1 research outputs found
Efficient and Phase-aware Video Super-resolution for Cardiac MRI
Cardiac Magnetic Resonance Imaging (CMR) is widely used since it can
illustrate the structure and function of heart in a non-invasive and painless
way. However, it is time-consuming and high-cost to acquire the high-quality
scans due to the hardware limitation. To this end, we propose a novel
end-to-end trainable network to solve CMR video super-resolution problem
without the hardware upgrade and the scanning protocol modifications. We
incorporate the cardiac knowledge into our model to assist in utilizing the
temporal information. Specifically, we formulate the cardiac knowledge as the
periodic function, which is tailored to meet the cyclic characteristic of CMR.
In addition, the proposed residual of residual learning scheme facilitates the
network to learn the LR-HR mapping in a progressive refinement fashion. This
mechanism enables the network to have the adaptive capability by adjusting
refinement iterations depending on the difficulty of the task. Extensive
experimental results on large-scale datasets demonstrate the superiority of the
proposed method compared with numerous state-of-the-art methods.Comment: MICCAI 202