Dynamic MRI Reconstruction with Motion-Guided Network

Qiaoying Huang, Dong Yang, Hui Qu, Jingru Yi, Pengxiang Wu, Dimitris Metaxas
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:275-284, 2019.

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 that unitizes deep neural networks with motion information to improve reconstruction quality. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components: dynamic reconstruction, motion estimation and motion compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches.

Cite this Paper


BibTeX
@InProceedings{pmlr-v102-huang19a, title = {Dynamic MRI Reconstruction with Motion-Guided Network}, author = {Huang, Qiaoying and Yang, Dong and Qu, Hui and Yi, Jingru and Wu, Pengxiang and Metaxas, Dimitris}, booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning}, pages = {275--284}, year = {2019}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, volume = {102}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v102/huang19a/huang19a.pdf}, url = { http://proceedings.mlr.press/v102/huang19a.html }, 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 that unitizes deep neural networks with motion information to improve reconstruction quality. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components: dynamic reconstruction, motion estimation and motion compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches.} }
Endnote
%0 Conference Paper %T Dynamic MRI Reconstruction with Motion-Guided Network %A Qiaoying Huang %A Dong Yang %A Hui Qu %A Jingru Yi %A Pengxiang Wu %A Dimitris Metaxas %B Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2019 %E M. Jorge Cardoso %E Aasa Feragen %E Ben Glocker %E Ender Konukoglu %E Ipek Oguz %E Gozde Unal %E Tom Vercauteren %F pmlr-v102-huang19a %I PMLR %P 275--284 %U http://proceedings.mlr.press/v102/huang19a.html %V 102 %X 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 that unitizes deep neural networks with motion information to improve reconstruction quality. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components: dynamic reconstruction, motion estimation and motion compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches.
APA
Huang, Q., Yang, D., Qu, H., Yi, J., Wu, P. & Metaxas, D.. (2019). Dynamic MRI Reconstruction with Motion-Guided Network. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 102:275-284 Available from http://proceedings.mlr.press/v102/huang19a.html .

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