  Deep learning based approaches have been used to improve image quality in
cone-beam computed tomography (CBCT), a medical imaging technique often used in
applications such as image-guided radiation therapy, implant dentistry or
orthopaedics. In particular, while deep learning methods have been applied to
reduce various types of CBCT image artifacts arising from motion, metal
objects, or low-dose acquisition, a comprehensive review summarizing the
successes and shortcomings of these approaches, with a primary focus on the
type of artifacts rather than the architecture of neural networks, is lacking
in the literature. In this review, the data generation and simulation
pipelines, and artifact reduction techniques are specifically investigated for
each type of artifact. We provide an overview of deep learning techniques that
have successfully been shown to reduce artifacts in 3D, as well as in
time-resolved (4D) CBCT through the use of projection- and/or volume-domain
optimizations, or by introducing neural networks directly within the CBCT
reconstruction algorithms. Research gaps are identified to suggest avenues for
future exploration. One of the key findings of this work is an observed trend
towards the use of generative models including GANs and score-based or
diffusion models, accompanied with the need for more diverse and open training
datasets and simulations.
