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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >On the development of a new non-rigid image registration using deformation based grid generation
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On the development of a new non-rigid image registration using deformation based grid generation

机译：使用基于变形的网格生成开发新的非刚性图像配准

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摘要

In this paper, we present the latest results of the development of a novel non-rigid image registration method (NiRuDeGG) using a well-established mathematical framework known as the deformation based grid generation. The deformation based grid generation method is able to generate a grid with desired grid density distribution which is free from grid folding. This is achieved by devising a positive monitor function describing the anticipated grid density in the computational domain. Based on it, we have successfully developed a new non-rigid image registration method, which has many advantages. Firstly, the functional to be optimized consists of only one term, a similarity measure. Thus, no regularization functional is required in this method. In particular, there is no weight to balance the regularization functional and the similarity functional as commonly required in many non-rigid image registration methods. Nevertheless, the regularity (no mesh folding) of the resultant deformation is theoretically guaranteed by controlling the Jacobian determinant of the transformation. Secondly, since no regularization term is introduced in the functional to be optimized, the resultant deformation field is highly flexible that large deformation frequently experienced in inter-patient or image-atlas registration tasks can be accurately estimated. Detailed description of the deformation based grid generation, a least square finite element (LSFEM) solver for the underlying div-curl system, and a fast div-curl solver approximating the LSFEM solution using inverse filtering, along with several 2D and 3D experimental results are presented.
机译：在本文中，我们介绍了一种新的非刚性图像配准方法（NiRuDeGG）的最新开发成果，该方法使用了众所周知的基于变形网格生成的数学框架。基于变形的网格生成方法能够生成具有期望的网格密度分布的网格，该网格没有网格折叠。这是通过设计一个正向监视函数来实现的，该函数描述了计算域中的预期网格密度。在此基础上，我们成功开发了一种新的非刚性图像配准方法，它具有许多优点。首先，要优化的功能仅包含一项，即相似性度量。因此，在该方法中不需要正则化功能。特别地，没有权衡在许多非刚性图像配准方法中通常需要的正则化功能和相似性功能。尽管如此，理论上通过控制变换的雅可比行列式可以保证合成变形的规则性（无网格折叠）。其次，由于未在要优化的功能中引入正则项，因此生成的变形字段具有很高的灵活性，可以准确估计在患者间或图像集注册任务中经常遇到的大变形。详细介绍了基于变形的网格生成，用于基础div-curl系统的最小二乘有限元（LSFEM）求解器以及使用逆滤波近似LSFEM解决方案的快速div-curl求解器，以及一些2D和3D实验结果。提出了。

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