Ct Sinogram Inpainting for Truncation Artifact Correction and Field-Of-View Extension

The field of view (FOV) in Cone Beam Computed tomography (CT) is limited by the detector size, often leading to truncation artifacts that obscure anatomical structures and compromise diagnostic accuracy. While several deep learning solutions have demonstrated success in extending the FOV, they primarily rely on spatial inpainting and do not fully exploit the frequency-domain characteristics of sinograms. Besides, some of the most successful solutions are slow as they work both in the projection and reconstruction domain or use heavy networks, hindering their implementation in real clinical practice. In this work we propose a new deep learning method that works in the sinogram domain solely to achieve accurate and efficient sinogram completion, reducing truncation artifacts while maintaining real-time clinical feasibility. The method is based on a recurrent network that incorporates Fast Fourier Convolution blocks at early feature extraction stages to enhance sinogram extrapolation by leveraging spectral domain information. The proposed method holds potential for improving CT image reconstruction quality, enhancing diagnostic accuracy, and expanding the applicability of C -arm CT systems in various medical fields.