Efficient and Phase-aware Video Super-resolution for Cardiac MRI
Jhih-Yuan Lin, Yu-Cheng Chang, Winston H. Hsu

TL;DR
This paper introduces a novel, end-to-end trainable video super-resolution network for cardiac MRI that leverages cardiac cycle knowledge and residual learning to enhance image quality without hardware changes.
Contribution
It presents a phase-aware, cyclic knowledge-based model with residual of residual learning for improved cardiac MRI super-resolution.
Findings
Outperforms state-of-the-art methods on large-scale datasets.
Effectively utilizes cardiac cycle information for super-resolution.
Adaptive refinement improves reconstruction quality.
Abstract
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…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced MRI Techniques and Applications · Image and Signal Denoising Methods
