Hybrid-Domain Neural Network Processing for Sparse-View CT Reconstruction

• Published in: IEEE transactions on radiation and plasma medical sciences Vol. 5; no. 1; pp. 88 - 98
• Main Authors: Hu, Dianlin, Liu, Jin, Lv, Tianling, Zhao, Qianlong, Zhang, Yikun, Quan, Guotao, Feng, Juan, Chen, Yang, Luo, Limin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Bioengineering; Biomedical imaging; Computed tomography; Convolution; Deep learning; Domains; hybrid-domain processing; Image quality; Image reconstruction; Information processing; Interpolation; Life Sciences; Medical imaging; Neural networks; Nondestructive testing; Radiation dosage; residual learning; sparse-view computed tomography (SVCT) reconstruction; X-rays; Deep learning; hybrid-domain processing; sparse-view computed tomography (SVCT) reconstruction; residual learning
• ISSN: 2469-7311; 2469-7303
• DOI: 10.1109/TRPMS.2020.3011413

X-ray computed tomography (CT) is one of the most widely used tools in medical imaging, industrial nondestructive testing, lesion detection, and other applications. However, decreasing the projection number to lower the X-ray radiation dose usually leads to severe streak artifacts. To improve the quality of the images reconstructed from sparse-view projection data, we developed a hybrid-domain neural network (HDNet) processing for sparse-view CT (SVCT) reconstruction in this study. The HDNet decomposes the SVCT reconstruction problem into two stages and each stage focuses on one mission, which reduces the learning difficulty of the entire network. Experiments based on the simulated and clinical datasets are performed to demonstrate the performance of the proposed method. Compared with other competitive algorithms, quantitative and qualitative results show that the proposed method makes a great improvement on artifact suppression, tiny structure restoration, and contrast retention.