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Paper Abstract and Keywords
Presentation 2023-03-07 10:51
Deep learning-based MRI phase unwrapping technique by using BlochSolver
Kota Tsutsui, Yuta Endo, Haruna Shibou, Sanae Takahashi, Kuninori Kobayashi, Shigehide Kuhara (Kyorin Univ) MI2022-110
Abstract (in Japanese) (See Japanese page) 
(in English) MRI (Magnetic Resonance Imaging) has excellent image contrast; however, phase errors due to magnetic field inhomogeneities are problematic. Shimming is a correction method for magnetic field inhomogeneities. The magnetic field map is obtained from the phase map; therefore, phase unwrapping techniques are required to correctly expand the phase wrap. Conventional methods occasionally suffer from difficulties in accurately expanding complicated phase unwrapping including phase discontinuities. There have been studies on phase-unwrapping methods using deep learning. However, there is a limitation in collecting sufficient phase images for learning by using only actual MRI systems. Consequently, methods to generate artificial data for training have been considered. However, these data sets are unable to represent the complex phase distribution as in an actual MRI. In this study, we investigated a method using an MRI simulator that can generate data more similar to actual data instead of generating artificial data, in the phase unwrapping technique using deep learning.
Keyword (in Japanese) (See Japanese page) 
(in English) MRI / phase unwrapping technique / Deep Learning / MRI Simulator / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 417, MI2022-110, pp. 150-154, March 2023.
Paper # MI2022-110 
Date of Issue 2023-02-27 (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 MI2022-110

Conference Information
Committee MI  
Conference Date 2023-03-06 - 2023-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWA SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2023-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep learning-based MRI phase unwrapping technique by using BlochSolver 
Sub Title (in English)  
Keyword(1) MRI  
Keyword(2) phase unwrapping technique  
Keyword(3) Deep Learning  
Keyword(4) MRI Simulator  
Keyword(5)  
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1st Author's Name Kota Tsutsui  
1st Author's Affiliation Kyorin University Graduate School of Health Sciences (Kyorin Univ)
2nd Author's Name Yuta Endo  
2nd Author's Affiliation Kyorin University (Kyorin Univ)
3rd Author's Name Haruna Shibou  
3rd Author's Affiliation Kyorin University (Kyorin Univ)
4th Author's Name Sanae Takahashi  
4th Author's Affiliation Kyorin University (Kyorin Univ)
5th Author's Name Kuninori Kobayashi  
5th Author's Affiliation Kyorin University (Kyorin Univ)
6th Author's Name Shigehide Kuhara  
6th Author's Affiliation Kyorin University (Kyorin Univ)
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Speaker Author-1 
Date Time 2023-03-07 10:51:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2022-110 
Volume (vol) vol.122 
Number (no) no.417 
Page pp.150-154 
#Pages 5 
Date of Issue 2023-02-27 (MI) 

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