Registration with probabilistic correspondences - Accurate and robust registration for pathological and inhomogeneous medical data
Registration with probabilistic correspondences - Accurate and robust registration for pathological and inhomogeneous medical data
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DOI：
10.1016/j.cviu.2019.102839
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发表时间：
2020-01-01
影响因子：
4.5
通讯作者：
Ehrhardt, Jan
中科院分区：
文献类型：
--
作者：
Krueger, Julia;Schultz, Sandra;Ehrhardt, Jan
两幅医学图像的配准通常基于在两幅图像中都存在对应区域这一假设。如果这一假设因例如病变而不成立，大多数方法就会遇到问题。所提出的配准方法基于稀疏图像表示的概率对应关系，并能够对可能缺失的对应关系进行稳健处理。采用最大后验概率框架来推导关于变形参数的优化准则，其目的是减少配准图像之间的形状和外观差异。多分辨率方法加快了优化速度并提高了配准的稳健性。计算出的概率对应关系使该方法能够处理图像中缺失的对应关系。此外，它们还提供了关于拟合质量以及可能不对应/病变图像区域的额外信息。将该方法与使用脑部磁共振和心脏图像的两种最先进的配准方法进行了比较：一种基于变分强度的配准算法和一种使用离散优化方案的基于特征的配准方法。使用额外模拟的中风病灶进行的综合定量评估表明，所提出的方法具有显著更高的准确性和稳健性。此外，对应概率图被用于表征脑部磁共振数据中的病变区域。
The registration of two medical images is usually based on the assumption that corresponding regions exist in both images. If this assumption is violated by e. g. pathologies most approaches encounter problems. The registration approach proposed is based on probabilistic correspondences of sparse image representations and enables a robust handling of potentially missing correspondences. A maximum a-posteriori framework is used to derive an optimization criterion with respect to deformation parameters that aim to reduce the shape and appearance differences between the registered images. A multi-resolution approach speeds up the optimization and increases the robustness of the registration. The computed probabilistic correspondences enable the approach to deal with missing correspondences in the images. Furthermore, they provide additional information about the quality of fit and potentially non-corresponding/pathological image regions. The approach is compared to two state-of-the-art registration methods using MR brain and cardiac images: a variational intensity-based registration algorithm and a feature-based registration approach using a discrete optimization scheme. The comprehensive quantitative evaluation using additional simulated stroke lesions shows a significantly higher accuracy and robustness of the proposed approach. Furthermore, the correspondence probability maps were used to characterize pathological regions in the MRI brain data.