Gao Xiang, Su Ting, Zhang Yunxin, Zhu Jiongtao, Tan Yuhang, Cui Han, Long Xiaojing, Zheng Hairong, Liang Dong, Ge Yongshuai
Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
University of Chinese Academy of Sciences, Beijing, China.
Quant Imaging Med Surg. 2023 Mar 1;13(3):1360-1374. doi: 10.21037/qims-22-609. Epub 2023 Feb 10.
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.
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.
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.
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.
X射线计算机断层扫描（CT）成像在医学筛查中的广泛应用使辐射安全成为公共卫生的主要关注点。稀疏视图CT是降低辐射剂量的一种有前景的解决方案。然而，使用稀疏视图CT获得的重建CT图像可能会出现严重的条纹伪影和结构信息丢失。
在本研究中，提出了一种新型的基于注意力的双分支网络（ADB-Net）来解决稀疏视图CT图像重建的不适定问题。在该网络中，下采样后的正弦图输入通过ADB-Net的2个并行分支（CT分支和正弦图分支）进行处理，以独立提取不同的高级特征图。这些特征图在一个指定的注意力模块中从3个角度（通道、平面和空间）进行融合，以实现互补优化，减轻稀疏视图CT成像中的条纹伪影和结构损失。
进行了数值模拟、拟人化胸部模型和体内临床前实验，以验证ADB-Net的稀疏视图CT成像性能。所提出的网络在数值数据上实现了20.6160的均方根误差（RMSE）、0.9257的结构相似性（SSIM）和38.8246的峰值信噪比（PSNR）。可视化结果表明，这种新开发的网络能够在保持精细结构的同时持续去除条纹伪影。
所提出的基于注意力的双分支深度网络ADB-Net为低剂量CT成像重建高质量稀疏视图CT图像提供了一种有前景的替代方案。
