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Improving Low-Dose CT Image Using Residual Convolutional Network

Abstract : Low-dose CT is an effective solution to alleviate radiation risk to patients, it also introduces additional noise and streak artifacts. In order to maintain a high image quality for low-dose scanned CT data, we propose a post-processing method based on deep learning and using 2-D and 3-D residual convolutional networks. Experimental results and comparisons with other competing methods show that the proposed approach can effectively reduce the low-dose noise and artifacts while preserving tissue details. It is also pointed out that the 3-D model can achieve better performance in both edge-preservation and noise-artifact suppression. Factors that may influence the model performance, such as model width, depth, and dropout, are also examined.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01685725
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, January 16, 2018 - 4:30:05 PM
Last modification on : Wednesday, August 5, 2020 - 3:41:23 AM

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Wei Yang, Huijuan Zhang, Jian Yang, Jiasong Wu, Xiangrui Yin, et al.. Improving Low-Dose CT Image Using Residual Convolutional Network. IEEE Access, IEEE, 2017, 5, pp.24698-24705. ⟨10.1109/ACCESS.2017.2766438⟩. ⟨hal-01685725⟩

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