Low-dose CT Via Convolutional Neural Network

Overview

Journal Biomed Opt Express

Specialty Radiology

Date 2017 Mar 9

PMID 28270976

Citations 147

Authors

Hu Chen

Yi Zhang

Weihua Zhang

Peixi Liao

Ke Li

Jiliu Zhou

Ge Wang

Affiliations

Soon will be listed here.

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.

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