Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging applications. However, CBCT suffers from shading artifacts owing to several factors, including photon scattering and data truncation. This paper presents a deep-learning-based method for eliminating the shading artifacts that interfere with the diagnostic and treatment processes.
