Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, FCT 14.6048, Houston, TX, 77030, USA.
Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA.
Med Phys. 2017 Jul;44(7):e43-e76. doi: 10.1002/mp.12256. Epub 2017 May 23.
Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.
图像配准和融合算法几乎存在于每个创建或使用放射治疗图像的软件系统中。大多数治疗计划系统都支持某种形式的图像配准和融合，以允许使用多模态和时间序列图像数据，甚至解剖图谱来辅助靶区和正常组织勾画。治疗实施系统在计划图像和治疗过程中采集的室内图像之间执行配准和融合，以辅助患者定位。先进的应用程序开始支持每日剂量评估，并使用图像配准和融合来传播轮廓和累积剂量，从而在治疗过程中获取的图像数据之间进行，以提供解剖变化和已交付剂量的最新估计。这些信息有助于检测可能导致治疗计划或处方更改的解剖和功能变化。由于图像配准过程的输出始终用作规划或交付的另一个过程的输入，因此了解和传达软件的不确定性以及特定配准的结果非常重要。不幸的是，对于可能出现噪声、失真和复杂解剖变化的实际情况，没有标准的数学形式来执行此操作。由于商业系统缺乏可用文档，这些系统的验证也变得复杂，导致这些系统以不理想的“黑盒”方式使用。鉴于这种情况以及图像配准和融合在治疗计划和实施中的核心作用，美国医学物理学家协会治疗物理委员会委托第 132 任务组审查放射治疗中图像配准（刚性和变形）的当前方法和解决方案，并为这些临床过程的质量保证和质量控制提供建议。
