title: Low-dose CT via convolutional neural network.
abstract: In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods.
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China; National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China.
authors.forename: Hu
authors.initials: H
authors.lastname: Chen
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China.
authors.forename: Weihua
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Department of Scientific Research and Education, The Sixth People's Hospital of Chengdu, Chengdu 610065, China.
authors.forename: Peixi
authors.initials: P
authors.lastname: Liao
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China; National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Li
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China.
authors.forename: Jiliu
authors.initials: J
authors.lastname: Zhou
authors.affiliation: Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
authors.forename: Ge
authors.initials: G
authors.lastname: Wang
keywords: (100.3190) Inverse problems; (100.6950) Tomographic image processing; (340.7440) X-ray imaging