Wajer Róża, Dabrowski-Tumanski Pawel, Wajer Adrian, Kazimierczak Natalia, Serafin Zbigniew, Kazimierczak Wojciech
Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland.
Faculty of Mathematics and Natural Sciences, School of Exact Sciences, Cardinal Stefan Wyszynski University, 01-815 Warsaw, Poland.
J Clin Med. 2025 Jun 13;14(12):4214. doi: 10.3390/jcm14124214.
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.
本系统评价评估了有关使用迭代重建（IR）算法和基于人工智能（AI）的降噪技术来提高口腔锥形束计算机断层扫描（CBCT）图像质量的文章。我们在PubMed、Scopus、科学网、ScienceDirect和Embase数据库中进行了详细检索。纳入标准为针对CBCT图像使用IR和AI的前瞻性或回顾性研究、对图像质量进行统计学评估的研究、针对人类的研究以及发表在英文同行评审期刊上的研究。由两位作者独立进行质量评估，冲突由第三位专家解决。对于偏倚评估，使用诊断准确性研究的质量评估（QUADAS）-2工具进行偏倚评估。共纳入11项研究，分析了一系列旨在减少CBCT图像噪声和伪影的IR和AI方法。我们所综述的每篇文章中使用的算法均使CBCT图像质量参数有统计学意义的提高。最常用的图像质量指标是峰值信噪比（PSNR）和对比噪声比（CNR）。Ylisiurua等人和Vestergaard等人展示了PSNR最显著的提高，他们报告称，所使用的两种深度学习（DL）技术的该参数均提高了30%以上。另一个用于提高CBCT图像质量的子类别是使用AI重建合成计算机断层扫描（sCT）图像。使用sCT可使PSNR提高17%至30%。对于更传统的方法，即滤波反投影（FBP）和迭代重建，PSNR参数有所改善，但幅度没那么大，为3%至13%。在评估CNR参数的研究论文中，实现了17%至29%的改善。使用AI和IR可以通过减少图像噪声显著提高口腔CBCT图像的质量。
