2019-05-24-huang19a.md

section	title	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Contributed Papers	Dynamic MRI Reconstruction with 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 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.	inproceedings	Proceedings of Machine Learning Research	huang19a	0	Dynamic MRI Reconstruction with Motion-Guided Network	275	284	275-284	275	false	Huang, Qiaoying and Yang, Dong and Qu, Hui and Yi, Jingru and Wu, Pengxiang and Metaxas, Dimitris	given family Qiaoying Huang given family Dong Yang given family Hui Qu given family Jingru Yi given family Pengxiang Wu given family Dimitris Metaxas	2019-05-24		PMLR	Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning	102	inproceedings	date-parts 2019 5 24	http://proceedings.mlr.press/v102/huang19a/huang19a.pdf