PDF(Pubmed)
Abstract：
UNASSIGNED: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations.
UNASSIGNED: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.
UNASSIGNED: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations.
UNASSIGNED: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.
UNASSIGNED: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.
UNASSIGNED: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations.
UNASSIGNED: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.
摘要：
■计算机断层扫描（CT）图像重建技术的最新进展是深度学习图像重建（DLIR）算法。由于迭代重建（IR）技术的缺点，例如负图像纹理和非线性空间分辨率，DLIR正在逐渐取代它们。然而,DLIR在头部和胸部CT中的潜在应用需要进一步检查。因此，该研究的目的是回顾DLIR对辐射剂量（RD）的影响，图像噪声(IN),以及在头部和胸部CT检查中与IR和FBP进行比较的研究结果。
■我们在PubMed中进行了详细的搜索，Scopus,WebofScience,科克伦图书馆,和Embase查找2017年至2023年间使用DLIR进行头部和胸部CT检查报告的文章。使用系统审查和荟萃分析（PRISMA）指南的首选报告项目从入围研究中检索数据。
■在搜索的196篇文章中，共包括15篇文章。总共包括1292个样本量。14篇被评为高，1篇被评为中等质量。所有研究都将DLIR与IR技术进行了比较。5项研究比较了DLIR与IR和FBP。综述显示DLIR提高了智商，CT头部和胸部检查的RD和IN降低。
■DLIR算法显示，与IR和FBP相比，低剂量CT头部和胸部检查的智商明显增强，IN降低。DLIR显示出通过降低辐射风险和提高诊断准确性来增强患者护理的潜力。
■我们在PubMed中进行了详细的搜索，Scopus,WebofScience,科克伦图书馆,和Embase查找2017年至2023年间使用DLIR进行头部和胸部CT检查报告的文章。使用系统审查和荟萃分析（PRISMA）指南的首选报告项目从入围研究中检索数据。
■在搜索的196篇文章中，共包括15篇文章。总共包括1292个样本量。14篇被评为高，1篇被评为中等质量。所有研究都将DLIR与IR技术进行了比较。5项研究比较了DLIR与IR和FBP。综述显示DLIR提高了智商，CT头部和胸部检查的RD和IN降低。
■DLIR算法显示，与IR和FBP相比，低剂量CT头部和胸部检查的智商明显增强，IN降低。DLIR显示出通过降低辐射风险和提高诊断准确性来增强患者护理的潜力。
