A synthetic combination of accurate de-enhanced registration and dynamic artificial sparsity for robust high-resolution liver DCE-MRI

Zhifeng Chen, Yujia Zhou, Xinyuan Zhang, Peiwei Yi, Zhongbiao Xu, Jian Gong, Zhenguo Yuan, Xia Kong, Yaohui Wang, Ling Xia, Wufan Chen, Yanqiu Feng, Feng Liu

Research output: Contribution to conferenceAbstractpeer-review


High spatiotemporal DCE-MRI is a valuable tool in liver disease diagnoses and treatments. Recently, there is a growing research trend which focuses on the motion-robustness of liver DCE-MRI. However, current techniques cannot simultaneously solve the motion problem when pursuing high spatiotemporal resolution. In this work, we propose to combine an accurate registration technique with dynamic artificial sparsity for high spatiotemporal resolution DCE-MRI of liver. The experiments indicated that the proposed framework results in better image quality than iGRASP due to de-enhanced image registration. Compared to motion-sorting techniques, the proposed framework generates better temporal resolution.
Original languageEnglish
Number of pages3
Publication statusPublished - 2020
Externally publishedYes
EventAnnual Meeting of ISMRM 2020 - Online, France
Duration: 8 Aug 202014 Aug 2020
Conference number: 28th


ConferenceAnnual Meeting of ISMRM 2020
Internet address

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