Parallel non-Cartesian Spatial-Temporal Dictionary Learning Neural Networks (stDLNN) for Accelerating Dynamic MRI

Dynamic MRI shows promising clinical values and several applications have been investigated such as cardiac, pulmonary, and hepatic imaging. However, a successful application of dynamic MRI is hampered by its time-consuming acquisition. To improve the performance and interpretability for the accelerating reconstruction methods, we proposed the parallel non-Cartesian Spatial-Temporal Dictionary Learning Neural Networks (stDLNN), which combines the traditional spatial-temporal dictionary learning methods with the deep neural networks for accelerating dynamic MRI. It has favorable interpretability and provides better image quality than the state-of-the-art CS methods (L+S, BCS) and deep learning methods (DCCNN, PNCRNN), especially at high acceleration rate at R=25.