Effective Incorporation of Spatial Information in a Mutual Information Based 3D-2D Registration of a CT Volume to X-Ray Images
Effective Incorporation of Spatial Information in a Mutual Information Based 3D-2D Registration of a CT Volume to X-Ray Images
复制标题
基于互信息的 CT 体积到 X 射线图像的 3D-2D 配准中空间信息的有效结合
DOI：
10.1007/978-3-540-85990-1_111
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Guoyan Zheng
中科院分区：
文献类型：
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作者：
Guoyan Zheng
本文讨论的问题，估计的三维刚性姿态的CT体积的一个对象，从其二维X射线投影。我们使用最大化的互信息，多模态和单模态图像配准任务的准确的相似性度量。然而，它是已知的，标准的互信息测度只考虑强度值，而不考虑空间信息，其鲁棒性是值得怀疑的。在本文中，而不是直接最大化互信息，我们建议使用来自Kullback-Leibler界的变分近似。空间信息，然后纳入到这个变分近似使用马尔可夫随机场模型。新衍生的相似性度量具有最小二乘形式，可以有效地最小化的多分辨率Levenberg-Marquardt优化。实验结果上的X射线和CT数据集的塑料体模和尸体脊柱段。
This paper addresses the problem of estimating the 3D rigid pose of a CT volume of an object from its 2D X-ray projections. We use maximization of mutual information, an accurate similarity measure for multi-modal and mono-modal image registration tasks. However, it is known that the standard mutual information measure only takes intensity values into account without considering spatial information and its robustness is questionable. In this paper, instead of directly maximizing mutual information, we propose to use a variational approximation derived from the Kullback-Leibler bound. Spatial information is then incorporated into this variational approximation using a Markov random field model. The newly derived similarity measure has a least-squares form and can be effectively minimized by a multi-resolution Levenberg-Marquardt optimizer. Experimental results are presented on X-ray and CT datasets of a plastic phantom and a cadaveric spine segment.