Department of Radiology, Alfred Health, Melbourne, Australia.
Department of Neuroscience, Monash University, Melbourne, Australia.
Br J Radiol. 2021 Oct 1;94(1126):20210406. doi: 10.1259/bjr.20210406. Epub 2021 Jun 23.
Artificial intelligence, including deep learning, is currently revolutionising the field of medical imaging, with far reaching implications for almost every facet of diagnostic imaging, including patient radiation safety. This paper introduces basic concepts in deep learning and provides an overview of its recent history and its application in tomographic reconstruction as well as other applications in medical imaging to reduce patient radiation dose, as well as a brief description of previous tomographic reconstruction techniques. This review also describes the commonly used deep learning techniques as applied to tomographic reconstruction and draws parallels to current reconstruction techniques. Finally, this paper reviews some of the estimated dose reductions in CT and positron emission tomography in the recent literature enabled by deep learning, as well as some of the potential problems that may be encountered such as the obscuration of pathology, and highlights the need for additional clinical reader studies from the imaging community.
人工智能，包括深度学习，正在彻底改变医学成像领域，对诊断成像的几乎所有方面都有着深远的影响，包括患者的辐射安全。本文介绍了深度学习的基本概念，并概述了其近期的历史及其在层析重建以及医学成像的其他应用中的应用，以降低患者的辐射剂量，以及简要描述了以前的层析重建技术。本文还介绍了应用于层析重建的常用深度学习技术，并与当前的重建技术进行了比较。最后，本文回顾了最近文献中深度学习在 CT 和正电子发射断层扫描中估计的剂量降低，并讨论了可能出现的一些潜在问题，如掩盖病理学，并强调了成像界需要进行更多的临床读者研究。
