Geng Xiujuan, Gu Hong, Shin Wanyong, Ross Thomas J, Yang Yihong
National Institute on Drug Abuse, NIH, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):598-606. doi: 10.1007/978-3-642-15705-9_73.
We propose an unbiased group-wise diffeomorphic registration technique to normalize a group of diffusion tensor (DT) images. Our method uses an implicit reference group-wise registration framework to avoid bias caused by reference selection. Log-Euclidean metrics on diffusion tensors are used for the tensor interpolation and computation of the similarity cost functions. The overall energy function is constructed by a diffeomorphic demons approach. The tensor reorientation is performed and implicitly optimized during the registration procedure. The performance of the proposed method is compared with reference-based diffusion tensor imaging (DTI) registration methods. The registered DTI images have smaller shape differences in terms of reduced variance of the fractional anisotropy maps and more consistent tensor orientations. We demonstrate that fiber tract atlas construction can benefit from the group-wise registration by producing fiber bundles with higher overlaps.
我们提出一种无偏的组内微分同胚配准技术，用于对一组扩散张量（DT）图像进行归一化处理。我们的方法使用隐式参考组内配准框架，以避免因参考选择而导致的偏差。扩散张量上的对数欧几里得度量用于张量插值和相似性代价函数的计算。整体能量函数通过微分同胚 demons 方法构建。在配准过程中进行张量重新定向并隐式优化。将所提出方法的性能与基于参考的扩散张量成像（DTI）配准方法进行比较。配准后的 DTI 图像在分数各向异性图的方差减小方面具有更小的形状差异，并且张量方向更一致。我们证明，通过生成具有更高重叠度的纤维束，纤维束图谱构建可受益于组内配准。
