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| Presentation | 2021-05-17 14:40 MR super-resolution based on signal-image domain learning using phase scrambling Fourier transform imaging Kazuki Yamato, Hiromichi Wakatsuki, Satoshi Ito (Utsunomiya Univ.)  MI2021-6 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In the phase-scrambling Fourier transform (PSFT) imaging, the signals not sampled during imaging can be extrapolated and the reconstructed high-resolution image can be acquired by the reconstruction processing after imaging under a limitation that a measurement object can be represented in the real function. We studied iterative methods to extrapolate MR signals and improved the spatial resolution of the reconstructed image. However, the improvement in resolution is low in the central part of the image in iterative methods. In addition, about 20 iterations are required to reconstruct the image. In this paper, we use Generic-ADMM-Net which is a kind of deep learning reconstruction method in order to improve the PSFT signal extrapolation and the spatial resolution. To verify the effectiveness of the proposed method, computational simulations were conducted. In this simulations, PSFT signals whose sampling rate was limited to 25% were input to deep learning and images reconstructed from full-data signal were trained as supervised data. As a result, it was confirmed that the aliasing distortion included in the reconstructed image was reduced and the resolution of the reconstructed image was improved. In addition, when the proposed method was applied to the signal including noise, the reconstructed image with less noise was obtained. Therefore, it was confirmed that the proposed method had a denoising effect, which was a feature not found in iterative methods. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Phase-scrambling Fourier transform / deep learning / super-resolution / MRI / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 21, MI2021-6, pp. 14-19, May 2021. | |
| Paper # | MI2021-6 | |
| Date of Issue | 2021-05-10 (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 | MI2021-6 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2021-05-17 - 2021-05-17 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Image Processing, etc |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2021-05-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | MR super-resolution based on signal-image domain learning using phase scrambling Fourier transform imaging |
| Sub Title (in English) | |
| Keyword(1) | Phase-scrambling Fourier transform |
| Keyword(2) | deep learning |
| Keyword(3) | super-resolution |
| Keyword(4) | MRI |
| Keyword(5) | |
| Keyword(6) | |
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| Keyword(8) | |
| 1st Author's Name | Kazuki Yamato |
| 1st Author's Affiliation | Utsunomiya University (Utsunomiya Univ.) |
| 2nd Author's Name | Hiromichi Wakatsuki |
| 2nd Author's Affiliation | Utsunomiya University (Utsunomiya Univ.) |
| 3rd Author's Name | Satoshi Ito |
| 3rd Author's Affiliation | Utsunomiya University (Utsunomiya Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-05-17 14:40:00 |
| Presentation Time | 30 minutes |
| Registration for | MI |
| Paper # | MI2021-6 |
| Volume (vol) | vol.121 |
| Number (no) | no.21 |
| Page | pp.14-19 |
| #Pages | 6 |
| Date of Issue | 2021-05-10 (MI) |
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