Enhancing Image Quality in Dental-Maxillofacial CBCT: The Impact of Iterative Reconstruction and AI on Noise Reduction-A Systematic Review.

This systematic review evaluates articles investigating the use of iterative reconstruction (IR) algorithms and artificial intelligence (AI)-based noise reduction techniques to improve the quality of oral CBCT images. A detailed search was performed across PubMed, Scopus, Web of Science, ScienceDirect, and Embase databases. The inclusion criteria were prospective or retrospective studies with IR and AI for CBCT images, studies in which the image quality was statistically assessed, studies on humans, and studies published in peer-reviewed journals in English. Quality assessment was performed independently by two authors, and the conflicts were resolved by the third expert. For bias assessment, the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. A total of eleven studies were included, analyzing a range of IR and AI methods designed to reduce noise and artifacts in CBCT images. A statistically significant improvement in CBCT image quality parameters was achieved by the algorithms used in each of the articles we reviewed. The most commonly used image quality measures were peak signal-to-noise ratio (PSNR) and contrast-to-noise ratio (CNR). The most significant increase in PSNR was demonstrated by Ylisiurua et al. and Vestergaard et al., who reported an increase in this parameter of more than 30% for both deep learning (DL) techniques used. Another subcategory used to improve the quality of CBCT images is the reconstruction of synthetic computed tomography (sCT) images using AI. The use of sCT allowed an increase in PSNR ranging from 17% to 30%. For the more traditional methods, FBP and iterative reconstructions, there was an improvement in the PSNR parameter but not as high, ranging from 3% to 13%. Among the research papers evaluating the CNR parameter, an improvement of 17% to 29% was achieved. The use of AI and IR can significantly improve the quality of oral CBCT images by reducing image noise.