Efficient and Phase-aware Video Super-resolution for Cardiac MRI
This work addresses the need for efficient and high-quality cardiac MRI scans without hardware upgrades, which is important for medical imaging applications, though it appears incremental as it builds on existing super-resolution techniques with domain-specific adaptations.
The authors tackled the problem of high-quality cardiac MRI acquisition being time-consuming and costly by proposing a novel end-to-end trainable network for video super-resolution, which demonstrated superiority over state-of-the-art methods in extensive experiments.
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.