Motion correction for cardiac PET-MRI studies

We proposed an unsupervised deep learning-based approach for deformable three-dimensional cardiac MR image registration. This method learns a motion model that balances image similarity and motion estimation accuracy. We validated our approach comprehensively on three datasets and demonstrated higher motion estimation and registration accuracy relative to several popular state-of-the-art image registration methods (Morales 2019).