  Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique
for tumor motion management in image-guided radiation therapy (IGRT). However,
current 4D-MRI suffers from low spatial resolution and strong motion artifacts
owing to the long acquisition time and patients' respiratory variations; these
limitations, if not managed properly, can adversely affect treatment planning
and delivery in IGRT. Herein, we developed a novel deep learning framework
called the coarse-super-resolution-fine network (CoSF-Net) to achieve
simultaneous motion estimation and super-resolution in 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 verify 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 with enhanced anatomic features, yielding 4D-MR images with high
spatiotemporal resolution.
