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| Presentation | 2021-03-17 13:30 Noise reduction method for sparse-view computed tomography using spatial frequency components Takayuki Okamoto, Hideaki Haneishi (Chiba Univ.)  MI2020-95 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Sparse-view CT, an imaging technique to reduce the number of projections, can reduce the radiation dose and scanning duration. However, insufficient sampling causes spoke-like artifacts in analytical reconstruction methods. We have developed a noise reduction method for sparse-view CT with super-resolution technique of sinograms. This paper proposes a new denoising method for sparse-view CT using deep learning with information in the frequency domain. The proposed method defines a loss function with additional structural information of the power spectrum and estimates the full sampling sinogram by supervised learning. The results of comparative experiments showed that the proposed method performs the best quantitative and qualitative evaluation performance, suggesting its effectiveness using frequency components. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Sparse-view CT / artifact reduction / deep learning / Fourier transform / power spectrum / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 431, MI2020-95, pp. 207-211, March 2021. | |
| Paper # | MI2020-95 | |
| Date of Issue | 2021-03-08 (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 | MI2020-95 |
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| Conference Information | |
| Committee | MI |
| Conference Date | 2021-03-15 - 2021-03-17 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical Imaging |
| Paper Information | |
| Registration To | MI |
| Conference Code | 2021-03-MI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Noise reduction method for sparse-view computed tomography using spatial frequency components |
| Sub Title (in English) | |
| Keyword(1) | Sparse-view CT |
| Keyword(2) | artifact reduction |
| Keyword(3) | deep learning |
| Keyword(4) | Fourier transform |
| Keyword(5) | power spectrum |
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| Keyword(8) | |
| 1st Author's Name | Takayuki Okamoto |
| 1st Author's Affiliation | Chiba University (Chiba Univ.) |
| 2nd Author's Name | Hideaki Haneishi |
| 2nd Author's Affiliation | Chiba University (Chiba Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-03-17 13:30:00 |
| Presentation Time | 15 minutes |
| Registration for | MI |
| Paper # | MI2020-95 |
| Volume (vol) | vol.120 |
| Number (no) | no.431 |
| Page | pp.207-211 |
| #Pages | 5 |
| Date of Issue | 2021-03-08 (MI) |
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