title: Component separation in NMR imaging and multidimensional spectroscopy through global least-squares analysis, based on prior knowledge.
abstract: Use of prior knowledge with regard to the number of components in an image or NMR data set makes possible a full analysis and separation of correlated sets of such data. It is demonstrated that a diffusional NMR microscopy image set can readily be separated into its components, with the extra benefit of a global least-squares fit over the whole image of the respective diffusional rates. As outlined, the computational approach (CORE processing) is also applicable to various multidimensional NMR data sets and is suggested as a potentially powerful tool in functional MRI.
authors.affiliation: Physical Chemistry, Royal Institute of Technology (KTH), Stockholm, SE-10044, Sweden. Peter@physchem.kth.se
authors.forename: P
authors.initials: P
authors.lastname: Stilbs
mesh_terms: D017666:Deuterium Oxide / Q000737:chemistry; D007091:Image Processing, Computer-Assisted; D016018:Least-Squares Analysis; D009682:Magnetic Resonance Spectroscopy; D011092:Polyethylene Glycols / Q000737:chemistry*; D013057:Spectrum Analysis; D014867:Water / Q000737:chemistry*
chemical_list: D014867:Water; D011092:Polyethylene Glycols; D017666:Deuterium Oxide