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Proceedings Paper

Unsupervised data fidelity enhancement network for spectral CT reconstruction
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Paper Abstract

Deep learning (DL) networks show a great potential in computed tomography (CT) imaging field. Most of them are supervised DL network greatly based on their capability and the amount of CT training data (i.e., low-dose CT measurements/high-quality ones). However, collection of large-scale CT datasets are time-consuming and expensive. In addition, the training and testing CT datasets used for supervised DL network are highly desired similarities in CT scan protocol (i.e., similar anatomical structure, and same kVp setting). These two issues are particularly critical in spectral CT imaging. In this work, to address the issues, we presents an unsupervised data fidelity enhancement network (USENet) to produce high-quality spectral CT images. Specifically, the presented USENet consists of two parts, i.e., supervised network and unsupervised network. In the supervised network, the spectral CT image pairs at 140 kVp (low-dose CT images/high-dose ones) are used for network training. It should be noted that there is a great difference of CT value between spectral CT images at 140 kVp and 80 kVp, and the supervised network trained with CT images at 140 kVp cannot be directly used for CT image reconstruction at 80 kVp. Then unsupervised network enrolls physical model and the spectral CT measurements at 80 kVp for fine-tuning the supervised network, which is the major contribution of the presented USENet method. Finally, accurate spectral CT reconstructions are achieved for the sparse-view and low-dose cases, which fully demonstrate the effectiveness of the presented USENet method.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124D (16 March 2020); doi: 10.1117/12.2548893
Show Author Affiliations
Danyang Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Sui Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Manman Zhu, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Qi Gao, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Zhaoying Bian, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Haiyun Huang, South China Univ. of Technology (China)
Shanli Zhang, The First Affiliated Hospital of Guangzhou Univ. of Traditional Chinese Medicine (China)
Jing Huang, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Dong Zeng, South China Univ. of Technology (China)
Jianhua Ma, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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