title: Improving multi-channel compressed sensing MRI with reweighted l 1 minimization.
abstract: Integrating compressed sensing (CS) and parallel imaging (PI) with multi-channel receiver has proven to be an effective technology to speed up magnetic resonance imaging (MRI). In this paper, we propose a method that extends the reweighted l 1 minimization to the CS-MRI with multi-channel data. The method applies a reweighted l 1 minimization algorithm to reconstruct each channel image, and then generates the final image by a sum-of-squares method. Computer simulations based on synthetic data and in vivo MRI imaging data show that the new method can improve the reconstruction quality at a slightly increased computation cost.
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, USA.
authors.forename: Ching-Hua
authors.initials: CH
authors.lastname: Chang
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, USA.
authors.forename: Jim X
authors.initials: JX
authors.lastname: Ji
keywords: Compressed sensing MRI; image reconstruction; multi-channel receive arrays; reweighted l1 minimization