Bender Edward T, Tomé Wolfgang A
Department of Medical Physics, The University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI 53705-2275, USA.
Phys Med Biol. 2009 Sep 21;54(18):5561-77. doi: 10.1088/0031-9155/54/18/014. Epub 2009 Aug 28.
The aim of this study was to investigate the utility of consistency metrics, such as inverse consistency, in contour-based deformable registration error analysis. Four images were acquired of the same phantom that has experienced varying levels of deformation. The deformations were simulated with deformable image registration. Using calculated deformation maps, the inconsistencies within the algorithm were investigated. This can be done, for example, by calculating deformation maps both in forward and reverse directions and applying them subsequently to an image. If the algorithm is not inverse consistent, then this final image will not be the same as the original, as it should be. Other consistency tests were done, for example by comparing different algorithms or by applying the deformation maps to a circular set of multiple deformations, whereby the original and final images are in fact the same. The resulting composite deformation map in this case contains a combination of the errors within those maps, because if error free, the resulting deformation map should be zero everywhere. We have termed this the generalized inverse consistency error map (Sigma(Chi)). The correlation between the consistency metrics and registration error varied considerably depending on the registration algorithm and type of consistency metric. There was also a trend for the actual registration error to be larger than the consistency metrics. A disadvantage of these techniques is that good performance in these consistency checks is a necessary but not sufficient condition for an accurate deformation method.
本研究的目的是探讨一致性度量（如逆一致性）在基于轮廓的可变形配准误差分析中的效用。对同一经历了不同程度变形的体模采集了四张图像。变形通过可变形图像配准进行模拟。利用计算得到的变形图，研究了算法内部的不一致性。例如，可以通过正向和反向计算变形图并随后将其应用于一幅图像来实现。如果算法不是逆一致的，那么最终图像将与原始图像不同，而它应该是相同的。还进行了其他一致性测试，例如通过比较不同算法或通过将变形图应用于一组多个圆形变形，此时原始图像和最终图像实际上是相同的。在这种情况下得到的复合变形图包含了这些图中误差的组合，因为如果没有误差，得到的变形图在各处都应该为零。我们将此称为广义逆一致性误差图（Sigma(Chi)）。一致性度量与配准误差之间的相关性根据配准算法和一致性度量的类型有很大差异。实际配准误差也有大于一致性度量的趋势。这些技术的一个缺点是，在这些一致性检查中表现良好是准确变形方法的必要但不充分条件。
