Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Department of Nuclear Medicine, LMU University Hospital, Munich, Germany.
Nuklearmedizin. 2023 Dec;62(6):379-388. doi: 10.1055/a-2179-6872. Epub 2023 Oct 12.
Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.
常规临床剂量学以及放射性药物治疗是未来治疗个体化的关键。然而，剂量学被认为是复杂和耗时的，在剂量学工作流程中的各个步骤中都存在各种挑战。基于图像的剂量学的一般工作流程包括定量成像、器官和肿瘤的分割、时间-活性曲线的拟合以及吸收剂量的转换。本工作综述了人工智能在提高剂量学工作流程中每一步的速度和准确性方面的潜力和优势。
