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Real-time imaging can be used to monitor, analyze and control medical treatments. In this thesis, we want to explain the spatiotemporal motion and thus enable more advanced procedures, especially real-time adaptation in radiation therapy. The motion occurring between image acquisitions can be quantified by image registration, which generates a mapping between the images. The contribution of the thesis consists of three papers, where we have used different approaches to estimate the motion between images.

In Paper I, we combine a state-of-the-art method in real-time tracking with a learned sparse-to-dense interpolation scheme. For this, we track an arbitrary number of regions in a sequence of medical images. We estimated a sparse displacement field, based on the tracking positions and used the interpolation network to achieve its dense representation.

Paper II was a contribution to a challenge in learnable image registration where we finished at 2nd place. Here we train a deep learning method to estimate the dense displacement field between two images. For this, we used a network architecture inspired by both conventional medical image registration methods and optical flow in computer vision.

For Paper III, we estimate the dynamics of spatiotemporal images by training a generative network. We use nonlinear dimensional reduction techniques and assume a linear dynamic in a low-dimensional latent space. In comparison with conventional image registration methods, we provide a method more suitable for real-world scenarios, with the possibility of imputation and extrapolation. Although the problem is challenging and several questions are left unanswered we believe a combination of conventional, learnable, and dynamic modeling of the motion is the way forward.