title: Improved compressed sensing MRI with multi-channel data using reweighted l(1) minimization.
abstract: Compressed sensing (CS) is an emerging technology to speed up magnetic resonance imaging (MRI). Since most clinical MRI scanners are equipped with multi-channel receiver systems, there has been a number of works to integrate CS with multi-channel systems. In this paper, we propose a method that extends the reweighted l(1) minimization to the CS MRI with multi-channel data. The simulated experimental results show that the new method can provide improved reconstruction quality.
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, TX 77843-3128, USA.
authors.forename: Ching-Hua
authors.initials: CH
authors.lastname: Chang
authors.forename: Jim
authors.initials: J
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity