张量值图像的变分配准

S. Barbieri, M. Welk, J. Weickert
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引用次数: 3

摘要

我们提出了一个张量值图像配准的变分框架。它基于一个包含四项的能量泛函:一个基于扩散张量常数约束的数据项,一个编码连接域变形和张量重定向的物理模型的兼容性项,以及一个用于变形和张量重定向的平滑项。虽然本文采用的张量变形模型是针对弥散张量MRI数据设计的,但由于数据与相容项的分离,使得模型可以很容易地适应不同的张量变形模型。我们通过多尺度梯度下降最小化了两个变换场的能量泛函。实验证明了该方法在张量值图像配准中的可行性和潜力。
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Variational registration of tensor-valued images
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.
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