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StatNet: Statistical Image Restoration for Low-Dose CT using Deep Learning

Authors
 Kihwan Choi  ;  Joon Seok Lim  ;  Sungwon Kim 
Citation
 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, Vol.14(6) : 1137-1150, 2020-10 
Journal Title
 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 
ISSN
 1932-4553 
Issue Date
2020-10
Abstract
Deep learning has recently attracted widespread interest as a means of reducing noise in low-dose CT (LDCT) images. Deep convolutional neural networks (CNNs) are typically trained to transfer high-quality image features of normal-dose CT (NDCT) images to LDCT images. However, existing deep learning approaches for denoising LDCT images often overlook the statistical property of CT images. In this paper, we propose an approach to statistical image restoration for LDCT using deep learning. We introduce a loss function to incorporate the noise property in image domain derived from the noise statistics in sinogram domain. In order to capture the spatially-varying statistics of CT images, we increase the receptive fields of the neural network to cover full-size CT slices. In addition, the proposed network utilizes z-directional correlation by taking multiple consecutive CT slices as input. For performance evaluation, the proposed networks are trained and validated with a public dataset consisting of LDCT-NDCT image pairs. We also perform a retrospective study by testing the networks with clinical LDCT images. The experimental results show that the denoising networks successfully reduce the noise level and restore the image details without adding artifacts. This study demonstrates that the statistical deep learning approach can restore the image quality of LDCT without loss of anatomical information.
Full Text
https://ieeexplore.ieee.org/abstract/document/9103190
DOI
10.1109/JSTSP.2020.2998413
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sungwon(김성원) ORCID logo https://orcid.org/0000-0001-5455-6926
Lim, Joon Seok(임준석) ORCID logo https://orcid.org/0000-0002-0334-5042
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/180365
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