Nonrigid registration of 3D tensor medical data
Nonrigid registration of 3D tensor medical data
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DOI：
10.1016/s1361-8415(02)00055-5
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发表时间：
2002-06-01
影响因子：
10.9
通讯作者：
Kikinis, R
中科院分区：
文献类型：
--
作者：
Ruiz-Alzola, J;Westin, CF;Kikinis, R
提供多值数据的新医学成像模式（例如相衬 MRA 和扩散张量 MM）需要通用表示来开发自动化算法。在本文中，我们提出了一个使用局部匹配来注册医疗体积多值数据的统一框架。本文将两个待匹配数据之间的相似性概念（通常用于标量（强度）数据）扩展到一般张量情况。我们的配准方法基于多分辨率方案，其中传播在较粗级别中估计的变形场以在下一个较精细级别中提供初始变形。在每个级别中，执行具有高度局部结构的区域的局部匹配以及随后的插值。因此，我们提供了一种算法，通过梯度和相关性计算来评估通用多值数据中的结构量。插值步骤是通过克里金估计器执行的，它为医学应用中的稀疏向量场插值提供了一个新颖的框架。合成数据和临床数据的结果说明了该方法的可行性。 (C) 2002 Elsevier Science B.V. 保留所有权利。
New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MM, 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. (C) 2002 Elsevier Science B.V. All rights reserved.