A compressive hybrid conjugate gradient image recovery approach for radial MRI

Abstract

ABSTRACTThis paper investigates an efficient compressed sensing (CS) approach that can be used to reconstruct radial magnetic resonance (MR) images from under-sampled measurements. In this approach, we propose a hybrid conjugate gradient (CG) method with a hybrid update parameter to optimize the CS cost function. With detailed mathematical proofs, the proposed CG method has proved to have sufficient descent and global convergence properties. In order to show efficiency of the proposed approach, experiments using a phantom and a living mouse cardiac example are carried out. Compared with two other widely used compressive CG approaches with undersampling rates from 5% to 20%, the proposed approach achieves better image quality and requires less running time. Meanwhile, the proposed H-CG approach can improve the robustness of magnetic resonance imaging image recovery above existing compressive CG approaches.