Enhancing 4d Cardiac Mri Registration Network With A Motion Prior Learned From Coronary Cta

Registration between phases in 4D cardiac MRI is essential for reconstructing high-quality anatomy and appreciating the dynamics. Complex sequential compartmental motion and heterogeneous image quality make it challenging to design regularization functionals in classic optimization settings. In this study, we propose to introduce a novel motion representation model (MRM) into an image registration network to impose spatially variant prior for cardiac motion. A set of highly representative deformation vector fields (DVFs) were generated from high-contrast CTA images. In the form of a convolutional auto-encoder, the MRM was trained to capture the spatial variant pattern of the DVF Jacobian. The CT-derived MRM was then incorporated into an unsupervised network to facilitate 4D MRI registration. Our method was evaluated on ten 4D MRI scans with multi-compartment manual segmentations and achieved 2.25 mm target registration errors (TRE) on left ventricle. Compared to networks without MRM, introduction of the MRM reduced TREs on two ventricles and pulmonary artery with statistical significance. Compared to the tuned SimpleElastix, our method achieved comparable results on all compartments without statistical significance, but with a much shorter registration time of 0.02 s.