Objective:Fixed denture is a common oral metal implant in clinic.Its materials are various and often non-removable.In the head and neck CT scan,the fixed denture often produces metal artifacts,which seriously interferes with the accurate display of the surrounding tissue.This systematic review aims to comprehensively summarize the latest progress of energy CT in reducing metal artifacts caused by fixed dentures in head and neck CT scans,so as to improve image quality and promote accurate clinical diagnosis.Methods:This study extensively reviewed and analyzed the recent literature on the removal of metal artifacts in fixed dentures by energy CT.Four main methods are discussed:scanning parameter optimization,algorithm processing(including iterative reconstruction IR,virtual monoenergetic images VMI and metal artifact reductionMAR),combined application of various post-processing techniques,and methods based on deep learning.Results:The optimization of scanning parameters was simple and easy,but the ability to remove metal artifacts was limited,and there was a risk of increasing radiation exposure.Algorithm processing methods such as IR,VMI and MAR show varying degrees of effectiveness,and the combined use performs better than a single technology.In addition,the emerging deep learning methods maintain high-quality images while significantly reducing metal artifacts.Conclusion:This review highlights the progress made in reducing metal artifacts caused by fixed dentures in head and neck scans using energy CT.The combined use of multiple post-processing techniques and the potential of deep learning methods provide hope for future research directions,aiming to improve the quality of CT images and enhance the accuracy of clinical diagnosis.
关键词
能量CT/固定义齿/金属伪影/伪影减少技术/深度学习
Key words
energy computed tomography/fixed denture/metal artifacts/artifact reduction techniques/deep learning
维普
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