Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network

被引:29
|
作者
Xu, Yifu [1 ]
Yan, Bin [1 ]
Zhang, Jingfang [2 ]
Chen, Jian [1 ]
Zeng, Lei [1 ]
Wang, Linyuang [1 ]
机构
[1] Natl Digital Switching Syst Engn & Technol R&D Ct, Zhengzhou 450002, Henan, Peoples R China
[2] 153 Cent Hosp Henan Prov, Zhengzhou 450002, Henan, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
NOISE SUPPRESSION; CT;
D O I
10.1155/2018/2527516
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background. Dual-energy computed tomography (DEC'') has been widely used due to improved substances identification from additional spectral information. The quality of material-specific image produced by DECT attaches great importance to the elaborated design of the basis material decomposition method. Objective.1 he aim of this work is to develop and validate a data-driven algorithm for the image-based decomposition problem. Methods. A deep neural net, consisting of a fully convolutional net (FCN) and a fully connected net, is proposed to solve the material decomposition problem. The former net extracts the feature representation of input reconstructed images, and the latter net calculates the decomposed basic material coefficients from the joint feature vector. The whole model was trained and tested using a modified clinical dataset. Results. The proposed FCN delivers image with about 60% smaller bias and 70% lower standard deviation than the competing algorithms, suggesting its better material separation capability. Moreover, FCN still yields excellent performance in case of photon noise. Conclusions. Our deep cascaded network features high decomposition accuracies and noise robust property. The experimental results have shown the strong function fitting ability of the deep neural network. Deep learning paradigm could be a promising way to solve the nonlinear problem in DECT.
引用
收藏
页数:9
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