## 1 Introduction  
X-ray Computed Tomography (CT) has found extensive utilization in clinical practice. Compared with other radiological examinations, CT can provide higher resolution medical tomographic anatomical images. Although CT brings great convenience to patients in diagnosing diseases, the hazards of the radiation generated by CT scans of the human body also gradually attracted attention [1]. In recent years, Low-Dose CT reconstruction (LDCT) has become a research hotspot [2]. Two main methods are used to reduce the CT radiation dose: reducing the tube current [3] and reducing the sampling views [4, 5]. Both ways may introduce noise and artifacts that do not belong to tissue structures, resulting in a loss of image details and sharpness, which will affect the doctor's diagnosis. To improve the reconstructed CT image quality,existing LDCT methods use core processing modules (e.g., denoising or interpolation) in the sinogram domain [6; 7], image domain [8; 9; 10; 11], or both domains simultaneously [12; 13; 14; 15; 16], or design new iterative reconstruction algorithms [17; 18]. This work focuses on sinogram domain processing for sparse-view LDCT reconstruction, i.e., improving LDCT by enhancing the projection data in the sinogram domain. In the last decade, with the revival of neural networks, LDCT reconstruction based on deep learning has gradually become a research hotspot. [PERSON] et al. [19] applied a deep learning method to denoise LDCT images in the image domain, employing a simple network consisting of three convolution layers and an activation function. Since then, a number of networks originating from computer vision tasks have also been continuously used for LDCT reconstruction, including supervised [20; 21; 22; 23] and unsupervised networks [15; 16]. In [20], the well-known Residual Encoder-Decoder (RED) network was used for LDCT reconstruction. Generative Adversarial Networks (GANs) have also been introduced into LDCT. [PERSON] et al. [21] proposed a conditional GAN for LDCT reconstruction, which was composed of generative, discriminant and sharp-detection networks. In addition to image domain denoising, LDCT can be achieved by processing in the sinogram domain. In [22; 23], convolutional neural networks were used directly to denoise the projected data and construct high-quality CT images through back projection. [PERSON] et al. [6] introduced a deep U-Net approach for the purpose of interpolating the projected data. To solve the data consistency problem of sinograms, [PERSON] et al. [8] proposed a sinogram synthesis method with prior knowledge constraints. Compared to image domain methods, sine domain methods are usually more efficient at coping with noise or insufficient projection data due to low doses. CT images reconstructed from low-quality sinograms usually contain more noise and artifacts [14], which are difficult to remove completely because it is hard to model their distributions in the image domain [5]. Recently, several dual-domain, end-end networks have been proposed, which act in both domains to cope with the low dose problem. [PERSON] et al. [24] proposed an end-to-end deep learning method which incorporates a fan beam projection layer to establish a connection between the two domains. In [12], a domain progressive 3D residual convolution network (DP-ResNet) was proposed for CT reconstruction in both the sinogram domain and the image domain. The dual-domain networks proposed in [12; 24] have serial structures. Due to the sequential nature of the process, errors occurring in the sinogram domain have a tendency to accumulate and propagate in the image domain. [PERSON] et al. [14] introduced a dual-domain network with a parallel structure in their work. This network was designed to combine two-domain information and avoid cumulative errors. Guided by the saying \"the deeper the better\", the structure of LDCT networks has a tendency to become increasingly complex, which means longer reconstruction times and higher computational costs.