TY - JOUR
T1 - SCRDN
T2 - Residual dense network with self-calibrated convolutions for low dose CT image denoising
AU - Ma, Limin
AU - Xue, Hengzhi
AU - Yang, Guangtong
AU - Zhang, Zitong
AU - Li, Chen
AU - Yao, Yudong
AU - Teng, Yueyang
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Low-dose computed tomography (LDCT) can reduce the X-ray radiation dose that the patients receive, up to 86%, which decreases the potential hazards and expands its application scope. However, LDCT images contain a lot of noise and artifacts, which brings great difficulties to doctors’ diagnosis. Recently, methods based on deep learning have obtained great success in noise reducing of LDCT images. In this paper, we propose a novel residual dense network with self-calibrated convolution (SCRDN) for LDCT images denoising. Compared with the basic CNN, SCRDN includes jump connection, dense connection and self-calibrated convolution instead of traditional convolution. It makes full use of the hierarchical features of original images to obtain the reconstructed images with more details. It also obtains a larger receptive field without introducing new parameters. The experimental results show that the proposed method can achieve performance improvements over most state-of-the-art methods used in CT denoising.
AB - Low-dose computed tomography (LDCT) can reduce the X-ray radiation dose that the patients receive, up to 86%, which decreases the potential hazards and expands its application scope. However, LDCT images contain a lot of noise and artifacts, which brings great difficulties to doctors’ diagnosis. Recently, methods based on deep learning have obtained great success in noise reducing of LDCT images. In this paper, we propose a novel residual dense network with self-calibrated convolution (SCRDN) for LDCT images denoising. Compared with the basic CNN, SCRDN includes jump connection, dense connection and self-calibrated convolution instead of traditional convolution. It makes full use of the hierarchical features of original images to obtain the reconstructed images with more details. It also obtains a larger receptive field without introducing new parameters. The experimental results show that the proposed method can achieve performance improvements over most state-of-the-art methods used in CT denoising.
KW - Dense network
KW - Image denoising
KW - Low-dose computed tomography (LDCT)
KW - Self-calibrated convolution
UR - http://www.scopus.com/inward/record.url?scp=85141001129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141001129&partnerID=8YFLogxK
U2 - 10.1016/j.nima.2022.167625
DO - 10.1016/j.nima.2022.167625
M3 - Article
AN - SCOPUS:85141001129
SN - 0168-9002
VL - 1045
JO - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
JF - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
M1 - 167625
ER -