Unsupervised Deep Learning Algorithm for Artifact Reduction in X-Ray CT Reconstruction From Truncated Data

We introduce a fully unsupervised framework designed to reconstruct X-ray CT images from truncated projections without requiring prior truncation correction. By incorporating a Radon projection layer as the final layer of a deep learning model and using a projection-based loss function, our method effectively removes truncation-related artifacts, particularly ring artifacts. The framework is demonstrated on small-scale images and further extended to large-scale or arbitrary-scale images. For large-scale reconstruction, fully connected layers are applied in a distributed manner, enabling memory-efficient processing even with limited GPU resources. The effectiveness of the framework is evaluated using PSNR, SSIM, and MAE ± SD metrics. In cases of high-degree truncation, the method achieves consistently higher PSNR and SSIM values and lower MAE ± SD, showing its ability to reduce ring artifacts while preserving reconstruction quality.