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Proceedings Paper

A deeper convolutional neural network for denoising low-dose CT images
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Paper Abstract

In recent years, CNN has been gaining attention as a powerful denoising tool after the pioneering work [7], developing 3-layer convolutional neural network (CNN). However, the 3-layer CNN may lose details or contrast after denoising due to its shallow depth. In this study, we propose a deeper, 7-layer CNN for denoising low-dose CT images. We introduced dimension shrinkage and expansion steps to control explosion of the number of parameters, and also applied the batch normalization to alleviate difficulty in optimization. The network was trained and tested with Shepp-Logan phantom images reconstructed by FBP algorithm from projection data generated in a fan-beam geometry. For a training set and a test set, the independently generated uniform noise with different noise levels was added to the projection data. The image quality improvement was evaluated both qualitatively and quantitatively, and the results show that the proposed CNN effectively reduces the noise without resolution loss compared to BM3D and the 3-layer CNN.

Paper Details

Date Published: 9 March 2018
PDF: 6 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105733P (9 March 2018); doi: 10.1117/12.2286720
Show Author Affiliations
Byeongjoon Kim, Yonsei Univ. (Korea, Republic of)
Hyunjung Shim, Yonsei Univ. (Korea, Republic of)
Jongduk Baek, Yonsei Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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