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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