Artifact Reduction in 3D and 4D Cone-Beam Computed Tomography Images With Deep Learning: A Review

Deep learning based approaches have been used to improve image quality in\ncone-beam computed tomography (CBCT), a medical imaging technique often used in\napplications such as image-guided radiation therapy, implant dentistry or\northopaedics. In particular, while deep learning methods have been applied to\nreduce various types of CBCT image artifacts arising from motion, metal\nobjects, or low-dose acquisition, a comprehensive review summarizing the\nsuccesses and shortcomings of these approaches, with a primary focus on the\ntype of artifacts rather than the architecture of neural networks, is lacking\nin the literature. In this review, the data generation and simulation\npipelines, and artifact reduction techniques are specifically investigated for\neach type of artifact. We provide an overview of deep learning techniques that\nhave successfully been shown to reduce artifacts in 3D, as well as in\ntime-resolved (4D) CBCT through the use of projection- and/or volume-domain\noptimizations, or by introducing neural networks directly within the CBCT\nreconstruction algorithms. Research gaps are identified to suggest avenues for\nfuture exploration. One of the key findings of this work is an observed trend\ntowards the use of generative models including GANs and score-based or\ndiffusion models, accompanied with the need for more diverse and open training\ndatasets and simulations.\n