Cone Beam Computed tomography (CT) imaging is widely used in diverse clinical applications, but its field of view (FOV) is often limited by the detector size, 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 recurrent network that works in the sinogram domain solely and that incorporates Fast Fourier Convolution blocks at early feature extraction stages, to enable larger receptive fields and enhance sinogram extrapolation by leveraging spectral domain information. Our approach aims to achieve accurate and efficient sinogram completion, reducing truncation artifacts while maintaining real-time clinical feasibility. 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.