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Article type: Research Article
Authors: Wu, Xiaochuana | He, Penga; b; c; * | Long, Zouronga | Guo, Xiaodonga | Chen, Mianyia | Ren, Xuezhia | Chen, Peijuna | Deng, Luzhena | An, Kangc | Li, Pengchenga | Wei, Biaoa; b; c | Feng, Penga; b; c; *
Affiliations: [a] The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China | [b] Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China | [c] ICT NDT Engineering Research Center, Ministry of Education, Chongqing University, Chongqing, China
Correspondence: [*] Corresponding authors. Peng He and Peng Feng E-mails: penghe@cqu.edu.cn and coe-fp@cqu.edu.cn
Abstract: BACKGROUND:Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which has the potential to distinguish different material compositions. Although material decomposition methods based on x-ray attenuation characteristics have good performance in dual-energy CT imaging, there are some limitations in terms of image contrast and noise levels. OBJECTIVE:This study focused on multi-material decomposition of spectral CT images based on a deep learning approach. METHODS:To classify and quantify different materials, we proposed a multi-material decomposition method via the improved Fully Convolutional DenseNets (FC-DenseNets). A mouse specimen was first scanned by spectral CT system based on a photon-counting detector with different energy ranges. We then constructed a training set from the reconstructed CT images for deep learning to decompose different materials. RESULTS:Experimental results demonstrated that the proposed multi-material decomposition method could more effectively identify bone, lung and soft tissue than the basis material decomposition based on post-reconstruction space in high noise levels. CONCLUSIONS:The new proposed approach yielded good performance on spectral CT material decomposition, which could establish guidelines for multi-material decomposition approaches based on the deep learning algorithm.
Keywords: Spectral CT, photon-counting detector, material decomposition, deep learning
DOI: 10.3233/XST-190500
Journal: Journal of X-Ray Science and Technology, vol. 27, no. 3, pp. 461-471, 2019
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