1Department
of Electrical Engineering, University at Buffalo, Buffalo, NY, United States;
2Department of Biomedical Engineering, University at Buffalo,
Buffalo, NY, United States; 3Department of Radiology, Washington
University, St. Louis, MO, United States
In this study, a broader family of nonlinear transforms is investigated for sparse representation of dynamic images in compressed sensing (CS). We propose a novel kernel-based CS method that implicitly and adaptively sparsifies the dynamic image series of interest using nonlinear transforms. The proposed method is evaluated using accelerated arterial spin labeled perfusion data. It is shown to be able to better preserve the spatial and temporal information than the conventional CS method with linear transforms.