Dynamic MRI reconstruction with end-to-end motion-guided network

Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of body regions. Modeling such information into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. However, existing deep learning based approaches neglect motion information during the reconstruction procedure, while traditional motion-guided methods are hindered by heuristic parameter tuning and long inference time. We propose a novel dynamic MRI reconstruction approach called MODRN and an end-to-end improved version called MODRN(e2e), both of which enhance the reconstruction quality by infusing motion information into the modeling process with deep neural networks. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components: Dynamic Reconstruction Network, Motion Estimation and Motion Compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-ofthe-art approaches. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

Temporal correlation in dynamic MRI, such as cardiac MRI, is important for understanding motion mechanisms of body regions. Existing deep learning based approaches neglect motion information during reconstruction, while traditional methods are hindered by heuristic parameter tuning and long inference time. The proposed MODRN and MODRN(e2e) enhance reconstruction quality by infusing motion information with deep neural networks, showing effectiveness compared to state-of-the-art approaches.