A Model-Based Deep Network for MRI Reconstruction Using Approximate Message Passing Algorithm

Abstract

We propose a novel model-based network to reconstruct the magnetic resonance (MR) image. In this network, the Approximate Message Passing (AMP) algorithm is unrolled to solve the optimization problem of compressed sensing MR imaging, and several CNN blocks is embedded as de-aliasing steps. We relax the restriction on the parameter selection of AMP algorithm, and enable the parameters trainable in our proposed method. Each CNN block and AMP block is followed by a data consistency (DC) operation, which can efficiently accelerate the convergence of the reconstruction network. The trainable parameters of our DC share the weights and can automatically adapt to the error pattern. Experimental results show that the proposed method obtains faster convergence speed and achieves a new state-of-the-art MR image reconstruction performance.