Compressed Sensing of Phase-Varied MR Images Using ADMM and Multi-Sparsifying Transform

Abstract

In most MR scanning, image reconstruction is executed by taking the inverse Fourier transform of acquired signal. However, image reconstruction in compressed sensing requires the choice of sparsifying transform function and algorithms for L1 norm minimization of cost function, therefore, the obtained image quality depends on those functions and reconstruction algorithms. In this paper, comparison of iterative shrinkage algorithm and ADMM (alternating direction method of multipliers) in compressed sensing of phase-varied MR images was performed. Simulation studies showed that the highest PSNR was obtained in ADMM combined with multi-scale sparsifying transform function.