Potential of metric homotopy between intensity and geometry information for multi-modal 3D registration

作     者：Glodeck, Daniel Hesser, Juergen Zheng, Lei 

作者机构：Heidelberg Univ Univ Med Ctr Mannheim Dept Radiat Oncol Expt Radiat Oncol Heidelberg Germany Heidelberg Univ Interdisziplinary Ctr Sci Comp IWR Heidelberg Germany 

出 版 物：《ZEITSCHRIFT FUR MEDIZINISCHE PHYSIK》 (医学物理学杂志)

年 卷 期：2018年第28卷第4期

页      面：325-334页

核心收录：

学科分类：1009[医学-特种医学] 10[医学] 

基　　金：AiF [KF2769323CR4] state of Baden-Wurttemberg through bwHPC 

主　　题：Image registration Multi-modal image processing Multi-metric Weighting functions 

摘      要：This paper focuses on a novel strategy increasing robustness with respect to local optima when using Mutual Information (MI) in multi-modal image registration. This is realized by integrating additional geometry information in the cost function. Hereby, the main innovation is a generalization of multi-metric registration approaches by means of a metric homotopy. Particularly we realize a method which automatically determines the parameters of the metric homotopy. To construct the cost function independent of the choice of the optimizer, the weighting is defined as a function of one of the metrics instead of optimizer steps. In addition, a differentiable cost function is developed. In comparison to the commonly used technique to process an intensity based registration on different resolutions, the proposed method is three times faster with unchanged accuracy. It is also shown that in the presence of large landmark errors the proposed method outperforms an approach in accuracy in which both similarity functionals are applied one after the other. The method is evaluated on 3D multi-modal human brain data sets from the Retrospective Image Registration Evaluation Project (RIRE). The evaluation is performed using the evaluation website of the RIRE project to make the registration results of the proposed method easily comparable to other methods. Therefore, the presented results are also available online on the RIRE project page.