Md Mashud Hyder1, 2, Bradley Peterson1, 2, Zhengchao Dong1, 2
1Brain Imaging Lab, Columbia University, New York, NY, United States; 2New York State Psychiatric Inst., New York, NY, United States
The theory of compressed sensing (CS) states that MRI images can be recovered efficiently from randomly undersampled k-space data under certain conditions. Many MRI images have sparse representation when we calculate their spatial finite-differences or wavelet coefficients. In this work we show that spatial finite-differences of a set of wavelet coefficients can increase the sparsity of MRI image, which results in improved recovery of CS MRI.