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

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 qu...