L1-Norm Regularization of Coil Sensitivities in Non-Linear Parallel Imaging Reconstruction

Carlos Fernndez-Granda^1,2, Julien Sngas³

Joint estimation of the coil sensitivities and the image in parallel imaging can suppress aliasing more effectively than methods based on low-resolution sensitivity estimates. We propose a joint estimation approach related to Compressed Sensing that exploits the sparsity of the coil sensitivities in k-space and in a base of Chebyshev polynomials within a greedy scheme to solve the ill-posed reconstruction problem. In vivo data reconstructions are presented and compared to results obtained with Generalized SENSE and Joint SENSE.