Coarse–Super-Resolution–Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI With Simultaneous Motion Estimation and Super-Resolution

In this study, we developed a novel deep learning framework called the coarse–super-resolution–fine network (CoSF-Net) to achieve simultaneous motion estimation and super-resolution within a unified model. We designed CoSF-Net by fully excavating the inherent properties of 4D-MRI, with consideration of limited and imperfectly matched training datasets. We conducted extensive experiments on multiple real patient datasets to assess the feasibility and robustness of the developed network. Compared with existing networks and three state-of-the-art conventional algorithms, CoSF-Net not only accurately estimated the deformable vector fields between the respiratory phases of 4D-MRI but also simultaneously improved the spatial resolution of 4D-MRI, enhancing anatomical features and producing 4D-MR images with high spatiotemporal resolution.
Source: IEE Transactions on Medical Imaging - Category: Biomedical Engineering Source Type: research