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The evolution and current situation in the application of dual-energy computed tomography: a bibliometric study

计算机断层摄影术 电流(流体) 计算机科学 医学物理学 双重能量 对偶(语法数字) 放射科 医学 物理 病理 热力学 文学类 艺术 骨质疏松症 骨矿物
作者
Ya Wang,Yun Peng,Tongtong Wang,Hui Li,Zhao Zhen,Lianggeng Gong,Bibo Peng
出处
期刊:Quantitative imaging in medicine and surgery [AME Publishing Company]
卷期号:13 (10): 6801-6813
标识
DOI:10.21037/qims-23-467
摘要

Dual-energy computed tomography (DECT) has received extensive attention in clinical practice; however, a quantitative assessment of published literature in this domain is presently lacking. This study thus aimed to characterize the application conditions, developmental trends, and research hot spots of DECT using bibliometric analysis.All literature on DECT was retrieved from the Web of Science Core Collection (WoSCC) on January 22, 2023. The co-occurrence, cooperation network, and co-citation of countries, institutions, references, authors, journals, and keywords were analyzed using CiteSpace, VOSviewer, and R-bibliometrix software.In total, 4,720 original articles and reviews were included. The number of publications related to DECT has rapidly increased since 2006. The USA (n=1,662) and Mayo Clinic (n=178) were found to be the most productive country and institution, respectively. The most cited article was published by Johnson TRC et al., while the article published by McCollough CH et al. in 2015 had the most co-citations. Schoepf UJ ranked first with most articles among 16,838 authors. The journal with the most published articles was European Radiology, with 411 publications. The timeline analysis indicated that material decomposition was the most recent topic, followed by gout, radiomics, proton therapy, and bone marrow edema.An increasing number of researchers are committed to researching DECT, with the USA making the most significant contributions in this area. Prior studies have primarily concentrated on cardiovascular diseases, and contemporary hot spots include expansion into to other fields, such as iodine quantification, deep learning, and bone marrow edema.
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