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

Qiaoying Huang, Yikun Xian, Dong Yang, Hui Qu, Jingru Yi, Pengxiang Wu, Dimitris N. Metaxas

Abstract: Highlights • A recurrent neural network for spatial-temporal data reconstruction • A network for motion estimation with a novel loss capturing long-term motion information • Two versions of motion-guided deep neural networks for dynamic MRI reconstruction Abstract 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-of-the-art approaches.