# Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT
## 1 Introduction  
Sparse-view CT is an imaging technique that collects a small number of projection images within the scanning angle and then reconstructs the CT slices [1]. It can effectively reduce radiation damage, CT scanning time, and data storage volume in high-pixel-number synchrotron radiation CT. However, the projection data collected by sparse-view CT does not satisfy the Nyquist sampling theorem. Reconstruction is an ill-posed inverse problem, and aliasing artifacts will appear in the reconstructed slice. Figure 1 displays the aliasing artifacts in the mouse brain data used in this study under different sparse views. Figure 1a-e shows the slice images reconstructed using FBP at 100 views, 200 views, 300 views, 400 views, and 1200 views (label), respectively. The images (a1-e1) and (a2-e2) are magnified views of the blue and red boxed areas in (a-e), respectively. The magnified images clearly reveal numerous streaking artifacts and noise. To correct aliasing artifacts, researchers have proposed some iterative correction methods based on compressed sensing [2; 3; 4; 5]. These methods introduce constraints to the iterative reconstruction process, greatly improving the quality of the reconstructed image. However, to solve for the optimal values of the objective function iteratively, the iterative approach involves constructing a mathematical model that requires multiple projections and back projections. This process demands substantial computational resources, rendering it impractical for high-pixel-number CT images. The rapid development of deep learning in recent years in the fields of noise reduction [6; 7], segmentation [8; 9], and super-resolution [10; 11; 12] has also provided new ideas for the correction of aliasing artifacts in sparse-view medical CT (512 \(\times\) 512 pixels). The correction of aliasing artifacts in sparse-view CT using deep learning methods is broadly classified into three main categories: sinogram domain methods [13; 14], slice domain methods [15; 16; 17; 18; 19; 20; 21], and the fusion of sinogram domain and slice domain methods [22; 23; 24; 25; 26; 27]. [PERSON] et al. [13] proposed the segmentation of sinograms into patches, followed by the use of a convolutional network to supplement the missing data in the sinogram. [PERSON] et al. [14] also suggested employing deep learning to enlarge a sinogram vertically and interpolate the data to create more projections. The proposed band patch horizontally has the same size as the sinogram and is only cropped vertically. Within the slice domain, [PERSON] et al. [15] proposed an FBPConvNet network to learn the residuals between low-quality inputs and labels. [PERSON] et al. [17] combined image wavelet transform and convolutional network using wavelet transform instead of the pooling operation of U-Net [28]. [PERSON] et al. [19] proposed a DD-Net slice domain network model with an encoding and decoding structure. In the fusion domain, [PERSON] et al. [22] suggested the use of a two-dimensional wavelet transform instead of the down-sampling approach of conventional networks, employing two U-Net models in the sinogram and image domains. [PERSON] [27] proposed supplementing missing  
Figure 1: Aliasing artifacts in the mouse brain data used in this study under different sparse views. (**a–e**) Slice images reconstructed using FBP at 100 views, 200 views, 300 views, 400 views, and 1200 views (label). The images (**a1–e1**) and (**a2–e2**) are magnified views of the blue and red boxed areas in (**a–e**), respectively. data with linear interpolation in the sinogram and using neural networks to correct artifacts in reconstructed images within the slice domain. In synchrotron CT imaging, a 2 k \(\times\) 2 k pixel detector is typically used [29; 30], resulting in both the acquired projection data and the number of pixels in the reconstructed CT image being 2 k \(\times\) 2 k in size.