Signed-distance function based non-rigid registration of image series with varying image intensity
Kateřina Škardová, Tomáš Oberhuber, Jaroslav Tintěra, Radomír Chabiniok · Discrete and Continuous Dynamical Systems - S · 2020
In this paper we propose a method for locally adjusted optical flow-based registration of multimodal images, which uses the segmentation of object of interest and its representation by the signed-distance function (OF \begin{document}$ ^{dist} $\end{document} method). We deal with non-rigid registration of the image series acquired by the Modiffied Look-Locker Inversion Recovery (MOLLI) magnetic resonance imaging sequence, which is used for a pixel-wise estimation of \begin{document}$ T_1 $\end{document} relaxation time. The spatial registration of the images within the series is necessary to compensate the patient's imperfect breath-holding. The evolution of intensities and a large variation of image contrast within the MOLLI image series, together with the myocardium of left ventricle (the object of interest) typically not being the most distinct object in the scene, makes the registration challenging. The paper describes all components of the proposed OF \begin{document}$ ^{dist} $\end{document} method and their implementation. The method is then compared to the performance of a standard mutual information maximization-based registration method, applied either to the original image (MIM) or to the signed-distance function (MIM \begin{document}$ ^{dist} $\end{document} ). Several experiments with synthetic and real MOLLI images are carried out. On synthetic image with a single object, MIM performed the best, while OF \begin{document}$ ^{dist} $\end{document} and MIM \begin{document}$ ^{dist} $\end{document} provided better results on synthetic images with more than one object and on real images. When applied to signed-distance function of two objects of interest, MIM \begin{document}$ ^{dist} $\end{document} provided a larger registration error, but more homogeneously distributed, compared to OF \begin{document}$ ^{dist} $\end{document} . For the real MOLLI image series with left ventricle pre-segmented using a level-set method, the proposed OF \begin{document}$ ^{dist} $\end{document} registration performed the best, as is demonstrated visually and by measuring the increase of mutual information in the object of interest and its neighborhood.