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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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