Juan Carlos Ramirez Giraldo1, Joshua D. Trzasko1, Armando Manduca1
1Center for Advanced Imaging Research, Mayo Clinic, Rochester, MN, USA
Compressed sensing(CS) can provide accurate reconstructions from highly undersampled data for certain types of MR scans, allowing faster acquisition times. Standard CS is based on l1-norm minimization, which offers mathematical guarantees of global convergence. It is known that fewer samples are required for CS based on minimization of an l0-norm, but this is mathematically more difficult and no convergence guarantees exist. We compare standard l1-norm CS with two algorithms that approximate the l0-norm with the lp-norm (p between 0 and 1), with different sampling pattern densities and parameterizations. Despite the lack of theoretical guarantees, both lp-norm algorithms always outperformed standard l1-norm CS.