Parallel imaging via sparse representation over a learned dictionary

This paper proposes an adaptive reconstruction method for parallel imaging (PI) via sparse representation over a learned dictionary and also a corresponding dictionary learning based PI (DL-PI) algorithm. DL-PI adopts the “divide and conquer” strategy to solve the ℓ₂-DL reconstruction formulation, with dictionary learning to capture the structure information and a Taylor approximation to update the target image analytically. The proposed approach has been applied to parallel magnetic resonance imaging (MRI) and compared to two latest state-of-the-art methods. The experimental results on in-vivo data show that the DL-PI algorithm possesses strong ability in detail preservation and is competent in artifact removal during the MR image reconstruction process.