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 toreduce 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 numerical terms. Compared with Patch-GAN, the proposed network can also reduce the RMSE of the reconstruction results by an average of 40% and increase the PSNR by an average of 26%. In conclusion, both qualitative and quantitative comparison and analysis demonstrate that our proposed method achieves a good artifact suppression effect and can suitably solve the complementary limited-angle problem.

中文翻译：

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使用生成对抗网络进行互补有限角度双能计算机断层扫描成像的有效正弦图修复

双能计算机断层扫描 (DECT) 为图像诊断提供更多解剖和功能信息。目前，流行的 DECT 成像系统至少需要扫描全角（即 360°）。在这项研究中，我们提出了一种使用互补有限角度扫描 (DECT-CL) 技术的 DECT，以减少辐射剂量并压缩成像系统的空间分布。双能总扫描为180°，其中低能和高能扫描范围分别为前90°和后90°。我们将这种双重有限角度问题描述为互补有限角度问题，这对于使用传统重建算法获得高质量图像具有挑战性。此外，提出了一种具有正弦图损失的互补正弦图修复生成对抗网络（CSI-GAN）来修复正弦图以抑制截断正弦图的奇异性。正弦图损失侧重于生成的正弦图在接近目标正弦图时的数据分布。我们使用同步代数重建技术，即全变量 (SART-TV) 算法进行图像重建。然后，以重建的胸膜和颅腔切片 CT 图像为例，我们评估了我们方法的性能，并基于均方根误差 (RMSE)、峰值信噪比 (PSNR) 和结构相似性对不同方法进行了数值比较(SSIM)。与传统算法相比，所提出的网络在数值方面显示出优势。与 Patch-GAN 相比，所提出的网络还可以将重建结果的 RMSE 平均降低 40%，并将 PSNR 平均提高 26%。总之，定性和定量比较和分析表明，我们提出的方法取得了良好的伪影抑制效果，可以很好地解决互补的有限角度问题。