Abstract #1651

RM-GROWL-GRASP: Image Registration Involved Two-step Motion Compensation System for Real-time Non-Cartesian Liver DCE-MRI

Zhifeng Chen^1,2, Peiwei Yi^1,2, Zhongbiao Xu^1,2, Jucheng Zhang³, Yingjie Mei^1,2, Xia Kong⁴, Zhenguo Yuan⁵, Yaohui Wang⁶, Ling Xia⁷, Yanqiu Feng^1,2, and Feng Liu⁸

¹School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China, ²Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China, ³Department of Radiology, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China, ⁴School of Computer and Information Science, Hubei Engineering University, Wuhan, China, ⁵Shandong Medical Imaging Research Institute, Shandong University, Jinan, China, ⁶Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China, ⁷Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, ⁸School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia

Motion is an inescapable problem in abdominal MRI. Involuntary organ movements caused mainly by respiratory often results in motion artifacts and image details blurring in liver MRI. For dynamic imaging, motion also harms temporal information. Recently, high spatiotemporal resolution free-breathing liver DCE-MRI have attracted much attentions of radiologists and scholars. We propose to combine mutual-information-based image registration with motion-sorted GROWL-GRASP approach for golden-angle radial liver DCE-MRI, which enable free-breathing imaging. The results demonstrate that better image quality including SNR benefit, lower motion artifacts and more diagnostic information can be generated compared to current motion compensation methods.

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