Limited-Angle CT Reconstruction with Generative Adversarial Network Sinogram Inpainting and Unsupervised Artifact Removal  
[PERSON]  
1  
[PERSON]  
2  
[PERSON]  
2 Imer Mongolia Metallic Materials Research Institute, Ningbo 315103, China; [EMAIL_ADDRESS] (P.N.J. ; [EMAIL_ADDRESS] (R.Z.) [PERSON]  
1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/10/](https://www.mdpi.com/journals/pdf/10/)]([https://www.mdpi.com/journals/pdf/10/](https://www.mdpi.com/journals/pdf/10/))  
of CT image reconstruction [16; 17; 18]. [PERSON] et al. [19] discovered that the similar striped artifacts between the reconstructed images by the FBP algorithm in limited-angle sinogram reconstruction, so they proposed a neural network feature fitting framework based on the image domain to learn the mapping between images with artifact and artifact-free images. Inspired by research on image restoration in machine vision, [PERSON] et al. [20] believed that performing image completion and predicting missing information in the projection domain is also a viable approach. They used a context encoder to complete the limited-angle sinogram, and then combined it with an iterative algorithm for the reconstruction. The result proved that a sinogram completion method based on context encoder can effectively improve the efficiency and quality of iterative reconstruction. Given that the above methods are performed in a single image domain or projection domain, [PERSON] et al. [21] proposed hybrid-domain convolutional neural network method, which combined projection domain completion and image domain artifact suppression, but in order to limit the generation of misinformation, supervised constraints from pairs of real sinogram-reconstructed image are necessary [22]. When the amount of training data is sufficient and the network is well trained, the supervised deep learning method can obtain good reconstruction results. In limited-angle CT reconstruction, however, it is difficult to obtain a large number of paired data for supervised training. For this reason, [PERSON] [23] first proposed the Sinogram Inpainting Network (SIN) method to train the network in an unsupervised manner. SIN consists of two parts: The first part is a U-net-based Generative Adversarial Network, which is used to complete the sinograms; The second part is a U-net Generative Adversarial Network of the same structure to learn the mapping from artifact images to artifact-free images. These two parts are connected by differentiable Radon and inverse Radon transform networks. SIN takes a sinogram of size \(256\times 256\) as input and generates a reconstructed image of the same size. The experimental results show that, when the limited angle range is \([0^{\circ},\ 120^{\circ}]\), the performance is better than that of the latest simultaneous algebraic reconstruction technique with Total Variation Regularization (SART-TV) [24] in both qualitative and quantitative aspects. [PERSON] et al. [25] proposed another unsupervised tomography reconstruction adversarial network (Tomo-GAN) based on hybrid-domain optimization. It also consists of two parts: the first part is the Cycle-Consistent Generative Adversarial Networks, which is responsible for mapping the simulation domain sinogram to the real domain sinogram. The second part is the multi-scale conditional GAN, which is developed using both the real-domain sinogram and the corresponding initial FBP reconstruction map as network input. It also introduces a Long-Short-Term Memory to extract features from a sequence of projections from different angles to improve the quality of the reconstruction results. With the above methods, Tomo-GAN successfully enlarged the size of the reconstructed image to \(512\times 512\) and significantly reduced the reconstruction time.