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.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Huang Q,Xian Y,Yang D,Qu H,Yi J,Wu P,Metaxas DNdoi
10.1016/j.media.2020.101901subject
Has Abstractpub_date
2021-02-01 00:00:00pages
101901eissn
1361-8415issn
1361-8423pii
S1361-8415(20)30265-6journal_volume
68pub_type
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