## 1 Introduction
designed a spatially structural two-dimensional dictionary and a motion-aware dictionary for four-dimensional CT reconstruction [25]. But most DL methods extract patches from the image and operate based on a patch, which causes concern that the image may render new patch aggregation artifacts at the patch boundary. Therefore, the convolutional sparse coding (CSC) method was presented and achieved better reconstruction results [26; 27]. Different from the afore-mentioned methods, it is worth emphasizing that the quality of CT images will be improved if a prior image is introduced. Therefore, in 2010, [PERSON] et al. proposed a method based on prior image constrained compressed sensing (PICCS), which reduced noise and radiation dose in breast cone beam CT imaging [28]. [PERSON] et al. designed a novel denoising method based on PICCS for dual energy CT [29]. [PERSON] et al. presented an adaptive PICCS method to enhance 4D CBCT reconstruction image quality [30]. [PERSON] et al. proposed a powerful statistical image reconstruction algorithm for CT for which prior information obtained from a probabilistic atlas is modeled for the CT image reconstruction [31]. [PERSON] et al. applied the PICCS reconstruction method in the cardiac interventional setting, which solved the view angle under-sampling and projection data truncation problems, causing by cardiac gating and the small size of the detector [32]. [PERSON] et al. proposed a sparse-view CT reconstruction method based on PICCS, which introduced a prior image to accurately reconstruct dynamic CT images from under-sampled projection datasets, and achieved good results [33; 34]. Later, the methods based on PICCS were widely used in energy spectrum CT reconstruction, which greatly improved the quality of energy spectrum CT reconstruction [35; 36; 37; 38]. However, most PICCS-based methods are based on TV sparse framework, and the gradient \(L_{1}\)-norm may cause blurred edges and loss image details. Recently, the image gradient \(L_{0}\)-norm has been extensively used in image restoration, image segmentation, and image smoothing [39; 40; 41]. It achieved better performance in sparse-view, limited-view, and LDCT reconstruction such as edge preservation, artifact reduction, and noise suppression [42; 43]. The primary reason is that the image gradient \(L_{0}\)-norm does not penalize large image gradient magnitudes, which may be more suitable for preserving image edge and reducing noise. In this work, based on the pioneering research of PICCS, we propose a novel improved PICCS algorithm for SVCT reconstruction (NPICCS). Firstly, the algorithm continues to utilize the advantage of prior image constrained compressed sensing, which recovers more details. Moreover, by introducing the \(L_{0}\)-norm of the image gradient regularization term into the algorithm framework, not only the edge information can be effectively preserved, but also noise can be greatly reduced. The major contribution of this article is manifested in the following two aspects. Firstly, the innovative introduction of \(L_{0}\)-norm regularization to the PICCS framework for SVCT reconstruction not only enhances the ability to preserve image details, but also reduces noise and artifacts in the images. Secondly, an effective split Bregman algorithm is employed to solve the NPICCS optimization model. The remainder of the article is structured as follows. In Section 2, some relevant algorithm models and fundamental theory are briefly introduced. The method to solve the algorithm model is described in Section 3. Section 4 discusses and compares some representative experiments. In the last section, we present the conclusion and discussion.