The reconstruction of X-rays CT images from sparse or limited-angle geometries is a highly challenging task. The lack of data typically results in artifacts in the reconstructed image and may even lead to object distortions. For this reason, the use of deep generative models in this context has great interest and potential success. In the Deep Generative Prior (DGP) framework, the use of diffusion-based generative models is combined with an iterative optimization algorithm for the reconstruction of CT images from sinograms acquired under sparse geometries, to maintain the explainability of a model-based approach while introducing the generative power of a neural network. There are therefore several aspects that can be further investigated within these frameworks to improve reconstruction quality, such as image generation, the model, and the iterative algorithm used to solve the minimization problem, for which we propose modifications with respect to existing approaches. The results obtained even under highly sparse geometries are very promising, although further research is clearly needed in this direction.

翻译：从稀疏或有限角度几何结构中重建X射线CT图像是一项极具挑战性的任务。数据缺失通常会导致重建图像中出现伪影，甚至可能引起物体形变。因此，在此背景下应用深度生成模型具有重要的研究价值和潜在的成功前景。在深度生成先验（DGP）框架中，我们将基于扩散的生成模型与迭代优化算法相结合，用于从稀疏几何条件下采集的正弦图中重建CT图像，从而在保持基于模型方法可解释性的同时，引入神经网络生成能力。因此，在该框架内仍有多个方面值得进一步研究以提升重建质量，如图像生成、模型设计以及用于求解最小化问题的迭代算法。针对这些方面，我们在现有方法基础上提出了改进方案。即使在高度稀疏的几何条件下，所获得的结果也显示出良好的前景，尽管该方向显然仍需进一步深入研究。