Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts.
Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts.
Acad Radiol. 2022 Nov;29(11):1709-1719. doi: 10.1016/j.acra.2021.10.024. Epub 2021 Nov 24.
Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction techniques and image processing-based software. The latest technologies use artificial intelligence (AI) for CT dose optimization and image quality improvement. While CT dose optimization has and can benefit from AI, variations in scanner technologies, reconstruction methods, and scan protocols can lead to substantial variations in radiation doses and image quality across and within different scanners. These variations in turn can influence performance of AI algorithms being deployed for tasks such as detection, segmentation, characterization, and quantification. We review the complex relationship between AI and CT radiation dose.
对 CT 辐射剂量优化和降低的关注导致了扫描仪效率的提高，并引入了几种重建技术和基于图像处理的软件。最新的技术使用人工智能 (AI) 进行 CT 剂量优化和图像质量改善。虽然 CT 剂量优化已经并可以受益于人工智能，但扫描仪技术、重建方法和扫描协议的差异会导致不同扫描仪之间以及同一扫描仪内的辐射剂量和图像质量出现很大差异。这些差异反过来又会影响人工智能算法在检测、分割、特征提取和定量等任务中的性能。我们回顾了 AI 和 CT 辐射剂量之间的复杂关系。
