超分辨率深度学习重建增强了神经血管冲突成像中的颅神经描绘和观察者间的一致性。

IF 3.8 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Academic Radiology Pub Date : 2024-11-14 DOI:10.1016/j.acra.2024.10.054
Yajie Li, Shiman Wu
{"title":"超分辨率深度学习重建增强了神经血管冲突成像中的颅神经描绘和观察者间的一致性。","authors":"Yajie Li, Shiman Wu","doi":"10.1016/j.acra.2024.10.054","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Super-resolution Deep Learning Reconstruction Enhances Cranial Nerve Depiction and Interobserver Agreement in Neurovascular Conflict Imaging.\",\"authors\":\"Yajie Li, Shiman Wu\",\"doi\":\"10.1016/j.acra.2024.10.054\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":50928,\"journal\":{\"name\":\"Academic Radiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.acra.2024.10.054\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.acra.2024.10.054","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Super-resolution Deep Learning Reconstruction Enhances Cranial Nerve Depiction and Interobserver Agreement in Neurovascular Conflict Imaging.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
期刊最新文献
Comparative Evaluation of Clinical-MRI, Radiomics, and Integrated Nomogram Models for Preoperative Prediction of Placenta Accreta Spectrum. Pulmonary 129Xe MRI: CNN Registration and Segmentation to Generate Ventilation Defect Percent with Multi-center Validation. Prognostic value of extracellular volume fraction in myocardial infarction and myocardial infarction with nonobstructive coronary arteries: A multicenter study. Accelerated spine MRI with deep learning based image reconstruction: a prospective comparison with standard MRI. Beyond the Procedure: The Hidden Crisis of Moral Injury in Interventional Radiology.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1