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Abstract #0577

Compressed-sensing dynamic imaging with self-learned nonlinear dictionary

Ukash Nakarmi 1 , Yanhua Wang 1 , Jingyuan Lyu 1 , Jie Zheng 2 , and Leslie Ying 1,3

1 Dept. of Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States, 2 Dept. of Radiology, Washington University, School of Medicine, MO, United States, 3 Dept. of Biomedical Engineering, State University of New York at Buffalo, NY, United States

In this abstract, we introduce a nonlinear polynomial-kernel-based model to represent the dynamic MR images sparsely. Based on the model, a novel compressed-sensing dMRI method with self-learned nonlinear dictionary (NL-D) is proposed. Simulation results show that the proposed method outperforms the conventional CS dMRI methods with linear transforms.

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