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Nonrigid registration of 3D tensor medical data.

机译：3D张量医学数据的非刚性注册。

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New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, require general representations for the development of automated algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data using local matching. The paper extends the usual concept of similarity between two pieces of data to be matched, commonly used with scalar (intensity) data, to the general tensor case. Our approach to registration is based on a multiresolution scheme, where the deformation field estimated in a coarser level is propagated to provide an initial deformation in the next finer one. In each level, local matching of areas with a high degree of local structure and subsequent interpolation are performed. Consequently, we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator, which provides a novel framework for the interpolation of sparse vector fields in medical applications. The feasibility of the approach is illustrated by results on synthetic and clinical data.
机译：提供多值数据的新医学成像模式，例如相衬MRA和扩散张量MRI，需要通用表示法来开发自动化算法。在本文中，我们提出了使用本地匹配来注册医疗体积多值数据的统一框架。本文将通常要用于标量（强度）数据的要匹配的两个数据之间的相似性的一般概念扩展到一般张量情况。我们的配准方法基于多分辨率方案，其中以较粗略的水平估计的变形场被传播以在下一个更精细的变形场中提供初始变形。在每个级别中，执行具有高度局部结构的区域的局部匹配和随后的内插。因此，我们提供了一种通过梯度和相关计算来评估通用多值数据中的结构量的算法。插值步骤通过Kriging估计器执行，该估计器为医学应用中的稀疏矢量场插值提供了一个新颖的框架。综合和临床数据结果说明了该方法的可行性。

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