Abstract #3346

A Synthetic Combination of Accurate De-enhanced Registration and Dynamic Artificial Sparsity for Robust High-Resolution Liver DCE-MRI

Zhifeng Chen¹, Yujia Zhou¹, Xinyuan Zhang¹, Peiwei Yi¹, Zhongbiao Xu², Jian Gong¹, Zhenguo Yuan³, Xia Kong⁴, Yaohui Wang⁵, Ling Xia⁶, Wufan Chen¹, Yanqiu Feng¹, and Feng Liu⁷

¹School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China, ²Department of Radiotherapy, Cancer Center, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Science, Guangzhou, China, ³Shandong Medical Imaging Research Institute, Shandong University, Jinan, China, ⁴School of Computer and Information Science, Hubei Engineering University, Wuhan, China, ⁵Division of Superconducting Magnet Science and Technology, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, China, ⁶Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, ⁷School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

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.

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