F He1,2, Y Wang1,2, X Tao1, M Zhu1,2, Z Hong1,2, Z Bian1, J Ma1. 1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. 2. Pazhou Lab, Guangzhou, 510330, China.
Abstract
OBJECTIVE: To build a helical CT projection data restoration model at random low-dose levels. METHODS: We used a noise estimation module to achieve noise estimation and obtained a low-dose projection noise variance map, which was used to guide projection data recovery by the projection data restoration module. A filtering back-projection algorithm (FBP) was finally used to reconstruct the images. The 3D wavelet group residual dense network (3DWGRDN) was adopted to build the network architecture of the noise estimation and projection data restoration module using asymmetric loss and total variational regularization. For validation of the model, 1/10 and 1/15 of normal dose helical CT images were restored using the proposed model and 3 other restoration models (IRLNet, REDCNN and MWResNet), and the results were visually and quantitatively compared. RESULTS: Quantitative comparisons of the restored images showed that the proposed helical CT projection data restoration model increased the structural similarity index by 5.79% to 17.46% compared with the other restoration algorithms (P < 0.05). The image quality scores of the proposed method rated by clinical radiologists ranged from 7.19% to 17.38%, significantly higher than the other restoration algorithms (P < 0.05). CONCLUSION: The proposed method can effectively suppress noises and reduce artifacts in the projection data at different low-dose levels while preserving the integrity of the edges and fine details of the reconstructed CT images.
OBJECTIVE: To build a helical CT projection data restoration model at random low-dose levels. METHODS: We used a noise estimation module to achieve noise estimation and obtained a low-dose projection noise variance map, which was used to guide projection data recovery by the projection data restoration module. A filtering back-projection algorithm (FBP) was finally used to reconstruct the images. The 3D wavelet group residual dense network (3DWGRDN) was adopted to build the network architecture of the noise estimation and projection data restoration module using asymmetric loss and total variational regularization. For validation of the model, 1/10 and 1/15 of normal dose helical CT images were restored using the proposed model and 3 other restoration models (IRLNet, REDCNN and MWResNet), and the results were visually and quantitatively compared. RESULTS: Quantitative comparisons of the restored images showed that the proposed helical CT projection data restoration model increased the structural similarity index by 5.79% to 17.46% compared with the other restoration algorithms (P < 0.05). The image quality scores of the proposed method rated by clinical radiologists ranged from 7.19% to 17.38%, significantly higher than the other restoration algorithms (P < 0.05). CONCLUSION: The proposed method can effectively suppress noises and reduce artifacts in the projection data at different low-dose levels while preserving the integrity of the edges and fine details of the reconstructed CT images.
Entities:
Keywords:
deep learning; low-dose helical computed tomography; noise estimation; projection data restoration
Authors: Bruce R Whiting; Parinaz Massoumzadeh; Orville A Earl; Joseph A O'Sullivan; Donald L Snyder; Jeffrey F Williamson Journal: Med Phys Date: 2006-09 Impact factor: 4.071
Authors: Hu Chen; Yi Zhang; Mannudeep K Kalra; Feng Lin; Yang Chen; Peixi Liao; Jiliu Zhou; Ge Wang Journal: IEEE Trans Med Imaging Date: 2017-06-13 Impact factor: 10.048