Strack Christian, Seifert Robert, Kleesiek Jens
AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland.
Heidelberg University, Heidelberg, Deutschland.
Radiologe. 2020 May;60(5):405-412. doi: 10.1007/s00117-020-00646-w.
Hybrid imaging enables the precise visualization of cellular metabolism by combining anatomical and metabolic information. Advances in artificial intelligence (AI) offer new methods for processing and evaluating this data.
This review summarizes current developments and applications of AI methods in hybrid imaging. Applications in image processing as well as methods for disease-related evaluation are presented and discussed.
This article is based on a selective literature search with the search engines PubMed and arXiv.
Currently, there are only a few AI applications using hybrid imaging data and no applications are established in clinical routine yet. Although the first promising approaches are emerging, they still need to be evaluated prospectively. In the future, AI applications will support radiologists and nuclear medicine radiologists in diagnosis and therapy.
混合成像通过结合解剖学和代谢信息，能够精确地可视化细胞代谢。人工智能（AI）的进展为处理和评估这些数据提供了新方法。
本综述总结了AI方法在混合成像中的当前发展和应用。介绍并讨论了其在图像处理中的应用以及与疾病相关的评估方法。
本文基于使用搜索引擎PubMed和arXiv进行的选择性文献检索。
目前，使用混合成像数据的AI应用很少，且尚无应用于临床常规的情况。尽管出现了一些有前景的初步方法，但仍需进行前瞻性评估。未来，AI应用将在诊断和治疗方面为放射科医生和核医学放射科医生提供支持。
