not generalize well. To address these problems,we employ Cold Diffusion, which explicity defines the degradation process caused by signal loss, enabling direct modeling of artifact removal. In addition, we introduce guidance based on the model and measurement consistency by Deep Diffusion Image Prior(DDIP). By combining Cold Diffusion-based method and DDIP, the proposed method achieves high reconstruction accuracy for MRI images, including those containing structures that are not adequately represented in the training data. We report experimental results using real images that show that the proposed method improves the accuracy better than conventional methods.
Keyword (in Japanese) (See Japanese page) 
(in English) Accelerated MRI / Cold Diffusion / Deep Image Prior / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 395, MI2025-74, pp. 59-60, March 2026.
Paper # MI2025-74 
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 MI2025-74

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) Improving the Accuracy of MRI Image Reconstruction Using Cold Diffusion with the Introduction of a Deep Diffusion Image Prior 
Sub Title (in English)  
Keyword(1) Accelerated MRI  
Keyword(2) Cold Diffusion  
Keyword(3) Deep Image Prior  
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1st Author's Name Shunsuke Fukutomi  
1st Author's Affiliation Nagoya Institute of Technology (NITech)
2nd Author's Name Tatsuya Yokota  
2nd Author's Affiliation Nagoya Institute of Technology (NITech)
3rd Author's Name Hidekata Hontani  
3rd Author's Affiliation Nagoya Institute of Technology (NITech)
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Speaker Author-1 
Date Time 2026-03-05 14:07:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2025-74 
Volume (vol) vol.125 
Number (no) no.395 
Page pp.59-60 
#Pages 2 
Date of Issue 2026-02-26 (MI) 

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