【摘要】 深度展开网络由于可解释性高和学习能力强的特点受到广泛关注。现有CT图像重建算法的正则化项大多只关注某一类域中的信息，重建结果常存在边缘模糊等信息丢失的问题。基于此，提出一种基于稀疏变换先验约束的深度展开网络，用于进行稀疏角度CT重建。考虑像素域信息和变换域信息对图像重建具有重要作用，构建结合变换域稀疏正则化项和像素域一致性正则化项2种具有信息互补作用的正则化项，据此重新设计稀疏角度CT重建的目标函数。根据所构建的目标函数进行迭代优化求解，将得到的一系列约束关系映射为新的深度展开网络，用于低剂量CT图像的迭代重建。实验结果表明：所换算法得到的重建结果与经典的FISTA算法相比，平均峰值信噪比(PSNR)和视觉信息保真度(VIF)均有较大改善。更多还原

【Abstract】 Deep iterative unfolding networks have garnered a lot of attention lately because of their great learning capabilities and good interpretability. The regularization terms in existing CT image reconstruction methods mostly focus on information within a specific domain, leading to issues such as edge blurring and information loss in the reconstructed results. Therefore, a sparse transform prior constrain based deep unfolding network is proposed for sparse-view CT reconstruction. Two regularization terms with complementary information—transform-domain sparse regularization and pixel-domain consistency regularization—are created in consideration of the important roles that both pixel-domain and transform-domain information play in picture reconstruction. Based on these, the objective function for sparse-view CT reconstruction is redesigned. Furthermore, a new deep unfolding network for iterative reconstruction of low-dose CT is created by mapping a set of constraint relationships established from an iterative optimization solution for the constructed objective function. Experimental results demonstrate that the algorithm presented in this paper achieves a great improvement on average peak signal to noise ratio(PSNR) and visual information fidelity(VIF) compared to the classical FISTA algorithms.更多还原