title: Channel reduction in massive array parallel MRI.
abstract: This paper presents a method to explore the flexibility of channel reduction in k-domain parallel imaging with massive arrays to improve the computation efficiency. MCMLI and GRAPPA are k-domain reconstruction methods that use a neighborhood of PE columns, FE line(s) and all channels in the interpolation kernels. For massive array which contains a large number of element coils computation cost can be a significant problem. In this paper, channel selection and reduction is performed according to the correlation between channel images for individual channel reconstructions. Simulation results show that the proposed channel reduction algorithm can achieve similar or improved reconstruction quality with significantly reduced computation for massive arrays with localized sensitivity.
authors: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.
authors: Shuo
authors: S
authors: Feng
authors: Jim
authors: J
authors: Ji
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D003201:Computers; D003627:Data Interpretation, Statistical; D004867:Equipment Design; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000295:instrumentation* / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software