Diffeomorphic demons: Efficient non-parametric image registration
Diffeomorphic demons: Efficient non-parametric image registration
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DOI：
10.1016/j.neuroimage.2008.10.040
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发表时间：
2009-03-01
期刊：
影响因子：
5.7
通讯作者：
Ayache, Nicholas
中科院分区：
文献类型：
--
作者：
Vercauteren, Tom;Pennec, Xavier;Ayache, Nicholas
提出了一种基于Thirion的Demons算法的非参数同构图像配准算法。在本文的第一部分中，我们表明，Thirion的恶魔算法可以被看作是一个优化过程的整个空间的位移场。我们提供了强大的理论根源的不同变体的Thirion的恶魔算法。这一分析预测了对称力变形的恶魔算法的理论优势。我们在对照实验中表明，这种优势在实践中得到了证实，并产生了更快的收敛。在本文的第二部分中，我们适应的恶魔算法的基础上的优化过程的空间的同构变换。与许多单形配准算法相比，我们的解决方案是计算效率高，因为在实践中，它只取代了几个组成的位移场的添加。我们的实验表明，除了是同构的，我们的算法提供的结果是类似的恶魔算法，但与转换更平滑，更接近黄金标准，可在受控实验中，在雅可比方面。(C)2008年爱思唯尔公司All rights reserved.
We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. In the first part of this paper, we show that Thirion's demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians. (C) 2008 Elsevier Inc. All rights reserved.