Artificial Intelligence for Artifact Reduction in Cone Beam Computed Tomographic Images: A Systematic Review

Abstract

Cone beam computed tomography (CBCT) allows for rapid and accessible acquisition of three-dimensional images with a lower radiation dose compared to conventional computed tomography (CT) scans. However, the quality of CBCT images is limited by a variety of artifacts. This systematic review attempts to explore different artificial intelligence-based solutions for enhancing the quality of CBCT scans and reducing different types of artifacts in these three-dimensional images. PubMed, Web of Science, Scopus, Embase, Cochrane, and Google Scholar were searched up to March 2025. Risk of bias of included studies was assessed using the QUADAS-II tool. Extracted data included bibliographic information, aim, imaging modality, anatomical site of interest, artificial intelligence modeling approach and details, data and dataset details, qualitative and quantitative performance metrics, and main findings. A total of 27 papers from 2018 to 2025 were included. These studies focused on five areas: metal artifact reduction, scatter correction, image reconstruction improvement, motion artifact reduction, and noise reduction. Artificial intelligence models mainly used U-Net variants, though hybrid and transformer-based models were also explored. The thoracic region was the most analyzed, and the structural similarity index measure and peak signal-to-noise-ratio were common performance metrics. Data availability was limited, with only 26 of studies providing public access and 15 sharing model source codes. Artificial intelligence-driven approaches have demonstrated promising results for CBCT artifact reduction. This review highlights a wide variability in performance assessments and that most studies have not received diagnostic validation, limiting conclusions on the true clinical impact of these artificial intelligence-based improvements.