An effective sinogram inpainting for complementary limited-angle dual-energy computed tomography imaging using generative adversarial networks

作者：Yizhong Wang, Wenkun Zhang, Ailong Cai, Linyuan Wang, Chao Tang, Zhiwei Feng, Lei Li, Ningning Liang, Bin Yan · 发表于：Journal of X-Ray Science and Technology · 年份：2020 · DOI：10.3233/xst-200736 · 被引用次数：17 · 研究领域：Advanced X-ray and CT Imaging、Medical Imaging Techniques and Applications、Advanced X-ray Imaging Techniques

Dual-energy computed tomography (DECT) provides more anatomical and functional information for image diagnosis. Presently, the popular DECT imaging systems need to scan at least full angle (i.e., 360°). In this study, we propose a DECT using complementary limited-angle scan (DECT-CL) technology to reduce the radiation dose and compress the spatial distribution of the imaging system. The dual-energy total scan is 180°, where the low- and high-energy scan range is the first 90° and last 90°, respectively. We describe this dual limited-angle problem as a complementary limited-angle problem, which is challenging to obtain high-quality images using traditional reconstruction algorithms. Furthermore, a complementary-sinogram-inpainting generative adversarial networks (CSI-GAN) with a sinogram loss is proposed to inpainting sinogram to suppress the singularity of truncated sinogram. The sinogram loss focuses on the data distribution of the generated sinogram while approaching the target sinogram. We use the simultaneous algebraic reconstruction technique namely, a total variable (SART-TV) algorithms for image reconstruction. Then, taking reconstructed CT images of pleural and cranial cavity slices as examples, we evaluate the performance of our method and numerically compare different methods based on root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Compared with traditional algorithms, the proposed network shows advantages in numeri...