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MRI 引导放疗中的实时运动管理:现状与人工智能展望。

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects.

机构信息

Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.

Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium.

出版信息

Radiother Oncol. 2024 Jan;190:109970. doi: 10.1016/j.radonc.2023.109970. Epub 2023 Oct 26.

Abstract

MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.

摘要

MRI 引导的放射治疗(MRIgRT)是一种高度复杂的治疗方式,允许根据每天治疗(分次间)发生的解剖变化进行调整,也允许根据治疗期间(分次内)发生的运动进行调整。在本透视论文中,我们描述了 MRIgRT 期间分次内运动管理的不同步骤,从成像到射束适应,以及临床和研究水平上目前可用的解决方案。此外,考虑到文献中的最新发展,预计将采用一种工作流程,通过 3D 方式补偿运动引起的超量和/或欠量,对放射治疗时间的影响最小。考虑到实时适应的时间限制,特别关注人工智能(AI)解决方案,将其作为传统算法的快速准确替代方案。

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