Fast Preconditioning for Accelerated Multi-contrast MRI Reconstruction

Real-time reconstruction in multi-contrast magnetic resonance imaging (MC-MRI) is very challenging due to the slow scanning and reconstruction process. In this study, we propose a novel algorithm to accelerate the MC-MRI reconstruction in the framework of compressed sensing. The problem is formulated as the minimization of the least square data fitting with joint total variation (JTV) regularization term. We first utilized the iterative reweighted least square (IRLS) framework to reformulate the problem. A joint preconditioner is dexterously designed to efficiently compute the inverse of large transform matrix at each iteration. We compared our algorithm with eight cutting-edge compressive sensing MRI algorithms on real MC-MRI dataset. Extensive experiments demonstrate that the proposed algorithm can achieve far better reconstruction performance than all other eight cutting-edge methods.