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A variational approach to the registration of tensor-valued images

机译:张量值图像配准的变分方法

摘要

We present a variational framework for the registration of tensor valued images. It is based on an energy functional with four terms: a data term based on a diffusion tensor constancy constraint, a compatibility term encoding the physical model linking domain deformations and tensor reorientation, and smoothness terms for deformation and tensor reorientation. Although the tensor deformation model employed here is designed with regard to diffusion tensor MRI data, the separation of data and compatibility term allows to adapt the model easily to different tensor deformation models. We minimise the energy functional with respect to both transformation fields by a multiscale gradient descent. Experiments demonstrate the viability and potential of this approach in the registration of tensor-valued images.
机译:我们提出了张量值图像配准的变体框架。它基于具有四个项的能量函数:基于扩散张量恒定性约束的数据项,编码将域变形和张量重新定向联系在一起的物理模型的兼容性项以及用于变形和张量重新定向的平滑项。尽管此处采用的张量变形模型是针对扩散张量MRI数据设计的,但数据和兼容性项的分离使该模型易于适应不同的张量变形模型。通过多尺度梯度下降,我们将两个转换场的能量函数最小化。实验证明了这种方法在张量值图像配准中的可行性和潜力。

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