Deformable image registration in radiation therapy.
Deformable image registration in radiation therapy.
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DOI：
10.3857/roj.2017.00325
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发表时间：
2017-06
影响因子：
2.3
通讯作者：
Kim S
中科院分区：
文献类型：
--
作者：
Oh S;Kim S
在放射肿瘤学领域引入各种先进技术后，放射治疗期间成像数据集的数量显著增加。因此，已经有许多研究提出了成像数据集使用的有意义的应用。这些应用通常需要一种方法来将数据集与参考对齐。可变形图像配准（Deformable Image Registration，简称DEM）是通过将图像数据集局部配准到参考图像集中来满足这一要求的过程。空间映射识别空间对应性，以便最小化两个或多个图像集合之间的差异。本文介绍了临床应用，验证和算法的并行技术。在放射治疗中的应用包括剂量累积、数学建模、自动分割和功能成像。讨论的确认方法基于解剖标志、物理体模、数字体模和每个应用目的。并行算法也简要回顾了两个算法组件：相似性指数和变形模型。
The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.