title: A software channel compression technique for faster reconstruction with many channels.
abstract: In magnetic resonance imaging, highly parallel imaging using coil arrays with a large number of elements is an area of growing interest. With increasing channel numbers for parallel acquisition, the increased reconstruction time and extensive computer memory requirements have become significant concerns. In this work, principal component analysis (PCA) is used to develop a channel compression technique. This technique efficiently reduces the size of parallel imaging data acquired from a multichannel coil array, thereby significantly reducing the reconstruction time and computer memory requirement without undermining the benefits of multichannel coil arrays. Clinical data collected with a 32-channel cardiac coil are used in all of the experiments. The performance of the proposed method on parallel, partially acquired data, as well as fully acquired data, was evaluated. Experimental results show that the proposed method dramatically reduces the processing time without considerable degradation in the quality of reconstructed images. It is also demonstrated that this PCA technique can be used to perform intensity correction in parallel imaging applications.
authors.affiliation: Advanced Concept Development, Invivo Corporation, Gainesville, FL 32603, USA. fhuang@invivocorp.com
authors.forename: Feng
authors.initials: F
authors.lastname: Huang
authors.forename: Sathya
authors.initials: S
authors.lastname: Vijayakumar
authors.forename: Yu
authors.initials: Y
authors.lastname: Li
authors.forename: Sarah
authors.initials: S
authors.lastname: Hertel
authors.forename: George R
authors.initials: GR
authors.lastname: Duensing
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006321:Heart / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000295:instrumentation*; D025341:Principal Component Analysis; D012984:Software