Image Denoising and Ring Artifacts Removal for Spectral CT via Deep Neural Network

IEEE Access

Biao Wei¹  Mi Zhou²  Chengyu Fan³  Xiaodong Guo⁴  Peng He⁴  Zourong Long⁴  Peng Feng⁴  Xuezhi Ren⁴  Xiaojie Lv⁴ 

[1] Champaign, Champaign, IL, USA;College of Science, Chongqing University of Technology, Chongqing, China;Deparment of Electrical and Computer Engineering, University of Illinois Urbana&x2013;Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China;

关键词: Spectral CT;    image denoising;    ring artifacts removal;    deep learning;   

DOI  :  10.1109/ACCESS.2020.3044708

来源: DOAJ

【 摘 要 】

The spectral computed tomography (CT) based on photon counting detectors can collect the incident photons with different energy ranges. However, due to the low photon counts in narrow energy bin and the unhomogeneous response problem of detector cells, there are severe noise and ring artifacts in reconstructed spectral CT images. We proposed an image denoising and ring artifacts removal method via improved Fully Convolutional Pyramid Residual Network (FCPRN). In our study, we scanned a mouse specimen with spectral CT based on photon counting detector, and reconstructed mouse CT images as data set. Then we use the data set to train our network for image denoising and ring artifacts removal. Experimental results demonstrated that the proposed method could reduce noise and suppress ring artifacts of spectral CT images concurrently in different energy ranges. And the performance of the FCPRN is better than that of some networks for CT image denoising.

【 授权许可】

Unknown