Nonrigid coregistration of diffusion tensor images using a viscous fluid model and mutual information
Nonrigid coregistration of diffusion tensor images using a viscous fluid model and mutual information
复制标题
DOI：
10.1109/tmi.2007.906786
复制
发表时间：
2007-11-01
影响因子：
10.6
通讯作者：
Sijbers, Jan
中科院分区：
文献类型：
--
作者：
Van Hecke, Wim;Leemans, Alexander;Sijbers, Jan
本文提出了一种基于粘性流体模型的非刚性共配准算法，该算法针对扩散张量图像（DTI）进行了优化，其中通过互信息准则来测量图像对应性。介绍了几种共同配准策略，并在模拟数据和大脑间 DTI 数据上进行了评估。两种张量重定向方法已被纳入并进行了定量评估。仿真和实验结果表明，尽管观察到张量重定向对局部变形场高度敏感，但所提出的粘性流体模型可以提供较高的配准精度。然而，与仿射图像匹配相比，这种配准方法已被证明可以显着改善空间对准。
In this paper, a nonrigid coregistration algorithm based on a viscous fluid model is proposed that has been optimized for diffusion tensor images (DTI), in which image correspondence is measured by the mutual information criterion. Several coregistration strategies are introduced and evaluated both on simulated data and on brain intersubject DTI data. Two tensor reorientation methods have been incorporated and quantitatively evaluated. Simulation as well as experimental results show that the proposed viscous fluid model can provide a high coregistration accuracy, although the tensor reorientation was observed to be highly sensitive to the local deformation field. Nevertheless, this coregistration method has demonstrated to significantly improve spatial alignment compared to affine image matching.