ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.
J Med Imaging Radiat Oncol. 2021 Aug;65(5):596-611. doi: 10.1111/1754-9485.13285. Epub 2021 Jul 19.
During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real-time image-guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)-based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker-based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker-based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.
在放射治疗过程中，由于身体的动态特性，器官和肿瘤会移动；这被称为分次内运动。分次内运动可导致肿瘤剂量不足和健康组织剂量过大，从而降低治疗效果，同时增加患者的毒性。放射肿瘤学界越来越认识到分次内目标运动管理的重要性。实时图像引导放射治疗（IGRT）可以跟踪目标并考虑运动，从而提高肿瘤的放射剂量，降低健康组织的剂量。最近，基于人工智能（AI）的方法已应用于运动管理，并显示出巨大的潜力。在这篇综述中，总结了使用 AI 进行运动管理的四个主要类别：基于标记的跟踪、无标记跟踪、全解剖监测和运动预测。基于标记和无标记的跟踪方法侧重于在整个治疗过程中跟踪单个目标。全解剖算法监测视野内全解剖结构的分次内变化。运动预测算法可用于补偿由于系统定位、处理和动作所需的时间而导致的延迟。
