MRI reconstruction based on Bayesian group sparse representation

Compressed sensing (CS) theory speeds up the magnetic resonance imaging (MRI) by undersampling the k-space data. Utilizing a proper sparse representation for image and incorporating prior information are vital to yield a high-quality reconstruction in CS-MRI. In this work, the multivariate Gaussian scale mixture (GSM) model is developed to precisely characterize to the statistical properties of sparse coefficients of group formed by similar patches, and a Bayesian group sparse representation (BGSR) is derived from maximum a posterior (MAP) estimation. The efficient multiclass orthogonal dictionaries learning is further integrated in BGSR driven CS-MRI reconstruction to enable high sparsity as well as performance enhancement. The solution is obtained by a use of alternating direction method of multipliers (ADMM) iteration, and a closed-form solution for each subproblem is separately deduced. The experimental results show that the proposed method provides a superior visual quality and performance indexes over the regularization based CS-MRI methods. Hence, the proposed method can be utilized to further accelerate MRI and produce the highly accurate reconstruction for subsequent image processing as well as clinical diagnosis. Furthermore, this work can be extended to other imaging applications and provide some references for Bayesian probabilistic model based CS-MRI reconstruction. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

In this work, a CS-MRI reconstruction method based on the multivariate Gaussian scale mixture (GSM) model and Bayesian group sparse representation (BGSR) is proposed, with the solution obtained through ADMM iteration and high sparsity achieved through efficient multiclass orthogonal dictionaries learning. The experimental results demonstrate superior visual quality and performance indexes compared to regularization based CS-MRI methods.