title: Compressed sensing MRI with multichannel data using multicore processors.
abstract: Compressed sensing (CS) is a promising method to speed up MRI. Because most clinical MRI scanners are equipped with multichannel receive systems, integrating CS with multichannel systems may not only shorten the scan time but also provide improved image quality. However, significant computation time is required to perform CS reconstruction, whose complexity is scaled by the number of channels. In this article, we propose a reconstruction procedure that uses ubiquitously available multicore central processing unit to accelerate CS reconstruction from multiple channel data. The experimental results show that the reconstruction efficiency benefits significantly from parallelizing the CS reconstructions and pipelining multichannel data into multicore processors. In our experiments, an additional speedup factor of 1.6-2.0 was achieved using the proposed method on a quad-core central processing unit. The proposed method provides a straightforward way to accelerate CS reconstruction with multichannel data for parallel computation.
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, 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; D001921:Brain / Q000033:anatomy & histology*; D044962:Data Compression / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation* / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity