The reconstruction of high-quality images from limited angular sparse-view tomography data is desired in many fields, including low-dose computed tomography and nondestructive detection. In this study, two regularization terms based on the assumptions of image continuity and nonzero pixels are utilized to reconstruct an image from incomplete data. By transforming the original nonlinear reconstruction model into a linear programming, the proposed method can obtain the optimal solution rather than the local minimum point, which helps to accurately reconstruct the images. To demonstrate the effect of the proposed method, we designed a simulation experiment under limited angle, few-view, and limited angular sparse-view conditions, and evaluated the reconstruction results using the structural similarity index, peak signal-to-noise ratio, and cosine distance. The experimental results show that our method can effectively overcome the adverse effects of sparse-view conditions and a limited scanning range. In the tomographic results of our method, streak artifacts do not appear, even when using projection data from 20 views of a 180° scanning angular range.

在许多领域，包括低剂量计算机断层扫描和无损检测，都需要从有限的角度稀疏视图断层扫描数据中重建高质量图像。在这项研究中，基于图像连续性和非零像素的两个正则化项被用来从不完整的数据中重建图像。通过将原始的非线性重建模型转化为线性规划，该方法可以获得最优解而不是局部最小点，从而有助于准确重建图像。为了证明该方法的效果，我们设计了一个在有​​限角度，少视角和有限角度稀疏视角条件下的仿真实验，并使用结构相似性指标，峰信噪比，和余弦距离。实验结果表明，我们的方法可以有效克服稀疏视图条件和有限的扫描范围的不利影响。在我们方法的层析成像结果中，即使使用来自180°扫描角度范围的20个视图的投影数据，也不会出现条纹伪影。