Super-resolution reconstruction of dynamic MRI by patch learning

Achieving both high spatial and temporal resolution is desired in dynamic Magnetic Resonance Imaging (MRI), however, it is difficult to satisfy because of the slow scanning speed of MRI caused by physical and physiological limits. In order to guarantee the temporal resolution, the amount of acquired k-space data is usually reduced. In this paper, a novel method-patch learning-based dynamic MRI super-resolution reconstruction, is proposed, where high resolution dynamic images are reconstructed based on the sampled low frequency k-space data together with a small amount of fully sampled frames as training data. The proposed method is also demonstrated by in-vivo dynamic MRI data.