Garajová Laura, Garbe Stephan, Sprinkart Alois M
Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
Klinik für Strahlentherapie und Radioonkologie, Universitätsklinikum Bonn, Bonn, Deutschland.
Radiologie (Heidelb). 2024 Oct;64(10):787-792. doi: 10.1007/s00117-024-01330-z. Epub 2024 Jun 14.
CLINICAL-METHODOLOGICAL PROBLEM: Imaging procedures employing ionizing radiation require compliance with European directives and national regulations in order to protect patients. Each exposure must be indicated, individually adapted, and documented. Unacceptable dose exceedances must be detected and reported. These tasks are time-consuming and require meticulous diligence.
Computed tomography (CT) is the most important contributor to medical radiation exposure. Optimizing the patient's dose is therefore mandatory. Use of modern technology and reconstruction algorithms already reduces exposure. Checking the indication, planning, and performing the examination are further important process steps with regard to radiation protection. Patient exposure is usually monitored by dose management systems (DMS). In special cases, a risk assessment is required by calculating the organ doses.
Artificial intelligence (AI)-assisted techniques are increasingly used in various steps of the process: they support examination planning, improve patient positioning, and enable automated scan length adjustments. They also provide real-time estimates of individual organ doses.
The integration of AI into medical imaging is proving successful in terms of dose optimization in various areas of the radiological workflow, from reconstruction to examination planning and performing exams. However, the use of AI in conjunction with DMS has not yet been considered on a large scale.
AI processes offer promising tools to support dose management. However, their implementation in the clinical setting requires further research, extensive validation, and continuous monitoring.
采用电离辐射的成像程序需要符合欧洲指令和国家法规，以保护患者。每次照射都必须有指征、个体化调整并记录在案。必须检测并报告不可接受的剂量超标情况。这些任务耗时且需要一丝不苟的严谨态度。
计算机断层扫描（CT）是医疗辐射暴露的最重要来源。因此，优化患者剂量是必不可少的。使用现代技术和重建算法已经可以减少辐射暴露。检查指征、规划和进行检查是辐射防护方面的进一步重要流程步骤。患者的辐射暴露通常由剂量管理系统（DMS）进行监测。在特殊情况下，需要通过计算器官剂量进行风险评估。
人工智能（AI）辅助技术在该过程的各个步骤中越来越多地被使用：它们支持检查规划、改善患者体位摆放，并能实现扫描长度的自动调整。它们还能提供各个器官剂量的实时估计。
事实证明，将AI整合到医学成像中在放射学工作流程的各个领域，从重建到检查规划和进行检查，在剂量优化方面都取得了成功。然而，AI与DMS结合使用尚未得到大规模考虑。
AI程序为支持剂量管理提供了有前景的工具。然而，它们在临床环境中的实施需要进一步研究、广泛验证和持续监测。
