Abstract #4745

Incorporating Motion-Sorting Technique into Keyhole and k-t GROWL Compound System for Rapid Golden-angle Liver DCE Imaging

Zhifeng Chen¹, Liyi Kang¹, Ling Xia¹, Xia Kong², Allan Jin³, Zhongbiao Xu⁴, Yaohui Wang⁵, and Feng Liu⁶

¹Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, ²Wuhan Institute of Physics and Mathematics (WIPM) of Chinese Academy of Sciences, Wuhan, China, ³Philips Healthcare, Shanghai, China, ⁴School of Biomedical Engineering, Guangdong Provincial Key Laborary of Medical Image Processing, Southern Medical University, Guangzhou, China, ⁵South China University of Technology, Guangzhou, China, ⁶School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

Liver DCE imaging plays an increasingly important role in the diagnosis of liver diseases, including hepatic cirrhosis, hepatocellular carcinoma, etc. Motion is an inevitable problem in liver imaging, which often leads to motion artifacts and blurring on image details. We propose to incorporate motion-sorting technique into parallel imaging GROWL and Keyhole compound system for golden-angle radial dynamic contrast-enhanced MRI. The experimental results demonstrated that the proposed scheme can generate better image quality than non-motion-sorting techniques. Compared to the tested motion-sorting techniques, similar image quality can be offered with greatly reduced computational cost.

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