人工智能在计算机断层扫描剂量优化中的应用。

Annals of the ICRP Pub Date : 2020-12-01 Epub Date: 2020-09-01 DOI:10.1177/0146645320940827
C H McCollough, S Leng
{"title":"人工智能在计算机断层扫描剂量优化中的应用。","authors":"C H McCollough,&nbsp;S Leng","doi":"10.1177/0146645320940827","DOIUrl":null,"url":null,"abstract":"<p><p>The field of artificial intelligence (AI) is transforming almost every aspect of modern society, including medical imaging. In computed tomography (CT), AI holds the promise of enabling further reductions in patient radiation dose through automation and optimisation of data acquisition processes, including patient positioning and acquisition parameter settings. Subsequent to data collection, optimisation of image reconstruction parameters, advanced reconstruction algorithms, and image denoising methods improve several aspects of image quality, especially in reducing image noise and enabling the use of lower radiation doses for data acquisition. Finally, AI-based methods to automatically segment organs or detect and characterise pathology have been translated out of the research environment and into clinical practice to bring automation, increased sensitivity, and new clinical applications to patient care, ultimately increasing the benefit to the patient from medically justified CT examinations. In summary, since the introduction of CT, a large number of technical advances have enabled increased clinical benefit and decreased patient risk, not only by reducing radiation dose, but also by reducing the likelihood of errors in the performance and interpretation of medically justified CT examinations.</p>","PeriodicalId":39551,"journal":{"name":"Annals of the ICRP","volume":"49 1_suppl","pages":"113-125"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0146645320940827","citationCount":"30","resultStr":"{\"title\":\"Use of artificial intelligence in computed tomography dose optimisation.\",\"authors\":\"C H McCollough,&nbsp;S Leng\",\"doi\":\"10.1177/0146645320940827\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The field of artificial intelligence (AI) is transforming almost every aspect of modern society, including medical imaging. In computed tomography (CT), AI holds the promise of enabling further reductions in patient radiation dose through automation and optimisation of data acquisition processes, including patient positioning and acquisition parameter settings. Subsequent to data collection, optimisation of image reconstruction parameters, advanced reconstruction algorithms, and image denoising methods improve several aspects of image quality, especially in reducing image noise and enabling the use of lower radiation doses for data acquisition. Finally, AI-based methods to automatically segment organs or detect and characterise pathology have been translated out of the research environment and into clinical practice to bring automation, increased sensitivity, and new clinical applications to patient care, ultimately increasing the benefit to the patient from medically justified CT examinations. In summary, since the introduction of CT, a large number of technical advances have enabled increased clinical benefit and decreased patient risk, not only by reducing radiation dose, but also by reducing the likelihood of errors in the performance and interpretation of medically justified CT examinations.</p>\",\"PeriodicalId\":39551,\"journal\":{\"name\":\"Annals of the ICRP\",\"volume\":\"49 1_suppl\",\"pages\":\"113-125\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1177/0146645320940827\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of the ICRP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/0146645320940827\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/9/1 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of the ICRP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/0146645320940827","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/9/1 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30

摘要

人工智能(AI)领域正在改变现代社会的几乎每一个方面,包括医学成像。在计算机断层扫描(CT)中,人工智能有望通过自动化和优化数据采集过程,包括患者定位和采集参数设置,进一步降低患者的辐射剂量。在数据采集之后,图像重建参数的优化、先进的重建算法和图像去噪方法提高了图像质量的几个方面,特别是在降低图像噪声和使用较低的辐射剂量进行数据采集方面。最后,基于人工智能的自动分割器官或检测和表征病理的方法已经从研究环境中转移到临床实践中,为患者护理带来自动化、更高的灵敏度和新的临床应用,最终增加了患者从医学上合理的CT检查中获益。总之,自从引入CT以来,大量的技术进步不仅通过减少辐射剂量,而且通过减少在医学上合理的CT检查的表现和解释中出现错误的可能性,从而增加了临床效益并降低了患者风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Use of artificial intelligence in computed tomography dose optimisation.

The field of artificial intelligence (AI) is transforming almost every aspect of modern society, including medical imaging. In computed tomography (CT), AI holds the promise of enabling further reductions in patient radiation dose through automation and optimisation of data acquisition processes, including patient positioning and acquisition parameter settings. Subsequent to data collection, optimisation of image reconstruction parameters, advanced reconstruction algorithms, and image denoising methods improve several aspects of image quality, especially in reducing image noise and enabling the use of lower radiation doses for data acquisition. Finally, AI-based methods to automatically segment organs or detect and characterise pathology have been translated out of the research environment and into clinical practice to bring automation, increased sensitivity, and new clinical applications to patient care, ultimately increasing the benefit to the patient from medically justified CT examinations. In summary, since the introduction of CT, a large number of technical advances have enabled increased clinical benefit and decreased patient risk, not only by reducing radiation dose, but also by reducing the likelihood of errors in the performance and interpretation of medically justified CT examinations.

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
Annals of the ICRP
Annals of the ICRP Medicine-Public Health, Environmental and Occupational Health
CiteScore
4.10
自引率
0.00%
发文量
3
期刊介绍: The International Commission on Radiological Protection was founded in 1928 to advance for the public benefit the science of radiological protection. The ICRP provides recommendations and guidance on protection against the risks associated with ionising radiation, from artificial sources as widely used in medicine, general industry and nuclear enterprises, and from naturally occurring sources. These reports and recommendations are published six times each year on behalf of the ICRP as the journal Annals of the ICRP. Each issue provides in-depth coverage of a specific subject area.
期刊最新文献
PAEDIATRIC PHANTOMS FOR THE NEXT RECOMMENDATIONS. A KEY ELEMENT OF INTERNAL DOSIMETRY FOR MEMBERS OF THE PUBLIC. Specific Absorbed Fractions for Reference Paediatric Individuals. Variation in hepatic segmental portal venous pulsed wave Doppler flow distribution in patients with NAFLD: A pilot study. Optimisation of Protection in Medical Imaging: Necessary, Challenging, and Possible.
×
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