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放疗应用中形变图像配准不确定性的回顾与建议。

Review and recommendations on deformable image registration uncertainties for radiotherapy applications.

机构信息

Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America.

Harvard Medical School, Boston, MA, United States of America.

出版信息

Phys Med Biol. 2023 Dec 13;68(24):24TR01. doi: 10.1088/1361-6560/ad0d8a.

Abstract

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

摘要

可变形图像配准(DIR)是放射治疗(RT)中许多应用中使用的通用工具。DIR 算法已在许多商业治疗计划系统中实现,提供了易于使用的解决方案。然而,DIR 的几何不确定性可能很大且难以量化,这给临床实践带来了障碍。目前,RT 界尚未就如何量化这些不确定性以及确定区分良好 DIR 结果和不良 DIR 结果的阈值达成一致意见。本综述总结了关于 DIR 不确定性来源及其对 RT 应用影响的当前文献。就如何处理这些不确定性以进行特定于患者的使用、委托和研究提供了建议。还为开发人员和供应商提供了建议,以帮助用户了解 DIR 不确定性,并使 DIR 在 RT 中的应用更安全、更可靠。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d75c/10725576/37c7b4cf0896/pmbad0d8af1_lr.jpg

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