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Presentation 2019-09-05 10:35
[Short Paper] Dynamic PET Image Reconstruction using Non-Negative Matrix Decomposition with Deep Image Prior
Tomoshige Shimomura, Kazuya Kawai (NIT), Muneyuki Sakata (Tokyo Metro. Inst. Gerontology), Yuichi Kimura (KU), Tatsuya Yokota, Hidekata Hontani (NIT) PRMU2019-24 MI2019-43
Abstract (in Japanese) (See Japanese page) 
(in English) We present a PET image reconstruction method that can reconstruct dynamic PET images with high SN ratio and can simultaneously segment the images into regions each of which has a different ligand dynamics. It is known that PET image reconstruction is highly sensitive with measurement noises included in sinograms and that stable PET image reconstruction needs appropriate constraints over the solution space. The proposed method constrains the temporal patterns of each voxel using the quadratic variation and the spatial patterns using a Deep Image Prior. For introducing these constraints of the solution space, the proposed method represents each of the temporal series of sinograms and of corresponding PET images using a matrix and factorizes the latter matrix into bases of spatial and temporal patterns. Here, the non-negative constraint is also introduced in the factorization. This non-negative matrix factorization enables the segmentation of the spatial patterns based on the difference of the temporal patterns. In this presentation, we describe the algorithm and show some experimental results.
Keyword (in Japanese) (See Japanese page) 
(in English) PET image / Image reconstruction / Deep Image Prior / Non-negative matrix factorization / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 193, MI2019-43, pp. 69-70, Sept. 2019.
Paper # MI2019-43 
Date of Issue 2019-08-28 (PRMU, MI) 
ISSN Online edition: ISSN 2432-6380
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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 PRMU MI IPSJ-CVIM  
Conference Date 2019-09-04 - 2019-09-05 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To MI 
Conference Code 2019-09-PRMU-MI-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Dynamic PET Image Reconstruction using Non-Negative Matrix Decomposition with Deep Image Prior 
Sub Title (in English)  
Keyword(1) PET image  
Keyword(2) Image reconstruction  
Keyword(3) Deep Image Prior  
Keyword(4) Non-negative matrix factorization  
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1st Author's Name Tomoshige Shimomura  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Kazuya Kawai  
2nd Author's Affiliation Nagoya Institute of Technology (NIT)
3rd Author's Name Muneyuki Sakata  
3rd Author's Affiliation Tokyo Metropolitan Institute of Gerontology (Tokyo Metro. Inst. Gerontology)
4th Author's Name Yuichi Kimura  
4th Author's Affiliation Kindai University (KU)
5th Author's Name Tatsuya Yokota  
5th Author's Affiliation Nagoya Institute of Technology (NIT)
6th Author's Name Hidekata Hontani  
6th Author's Affiliation Nagoya Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2019-09-05 10:35:00 
Presentation Time 10 minutes 
Registration for MI 
Paper # PRMU2019-24, MI2019-43 
Volume (vol) vol.119 
Number (no) no.192(PRMU), no.193(MI) 
Page pp.69-70 
#Pages
Date of Issue 2019-08-28 (PRMU, MI) 


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