A Novel Iterative Shrinkage Algorithm for CS-MRI via Adaptive Regularization

Abstract

A new algorithm is proposed for compressed sensingmagnetic resonance imaging (CS-MRI). The l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm (0 <; p ≤ 1) based adaptive regularization model is used for MRI. The algorithm is established by using a novel iterative shrinkage scheme. In the iteration, the quasi-Newton method is employed. In the shrinkage, the threshold is defined varyingly. Also, the parameter p is selected dynamically in the algorithm. Comparing with some certain state-of-the-art methods for the noisy case, the proposed algorithm provides a higher accuracy of the MR image reconstruction. The performance of the proposed algorithm is validated by the theoretical analysis as well as some experimental results.