A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images - researchr publication related

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Yongfeng Gao, Zhengrong Liang, William Moore, Hao Zhang 0026, Marc J. Pomeroy, John A. Ferretti, Thomas V. Bilfinger, Jianhua Ma, Hongbing Lu. A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images. IEEE Trans. Med. Imaging, 38(8):1981-1992, 2019. [doi]

The following publications are possibly variants of this publication:

Erratum to "Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images"Hao Zhang, Hao Han, Zhengrong Liang, Yifan Hu, Yan Liu, William Moore, Jianhua Ma, Hongbing Lu. tmi, 35(6):1587, 2016. [doi]

Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT ImagesHao Zhang, Hao Han, Zhengrong Liang, Yifan Hu, Yan Liu, William Moore, Jianhua Ma, Hongbing Lu. tmi, 35(3):860-870, 2016. [doi]

Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CTYongfeng Gao, Jiaxing Tan, Yongyi Shi, Hao Zhang 0026, Siming Lu, Amit Gupta, Haifang Li, Michael Reiter, Zhengrong Liang. tmi, 42(11):3129-3139, November 2023. [doi]

Full Dose CT Database Induced Reconstruction With Nonlocal Means Prior for Ultra-Low-Dose Lung CT: A Preliminary StudyWenlei Liu, Peng Gao, Yuanke Zhang, Tianshuai Liu, Huangsheng Pu, Junyan Rong, Hongbing Lu. access, 7:154346-154359, 2019. [doi]

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