Patient setup error measurement using 3D intensity-based image registration techniques
Patient setup error measurement using 3D intensity-based image registration techniques
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DOI：
10.1016/s0360-3016(03)00083-x
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发表时间：
2003-05-01
影响因子：
7
通讯作者：
Carrie, C
中科院分区：
文献类型：
--
作者：
Clippe, S;Sarrut, D;Carrie, C
目的：适形放射治疗需要参考初始三维 (3D) CT 图像进行准确的患者定位。通过与患者治疗前立即获取的射野图像进行比较来控制患者摆位。已经提出了几种通常基于分割程序的自动方法。然而，门户图像的对比度非常低，导致分割不准确。 In this study, we propose an intensity-based (with no segmentation), fully automatic, 3D method, associating two portal images and a 3D CT scan to estimate patient setup.Methods and Materials: Images of an anthropomorphic phantom were used.首先对骨盆区域进行 CT 扫描，然后将模型安装在七个位置。该过程是刚性变换空间中相似性度量的 3D 优化。为了避免在每次迭代时生成耗时的数字重建射线照片，我们使用了二维变换和两组特定的和预先生成的数字重建射线照片。我们还提出了一种计算几对图像之间基于强度的相似性度量的技术。使用相关系数、卡方、互信息和相关比。结果：使用相关比获得了最佳结果。七个测试位置的均方根误差中位数为 2.0 mm，相关系数、卡方和互信息分别为 3.6、4.4 和 5.1。 结论：无需任何分割步骤即可对设置误差进行完整 3D 分析。它快速且准确，因此可以在每次治疗之前使用。 The method presents three main advantages for clinical implementation-it is fully automatic, applicable to all tumor sites, and requires no additional device. (C) 2003 爱思唯尔公司。
Purpose: Conformal radiotherapy requires accurate patient positioning with reference to the initial three-dimensional (3D) CT image. Patient setup is controlled by comparison with portal images acquired immediately before patient treatment. Several automatic methods have been proposed, generally based on segmentation procedures. However, portal images are of very low contrast, leading to segmentation inaccuracies. In this study, we propose an intensity-based (with no segmentation), fully automatic, 3D method, associating two portal images and a 3D CT scan to estimate patient setup.Methods and Materials: Images of an anthropomorphic phantom were used. A CT scan of the pelvic area was first acquired, then the phantom was installed in seven positions. The process is a 3D optimization of a similarity measure in the space of rigid transformations. To avoid time-consuming digitally reconstructed radiograph generation at each iteration, we used two-dimensional transformations and two sets of specific and pregenerated digitally reconstructed radiographs. We also propose a technique for computing intensity-based similarity measures between several couples of images. A correlation coefficient, chi-square, mutual information, and correlation ratio were used.Results: The best results were obtained with the correlation ratio. The median root mean square error was 2.0 mm for the seven positions tested and was, respectively, 3.6, 4.4, and 5.1 for correlation coefficient, chi-square, and mutual information.Conclusion: Full 3D analysis of setup errors is feasible without any segmentation step. It is fast and accurate and could therefore be used before each treatment session. The method presents three main advantages for clinical implementation-it is fully automatic, applicable to all tumor sites, and requires no additional device. (C) 2003 Elsevier Inc.