Learing Sampling and Reconstruction Using Bregman Iteration for CS-MRI

Abstract

The purpose of compressed sensing magnetic resonance imaging (CS-MRI) is to reconstruct clear images using data from the Nyquist sampling space. By reducing the amount of sampling, MR imaging can be accelerated, thereby improving the efficiency of device data collection and increasing patient throughput. The two basic challenges in CS-MRI are designing sparse sampling masks and designing effective reconstruction algorithms. In order to be consistent with the analysis conclusion of CS theory, we propose a bi-level optimization model to optimize the sampling mask and the reconstruction network at the same time under the constraints of data terms. The proposed sampling sub-network is based on an additive gradient strategy. In our reconstructed subnet, we design a phase deep unfolding network based on the Bregman iterative algorithm to find the solution of constrained problems by solving a series of unconstrained problems. Experiments on two widely used MRI datasets show that our proposed model yields sub-sampling patterns and reconstruction models customized for training data, achieving state-of-the-art results in terms of quantitative metrics and visual quality.