An efficient locally affine framework for the smooth registration of anatomical structures
An efficient locally affine framework for the smooth registration of anatomical structures
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DOI：
10.1016/j.media.2008.01.002
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发表时间：
2008-08-01
影响因子：
10.9
通讯作者：
Malandain, G.
中科院分区：
文献类型：
--
作者：
Commowick, O.;Arsigny, V.;Malandain, G.
基于稠密变换的主体内和主体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，并且当处理由不同中心或使用不同协议采集的数据时，可能必须针对每个患者进行调整。因此，我们在这篇文章中提出了一种方法，通过使用更少的自由度比密集注册，在注册中引入更多的一致性。这是通过使用一组仿射变换仅在用户定义的区域上配准图像来完成的，这些仿射变换以非常有效的方式一起优化。由于仿射分量的新正则化，我们的框架还确保了平滑和连贯的变换。最后，我们确保了可逆的转换感谢对数欧几里德polyaffine框架。这使我们能够得到一个更强大的和非常有效的配准方法，同时获得良好的结果如下所述。我们进行了定性和定量评估所获得的结果在两个应用程序：首先是基于图谱的大脑分割，比较我们的结果与密集的配准算法。然后，我们的框架特别适合的第二个应用涉及下腹部区域的骨骼配准。在这种情况下，我们获得了比密集配准更好的股骨头定位。对于这两种应用程序，我们显示了显着的改善计算时间，这是至关重要的临床应用。(C)2008 Elsevier B.V.保留所有权利。
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. (C) 2008 Elsevier B.V. All rights reserved.