A local fast marching-based diffusion tensor image registration algorithm by simultaneously considering spatial deformation and tensor orientation.
A local fast marching-based diffusion tensor image registration algorithm by simultaneously considering spatial deformation and tensor orientation.
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DOI：
10.1016/j.neuroimage.2010.04.004
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发表时间：
2010-08-01
期刊：
影响因子：
5.7
通讯作者：
Wong, Stephen T. C.
中科院分区：
文献类型：
--
作者：
Xue, Zhong;Li, Hai;Guo, Lei;Wong, Stephen T. C.
空间对齐扩散张量图像（DTI）是定量比较从不同受试者或同一受试者在不同时间点获得的神经图像的关键步骤。与传统的标量或多通道图像配准方法不同，DTI配准中应考虑张量方向。最近，文献中提出了几种 DTI 配准方法，但变形场纯粹依赖于张量特征而不是整个张量信息。其他方法，例如分段仿射变换和微分同胚非线性配准算法，通过同时考虑配准过程中张量的重新定向和变形来使用配准目标函数的解析梯度。然而，仅利用相对局部的张量信息，例如体素张量相似性。本文提出了一种新的 DTI 图像配准算法，称为基于局部快速行进（FM）的同时配准。该算法不仅考虑配准过程中张量的方向，而且利用每个体素的邻域张量信息来驱动变形，并且这种邻域张量信息是从感兴趣体素周围的局部快速行进算法中提取的。这些基于局部快速行进的张量特征有效地反映了球形邻域内每个体素周围的扩散模式，并且可以捕获解剖结构的相对独特的特征。使用模拟和真实的DTI人脑数据，实验结果表明，该算法比基于FA的配准更准确，并且比基于邻域张量相似性的配准更高效。
It is a key step to spatially align diffusion tensor images (DTI) to quantitatively compare neural images obtained from different subjects or the same subject at different timepoints. Different from traditional scalar or multi-channel image registration methods, tensor orientation should be considered in DTI registration. Recently, several DTI registration methods have been proposed in the literature, but deformation fields are purely dependent on the tensor features not the whole tensor information. Other methods, such as the piece-wise affine transformation and the diffeomorphic non-linear registration algorithms, use analytical gradients of the registration objective functions by simultaneously considering the reorientation and deformation of tensors during the registration. However, only relatively local tensor information such as voxel-wise tensor-similarity, is utilized. This paper proposes a new DTI image registration algorithm, called local fast marching (FM)-based simultaneous registration. The algorithm not only considers the orientation of tensors during registration but also utilizes the neighborhood tensor information of each voxel to drive the deformation, and such neighborhood tensor information is extracted from a local fast marching algorithm around the voxels of interest. These local fast marching-based tensor features efficiently reflect the diffusion patterns around each voxel within a spherical neighborhood and can capture relatively distinctive features of the anatomical structures. Using simulated and real DTI human brain data the experimental results show that the proposed algorithm is more accurate compared with the FA-based registration and is more efficient than its counterpart, the neighborhood tensor similarity-based registration.
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