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基于人工智能的合成计算断层摄影在仅磁共振放疗中的开发和临床应用面临的挑战和机遇。

Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy.

机构信息

Department of Oncology-Pathology, Karolinska Institute, Solna, Sweden; Radiotherapy Physics and Engineering, Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Solna, Sweden.

Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.

出版信息

Radiother Oncol. 2024 Sep;198:110387. doi: 10.1016/j.radonc.2024.110387. Epub 2024 Jun 15.

Abstract

Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.

摘要

基于磁共振成像(MRI)生成的合成计算机断层扫描(sCT)可作为放射治疗(RT)中计划 CT 的替代,从而消除了与多模态成像配对相关的配准不确定性,降低了成本并减少了患者的辐射暴露。目前,CE/FDA 批准的 sCT 解决方案可用于骨盆、大脑和头颈部,而更复杂的深度学习(DL)算法正在研究其他解剖部位。实现 sCT 广泛临床应用的主要挑战在于缺乏 sCT 委托和质量保证(QA)的共识,导致不同医院的 sCT 方法存在差异。为了解决这个问题,一组专家在 2022 年 ESTRO 物理研讨会上聚集一堂,讨论将 sCT 解决方案整合到临床中的问题,并报告该过程及其结果。本立场文件重点介绍了 sCT 开发和委托的各个方面,概述了安全实施仅基于 MRI 的 RT 工作流程的关键要素。

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