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Insights Into ImagingSCIE

国际简称：INSIGHTS IMAGING 参考译名：洞察成像

中科院分区
2区
CiteScore分区
Q1
JCR分区
Q1

基本信息：: ISSN：1869-4101; E-ISSN：1869-4101; 是否OA：开放; 是否预警：否; TOP期刊：是

出版信息：: 出版地区：GERMANY; 出版商：Springer Berlin Heidelberg; 出版语言：English; 出版周期：1 issue/year; 出版年份：2010; 研究方向：Medicine-Radiology, Nuclear Medicine and Imaging

评价信息：: 影响因子：4.1; CiteScore指数：7.3; SJR指数：1.24; SNIP指数：2.142

发文数据：: Gold OA文章占比：99.66%; 研究类文章占比：77.40%; 年发文量：208; 自引率：0.0425...; 开源占比：0.996; 出版撤稿占比：0; 出版国人文章占比：0.02; OA被引用占比：1

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英文简介期刊介绍 CiteScore数据中科院SCI分区 JCR分区发文数据常见问题

英文简介Insights Into Imaging期刊介绍

Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere!

I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe.

Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy.

A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field.

I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly.

The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members.

The journal went open access in 2012, which means that all articles published since then are freely available online.

期刊简介Insights Into Imaging期刊介绍

《Insights Into Imaging》自2010出版以来，是一本医学优秀杂志。致力于发表原创科学研究结果，并为医学各个领域的原创研究提供一个展示平台，以促进医学领域的的进步。该刊鼓励先进的、清晰的阐述，从广泛的视角提供当前感兴趣的研究主题的新见解，或审查多年来某个重要领域的所有重要发展。该期刊特色在于及时报道医学领域的最新进展和新发现新突破等。该刊近一年未被列入预警期刊名单，目前已被权威数据库SCIE收录，得到了广泛的认可。

该期刊投稿重要关注点：

预计审稿时间： 13 Weeks
国际TOP期刊
医学
RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
SCIE
中科院2区
非预警

Cite Score数据（2024年最新版）Insights Into Imaging Cite Score数据

CiteScore：7.3
SJR：1.24
SNIP：2.142

学科类别	分区	排名	百分位
大类：Medicine 小类：Radiology, Nuclear Medicine and Imaging	Q1	42 / 333	87%

CiteScore 是由Elsevier(爱思唯尔)推出的另一种评价期刊影响力的文献计量指标。反映出一家期刊近期发表论文的年篇均引用次数。CiteScore以Scopus数据库中收集的引文为基础，针对的是前四年发表的论文的引文。CiteScore的意义在于，它可以为学术界提供一种新的、更全面、更客观地评价期刊影响力的方法，而不仅仅是通过影响因子(IF)这一单一指标来评价。

历年Cite Score趋势图

中科院SCI分区Insights Into Imaging 中科院分区

中科院 2023年12月升级版 综述期刊：否 Top期刊：否

大类学科	分区	小类学科	分区
医学	2区	RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING 核医学	2区

中科院分区表 是以客观数据为基础，运用科学计量学方法对国际、国内学术期刊依据影响力进行等级划分的期刊评价标准。它为我国科研、教育机构的管理人员、科研工作者提供了一份评价国际学术期刊影响力的参考数据，得到了全国各地高校、科研机构的广泛认可。

中科院分区表 将所有期刊按照一定指标划分为1区、2区、3区、4区四个层次，类似于“优、良、及格”等。最开始，这个分区只是为了方便图书管理及图书情报领域的研究和期刊评估。之后中科院分区逐步发展成为了一种评价学术期刊质量的重要工具。

历年中科院分区趋势图

JCR分区Insights Into Imaging JCR分区

2023-2024 年最新版

按JIF指标学科分区	收录子集	分区	排名	百分位
学科：RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING	SCIE	Q1	30 / 204	85.5%

按JCI指标学科分区	收录子集	分区	排名	百分位
学科：RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING	SCIE	Q1	30 / 204	85.54%

JCR分区的优势在于它可以帮助读者对学术文献质量进行评估。不同学科的文章引用量可能存在较大的差异，此时单独依靠影响因子(IF)评价期刊的质量可能是存在一定问题的。因此，JCR将期刊按照学科门类和影响因子分为不同的分区，这样读者可以根据自己的研究领域和需求选择合适的期刊。

历年影响因子趋势图

发文数据

2023-2024 年国家/地区发文量统计

国家/地区数量
Italy65
USA64
England38
Austria34
Spain32
France29
Netherlands28
Switzerland21
GERMANY (FED REP GER)20
Belgium19

本刊中国学者近年发表论文

1、Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors
Author: Jan, Ya-Ting; Tsai, Pei-Shan; Huang, Wen-Hui; Chou, Ling-Ying; Huang, Shih-Chieh; Wang, Jing-Zhe; Lu, Pei-Hsuan; Lin, Dao-Chen; Yen, Chun-Sheng; Teng, Ju-Ping; Mok, Greta S. P.; Shih, Cheng-Ting; Wu, Tung-Hsin

Journal: INSIGHTS INTO IMAGING. 2023; Vol. 14, Issue 1, pp. -. DOI: 10.1186/s13244-023-01412-x
2、Multi-channel deep learning model-based myocardial spatial-temporal morphology feature on cardiac MRI cine images diagnoses the cause of LVH
Author: Diao, Kaiyue; Liang, Hong-qing; Yin, Hong-kun; Yuan, Ming-jing; Gu, Min; Yu, Peng-xin; He, Sen; Sun, Jiayu; Song, Bin; Li, Kang; He, Yong

Journal: INSIGHTS INTO IMAGING. 2023; Vol. 14, Issue 1, pp. -. DOI: 10.1186/s13244-023-01401-0
3、CT and MRI features of hepatic epithelioid haemangioendothelioma: a multi-institutional retrospective analysis of 15 cases and a literature review
Author: Luo, Lianmei; Cai, Zeyu; Zeng, Sihui; Wang, Lizhu; Kang, Zhuang; Yang, Ning; Zhang, Yaqin

Journal: INSIGHTS INTO IMAGING. 2023; Vol. 14, Issue 1, pp. -. DOI: 10.1186/s13244-022-01344-y
4、Predicting histologic differentiation of solitary hepatocellular carcinoma up to 5 cm on gadoxetate disodium-enhanced MRI
Author: Yang, Ting; Wei, Hong; Wu, Yuanan; Qin, Yun; Chen, Jie; Jiang, Hanyu; Song, Bin

Journal: INSIGHTS INTO IMAGING. 2023; Vol. 14, Issue 1, pp. -. DOI: 10.1186/s13244-022-01354-w
5、Predictive models and early postoperative recurrence evaluation for hepatocellular carcinoma based on gadoxetic acid-enhanced MR imaging
Author: Li, Qian; Wei, Yi; Zhang, Tong; Che, Feng; Yao, Shan; Wang, Cong; Shi, Dandan; Tang, Hehan; Song, Bin

Journal: INSIGHTS INTO IMAGING. 2023; Vol. 14, Issue 1, pp. -. DOI: 10.1186/s13244-022-01359-5
6、Impact of glycemic control on biventricular function in patients with type 2 diabetes mellitus: a cardiac magnetic resonance tissue tracking study
Author: Zhu, Jing; Li, Wenjia; Chen, Fang; Xie, Zhen; Zhuo, Kaimin; Huang, Ruijue

Journal: INSIGHTS INTO IMAGING. 2023; Vol. 14, Issue 1, pp. -. DOI: 10.1186/s13244-022-01357-7
7、Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions
Author: He, Quan-Hao; Feng, Jia-Jun; Lv, Fa-Jin; Jiang, Qing; Xiao, Ming-Zhao

Journal: INSIGHTS INTO IMAGING. 2023; Vol. 14, Issue 1, pp. -. DOI: 10.1186/s13244-022-01349-7
8、A feasibility study of reduced full-of-view synthetic high-b-value diffusion-weighted imaging in uterine tumors
Author: Tang, Qian; Zhou, Qiqi; Chen, Wen; Sang, Ling; Xing, Yu; Liu, Chao; Wang, Kejun; Liu, Weiyin Vivian; Xu, Lin

Journal: INSIGHTS INTO IMAGING. 2023; Vol. 14, Issue 1, pp. -. DOI: 10.1186/s13244-022-01350-0