Modular toolbox for derivative-based medical image registration

摘要

Registration of medical images, i.e. the integration of two or more images into a common geometrical system of reference so that corresponding image structures correctly align, is an active field of current research. Registration algorithms in general are composed of three main building blocks: a geometrical transformation is applied in order to transform the images into the geometrical system of reference, a similarity measure puts the comparison of the images into quantifiable terms, and an optimization algorithm searches for that transformation that leads to optimal similarity between the images. Whereas in the literature fixed configurations of registration algorithms are investigated, here we present a modular toolbox containing several similarity measures, transformation classes and optimization strategies. Derivative-free optimization is applicable for any similarity measure, but is not fast enough in clinical practice. Hence we consider much faster derivative-based Gauss-Newton and Levenberg-Marquardt optimization algorithms that can be used in conjunction with frequently needed similarity measures for which derivatives Can be easily obtained. The implemented similarity measures, geometrical transformations and optimization methods can be freely combined in order to configure a registration algorithm matching the requirements of a particular clinical application. Test examples show that particular algorithm configurations out of this toolbox allow e.g. for an improved lesion identification and localization in PET-CT or MR registration applications.