State-of-the-art deformable image registration approaches achieve impressive results and are commonly used in diverse image processing applications. However, these approaches are computationally expensive even on GPUs  due to their requirement to solve an optimization problem for each image pair during registration . Most Learning based methods either required labeled data or do not guarantee a diffeomorphic registration or deformation field reversibility . Adrian V. Dalca et. al. presented an unsupervised Deep-Learning framework for diffeomorphic image registration named Voxelmorph in .
In this thesis the network described in  will be implemented and trained on Cardiac Magnetic Resonance images to build an application for fast diffeomorphic image registration. The results will be compared to state-of-the-art diffeomorphic image registration methods. Additionally the method will be evaluated by comparing segmented areas as well as landmark locations of co-registered images. Furthermore the method in  will be extended to a one-to-many registration method using the approach in  to fulfill the desire for motion estimation of anatomy of interest for increasingly available dynamic imaging data . Data used in this thesis will be provided by Siemens Healthineers. The implementation will be done using a open source framework like PyTorch .
The thesis will include the following points:
• Literature research of the topic of state-of-the-art methods regarding diffeomorphic image registration and one to many registration
• Implementing a Neural Network for diffeomorphic image regstration and extending it to a one-to-many registration
• Comparison of the results with state-of-the-art image registration methods
 Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J. V. & Dalca, A. V. VoxelMorph: A Learning Framework for
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 Ashburner, J. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95 –113. ISSN: 1053-8119. http://www.sciencedirect.com/science/article/pii/S1053811907005848 (2007).
 Metz, C., Klein, S., Schaap, M., van Walsum, T. & Niessen, W. Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach. Medical Image Analysis 15, 238 –249. ISSN: 1361-8415. http://www.sciencedirect.com/science/article/pii/S1361841510001155 (2011).
 Paszke, A. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. CoRR abs/1912.01703. arXiv: 1912.01703. http://arxiv.org/abs/1912.01703 (2019).