MRI-guided radiotherapy (MRgRT) enables new ways to improve dose delivery to moving tumors and the organs-at-risk (e.g. in abdomen) by steering the radiation beam based on real-time MRI. While state-of-the-art techniques (e.g. compressed sensing) can provide the required acquisition speed, the corresponding reconstruction time is too long for real-time processing. In this work, we investigate the use of multiple deep neural networks for image reconstruction and subsequent motion estimation. We show that a single motion estimation network can estimate high-quality 2D deformation vector fields from aliased images, even for high undersampling factors up to R=25.
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