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Paper Abstract and Keywords
Presentation 2022-01-24 13:05
Reduction of Truncation Artifacts by Massive-Training Artificial Neural Network (MTANN) in Fast-Acquisition MRI of the Knee
Maodong Xiang, Ze Jin, Kenji Suzuki (Tokyo Tech) IE2021-31
Abstract (in Japanese) (See Japanese page) 
(in English) MRI has a relatively long acquisition time, leading to patient comfort problems and artifacts from patient motion. Accelerated MRI acquisitions by taking fewer samples in the k-space involve a trade-off between the image-quality degradation due to artifacts and acquisition time. The purpose of our study was to reduce truncation artifacts in reconstructing MR images from under-sampled k-space data in fast-acquisition MRI. This study proposed a novel massive-training artificial neural network (MTANN) scheme. The MTANN is our original deep-learning model that employs neural network regression in a convolutional manner. To investigate the nature of k-space under-sampling, we proposed a sampling pattern-specific kernel that extends the conventional square kernel to exploit more spatial information that can help MTANN to reduce truncation artifacts. We conducted experiments to evaluate the performance of our scheme with under-sampled MR images of 20 patients with 795 slices. The results showed that our proposed MTANN scheme substantially reduced artifacts in reconstructed MR images from under-sampled k-space data, while the image quality was well-maintained.
Keyword (in Japanese) (See Japanese page) 
(in English) MRI / Reconstruction / Deep Learning / Noise reduction / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 346, IE2021-31, pp. 21-26, Jan. 2022.
Paper # IE2021-31 
Date of Issue 2022-01-17 (IE) 
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)
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Conference Information
Committee IE  
Conference Date 2022-01-24 - 2022-01-24 
Place (in Japanese) (See Japanese page) 
Place (in English) National Institute of Informatics 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, Image Coding, etc. 
Paper Information
Registration To IE 
Conference Code 2022-01-IE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Reduction of Truncation Artifacts by Massive-Training Artificial Neural Network (MTANN) in Fast-Acquisition MRI of the Knee 
Sub Title (in English)  
Keyword(1) MRI  
Keyword(2) Reconstruction  
Keyword(3) Deep Learning  
Keyword(4) Noise reduction  
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1st Author's Name Maodong Xiang  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
2nd Author's Name Ze Jin  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
3rd Author's Name Kenji Suzuki  
3rd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
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Speaker Author-2 
Date Time 2022-01-24 13:05:00 
Presentation Time 25 minutes 
Registration for IE 
Paper # IE2021-31 
Volume (vol) vol.121 
Number (no) no.346 
Page pp.21-26 
#Pages 6 
Date of Issue 2022-01-17 (IE) 

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