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人工智能驱动的螺旋式和机器人放射治疗中的自适应运动管理:创新、挑战与未来方向

Artificial Intelligence (AI)-Driven Adaptive Motion Management in Helical and Robotic Radiotherapy: Innovations, Challenges, and Future Directions.

作者信息

Panda Dhiren K, Das Saurjya R, Kumar Shishir

机构信息

Anatomy, Institute of Medical Sciences (IMS) & SUM Hospital, Siksha 'O' Anusandhan University, Bhubaneswar, IND.

出版信息

Cureus. 2025 Apr 4;17(4):e81702. doi: 10.7759/cureus.81702. eCollection 2025 Apr.

Abstract

Artificial intelligence (AI) has revolutionized motion-adaptive radiotherapy (ART) by enhancing tumor-tracking accuracy and optimizing radiation dosage delivery. Traditional motion management techniques, such as respiratory gating and internal target volume (ITV) expansion, often result in increased treatment margins and unintended radiation exposure. AI-powered real-time motion tracking, deformable image registration (DIR), and ART offer superior tumor localization, automated dose modulation, and real-time imaging integration. This study examined AI-based motion guidance technologies in helical tomotherapy (HT) and CyberKnife (Accuray, Madison, WI) robotic radiosurgery, highlighting technical innovations, engineering challenges, and clinical applications. HT employs megavoltage computed tomography (MVCT) for intra-fractional motion monitoring, whereas CyberKnife utilizes x-ray-based beam correction via a robotic arm to achieve submillimeter precision. Despite advancements, challenges such as AI processing latency, tumor motion variability, and multimodal imaging integration persist. Future research should focus on improving the AI response times, enhancing motion prediction algorithms, and developing fully automated AI-based radiation delivery systems.

摘要

人工智能(AI)通过提高肿瘤追踪精度和优化放射剂量输送,彻底改变了运动自适应放疗(ART)。传统的运动管理技术,如呼吸门控和内部靶区体积(ITV)扩展,往往会导致治疗边界增加和意外的辐射暴露。基于人工智能的实时运动追踪、可变形图像配准(DIR)和ART提供了卓越的肿瘤定位、自动剂量调制和实时成像整合。本研究考察了螺旋断层放疗(HT)和赛博刀(Accuray公司,威斯康星州麦迪逊)机器人放射外科中基于人工智能的运动引导技术,重点介绍了技术创新、工程挑战和临床应用。HT采用兆伏级计算机断层扫描(MVCT)进行分次内运动监测,而赛博刀则通过机器人手臂利用基于X射线的束流校正来实现亚毫米精度。尽管取得了进展,但诸如人工智能处理延迟、肿瘤运动变异性和多模态成像整合等挑战依然存在。未来的研究应专注于缩短人工智能响应时间、改进运动预测算法以及开发基于人工智能的全自动放射输送系统。

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