In this work, we study the influence of prior knowledge in learning-based non-Cartesian 2D CINE MR image reconstruction. The proposed approach uses a novel minimal deep learning setup to embed the acquired non-Cartesian multi-coil data and conventional spatio-temporal (3D and 2D+t) Fields-of-Experts regularization in a proximal gradient variational network, achieving promising results for up to 12-fold retrospectively undersampled tiny golden-angle radial CINE imaging.
This abstract and the presentation materials are available to members only; a login is required.