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In this paper, we propose a non-convex regularization magnetic resonance imaging (MRI) reconstruction model via the smoothly clipped absolute deviation (SCAD) penalty function, which can effectively improve the fitting performance and prevent systematic underestimation compared with the classical total variation (TV) regularization. Then, we choose the alternating direction method of multipliers (ADMM) algorithm to solve the non-convex regularization model. The experiment results show that the efficiency of the proposed model and algorithm in comparison with some other typical methods. [ABSTRACT FROM AUTHOR]

The total variation (TV) regularization technique is a popular method for magnetic resonance imaging (MRI) reconstruction. In this paper, the generalized minimax concave (GMC) penalty function is used to construct a nonconvex regularized MRI model, which can effectively prevent the systematic underestimation characteristic of the standard TV regularization. In addition, the cost function can maintain convexity under certain conditions. To solve the new non-convex model, we describe a symmetric alternating direction method of multipliers (S-ADMM) algorithm, which is faster than the original ADMM. The experiment results show the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]