Eckl Jutta, Daum Volker, Hornegger Joachim, Pohl Kilian M
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg.
Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-Universität Erlangen-Nürnberg.
Proc IEEE Int Symp Biomed Imaging. 2012 May;2012:704-707. doi: 10.1109/ISBI.2012.6235645. Epub 2012 Jul 12.
Registration methods frequently rely on prior information in order to generate anatomical meaningful transformations between medical scans. In this paper, we propose a novel intensity based non-rigid registration framework, which is guided by landmarks and a regularizer based on Principle Component Analysis (PCA). Unlike existing methods in this domain, the computational complexity of our approach reduces with the number of landmarks. Furthermore, our PCA is invariant to translations. The additional regularizer is based on the outcome of this PCA. We register a skull CT scan to MR scans aquired by a MR/PET hybrid scanner. This aligned CT scan can then be used to gain an attenuation map for PET reconstruction. As a result we have a Dice coefficient for bone areas at 0.71 and a Dice coefficient for bone and soft issue areas at 0.97.
配准方法通常依赖先验信息，以便在医学扫描之间生成具有解剖学意义的变换。在本文中，我们提出了一种基于强度的新型非刚性配准框架，该框架由地标引导，并基于主成分分析（PCA）的正则化器。与该领域的现有方法不同，我们方法的计算复杂度随着地标数量的增加而降低。此外，我们的PCA对平移具有不变性。额外的正则化器基于该PCA的结果。我们将颅骨CT扫描与由MR/PET混合扫描仪获取的MR扫描进行配准。然后，这种对齐的CT扫描可用于获取PET重建的衰减图。结果，我们得到骨区域的Dice系数为0.71，骨和软组织区域的Dice系数为0.97。
