Efficient Dual ADMMs for Sparse Compressive Sensing MRI Reconstruction

TL;DR

This paper introduces efficient dual ADMM algorithms for sparse MRI reconstruction, leveraging a dual problem formulation and convergence-guaranteed techniques to improve accuracy and speed in compressive sensing MRI.

Contribution

It develops a symmetric Gauss-Seidel based ADMM and a generalized ADMM for dual sparse MRI reconstruction, ensuring convergence and enhanced efficiency over traditional methods.

Findings

Achieves high reconstruction accuracy on simulated and real MRI data.

Demonstrates faster convergence compared to standard ADMM methods.

Provides theoretical convergence guarantees for the proposed algorithms.

Abstract

Magnetic Resonance Imaging (MRI) is a kind of medical imaging technology used for diagnostic imaging of diseases, but its image quality may be suffered by the long acquisition time. The compressive sensing (CS) based strategy may decrease the reconstruction time greatly, but it needs efficient reconstruction algorithms to produce high-quality and reliable images. This paper focuses on the algorithmic improvement for the sparse reconstruction of CS-MRI, especially considering a non-smooth convex minimization problem which is composed of the sum of a total variation regularization term and a $\ell_1$-norm term of the wavelet transformation. The partly motivation of targeting the dual problem is that the dual variables are involved in relatively low-dimensional subspace. Instead of solving the primal model as usual, we turn our attention to its associated dual model composed of three variable blocks and two separable non-smooth function blocks. However, the directly extended alternating direction method of multipliers (ADMM) must be avoided because it may be divergent, although it usually performs well numerically. In order to solve the problem, we employ a symmetric Gauss-Seidel (sGS) technique based ADMM. Compared with the directly extended ADMM, this method only needs one additional iteration, but its convergence can be guaranteed theoretically. Besides, we also propose a generalized variant of ADMM because this method has been illustrated to be efficient for solving semidefinite programming in the past few years. Finally, we do extensive experiments on MRI reconstruction using some simulated and real MRI images under different sampling patterns and ratios. The numerical results demonstrate that the proposed algorithms significantly achieve high reconstruction accuracies with fast computational speed.