Commowick O, Arsigny V, Isambert A, Costa J, Dhermain F, Bidault F, Bondiau P-Y, Ayache N, Malandain G
INRIA Sophia Antipolis, ASCLEPIOS Team, 2004 route des lucioles, BP93, 06902 Sophia Antipolis Cedex, France; DOSIsoft S.A., 45 avenue Carnot, 94230 Cachan, France.
INRIA Sophia Antipolis, ASCLEPIOS Team, 2004 route des lucioles, BP93, 06902 Sophia Antipolis Cedex, France.
Med Image Anal. 2008 Aug;12(4):427-441. doi: 10.1016/j.media.2008.01.002. Epub 2008 Jan 31.
Intra-subject and inter-subject nonlinear registration based on dense transformations requires the setting of many parameters, mainly for regularization. This task is a major issue, as the global quality of the registration will depend on it. Setting these parameters is, however, very hard, and they may have to be tuned for each patient when processing data acquired by different centers or using different protocols. Thus, we present in this article a method to introduce more coherence in the registration by using fewer degrees of freedom than with a dense registration. This is done by registering the images only on user-defined areas, using a set of affine transformations, which are optimized together in a very efficient manner. Our framework also ensures a smooth and coherent transformation thanks to a new regularization of the affine components. Finally, we ensure an invertible transformation thanks to the Log-Euclidean polyaffine framework. This allows us to get a more robust and very efficient registration method, while obtaining good results as explained below. We performed a qualitative and quantitative evaluation of the obtained results on two applications: first on atlas-based brain segmentation, comparing our results with a dense registration algorithm. Then the second application for which our framework is particularly well suited concerns bone registration in the lower-abdomen area. We obtain in this case a better positioning of the femoral heads than with a dense registration. For both applications, we show a significant improvement in computation time, which is crucial for clinical applications.
基于密集变换的受试者内和受试者间非线性配准需要设置许多参数，主要用于正则化。这项任务是一个主要问题，因为配准的整体质量将取决于它。然而，设置这些参数非常困难，并且在处理由不同中心获取的数据或使用不同协议时，可能需要为每个患者进行调整。因此，我们在本文中提出了一种方法，通过使用比密集配准更少的自由度来在配准中引入更多的一致性。这是通过仅在用户定义的区域上使用一组仿射变换来配准图像来实现的，这些仿射变换以非常有效的方式一起进行优化。由于对仿射分量进行了新的正则化，我们的框架还确保了平滑且连贯的变换。最后，由于对数欧几里得多仿射框架，我们确保了可逆变换。这使我们能够获得一种更强大且非常有效的配准方法，同时如下所述获得良好的结果。我们在两个应用中对所得结果进行了定性和定量评估：首先是基于图谱的脑部分割，将我们的结果与密集配准算法进行比较。然后是我们的框架特别适合的第二个应用，即下腹部区域的骨配准。在这种情况下，我们获得了比密集配准更好的股骨头定位。对于这两个应用，我们都显示出计算时间有显著改善，这对于临床应用至关重要。
