Paper Abstract and Keywords |
Presentation |
2023-03-07 15:38
[Short Paper]
A Denoising Method for Low Dose CT by Iterative Processing Using Self-Supervised Learning Yuki Sato, Hiroyuki Kudo (Univ of Tsukuba) MI2022-121 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In recent years, patient exposure has become an issue, and low-dose CT, which reduces the amount of radiation irradiated, has been studied. However, the data obtained from low-dose CT contains a large amount of noise, and the same patient is not scanned twice at the same time, so pair data of normal-dose CT and low-dose CT cannot be obtained. Self-supervised learning, which uses only noise data for learning, is a deep learning-based denoising method that has been applied to low-dose CT denoising. However, excessive smoothing was found to cause loss of fine structures in CT images.
In this study, we proposed a self-supervised learning method that uses the loss obtained in an iterative process with two networks, one to infer noise and the other to infer clean data. The proposed method was found to be more effective in suppressing smoothing than conventional methods. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
CT / Denoise / Deep Learning / Self-supervised learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 417, MI2022-121, pp. 192-193, March 2023. |
Paper # |
MI2022-121 |
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) |
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MI2022-121 |
Conference Information |
Committee |
MI |
Conference Date |
2023-03-06 - 2023-03-07 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
OKINAWA SEINENKAIKAN |
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(See Japanese page) |
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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) |
A Denoising Method for Low Dose CT by Iterative Processing Using Self-Supervised Learning |
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CT |
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Denoise |
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Deep Learning |
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Self-supervised learning |
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1st Author's Name |
Yuki Sato |
1st Author's Affiliation |
University of Tsukuba (Univ of Tsukuba) |
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Hiroyuki Kudo |
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University of Tsukuba (Univ of Tsukuba) |
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Speaker |
Author-1 |
Date Time |
2023-03-07 15:38:00 |
Presentation Time |
13 minutes |
Registration for |
MI |
Paper # |
MI2022-121 |
Volume (vol) |
vol.122 |
Number (no) |
no.417 |
Page |
pp.192-193 |
#Pages |
2 |
Date of Issue |
2023-02-27 (MI) |
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