Attention-based dual-branch deep network for sparse-view computed tomography image reconstruction

Quantitative Imaging in Medicine and Surgery

article

Xiang Gao¹  Ting Su¹  Yunxin Zhang³  Jiongtao Zhu¹  Yuhang Tan¹  Han Cui¹  Xiaojing Long¹  Hairong Zheng⁵  Dong Liang¹  Yongshuai Ge¹ 

[1] Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology , Chinese Academy of Sciences;University of Chinese Academy of Sciences;Department of Vascular Surgery , Beijing Jishuitan Hospital;College of Physics and Optoelectronic Engineering , Shenzhen University;Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology , Chinese Academy of Sciences

关键词: CT reconstruction;    sparse-view CT;    streak artifact;    deep learning;    attention;   

DOI  :  10.21037/qims-22-609

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: The widespread application of X-ray computed tomography (CT) imaging in medical screening makes radiation safety a major concern for public health. Sparse-view CT is a promising solution to reduce the radiation dose. However, the reconstructed CT images obtained using sparse-view CT may suffer severe streaking artifacts and structural information loss. Methods: In this study, a novel attention-based dual-branch network (ADB-Net) is proposed to solve the ill-posed problem of sparse-view CT image reconstruction. In this network, downsampled sinogram input is processed through 2 parallel branches (CT branch and signogram branch) of the ADB-Net to independently extract the distinct, high-level feature maps. These feature maps are fused in a specified attention module from 3 perspectives (channel, plane, and spatial) to allow complementary optimizations that can mitigate the streaking artifacts and the structure loss in sparse-view CT imaging. Results: Numerical simulations, an anthropomorphic thorax phantom, and in vivo preclinical experiments were conducted to verify the sparse-view CT imaging performance of the ADB-Net. The proposed network achieved a root-mean-square error (RMSE) of 20.6160, a structural similarity (SSIM) of 0.9257, and a peak signal-to-noise ratio (PSNR) of 38.8246 on numerical data. The visualization results demonstrate that this newly developed network can consistently remove the streaking artifacts while maintaining the fine structures. Conclusions: The proposed attention-based dual-branch deep network, ADB-Net, provides a promising alternative to reconstruct high-quality sparse-view CT images for low-dose CT imaging.

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