Diagnostic applications often require the estimation of organ motion. Image registration enables motion estimation by computing deformation fields for an image pair. In this work, voxelmorph, a framework for deep learning-based diffeomorphic image registration is used to register CINE cardiac MR images in four-chamber view. Additionally, the framework is extended to a one-to-many registration to also utilize temporal information within a time-resolved MR scan. Registration performance as well as the performance of a valve tracking application using this approach are evaluated. The results are comparable to a state-of-the-art registration method, while noticeably reducing the computation time.
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