title: Nonnegative Canonical Polyadic Decomposition for Tissue-Type Differentiation in Gliomas.
abstract: Magnetic resonance spectroscopic imaging (MRSI) reveals chemical information that characterizes different tissue types in brain tumors. Blind source separation techniques are used to extract the tissue-specific profiles and their corresponding distribution from the MRSI data. We focus on automatic detection of the tumor, necrotic and normal brain tissue types by constructing a 3D MRSI tensor from in vivo 2D-MRSI data of individual glioma patients. Nonnegative canonical polyadic decomposition (NCPD) is applied to the MRSI tensor to differentiate various tissue types. An in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in glioma patients compared to previous matrix-based decompositions, such as nonnegative matrix factorization and hierarchical nonnegative matrix factorization.
authors.affiliation: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
authors.forename: H N
authors.initials: HN
authors.lastname: Bharath
authors.affiliation: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
authors.forename: D M
authors.initials: DM
authors.lastname: Sima
authors.affiliation: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
authors.forename: N
authors.initials: N
authors.lastname: Sauwen
authors.affiliation: Biomedical MRI Unit/Molecular Small Animal Imaging Center, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
authors.forename: U
authors.initials: U
authors.lastname: Himmelreich
authors.affiliation: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
authors.forename: L
authors.initials: L
authors.lastname: De Lathauwer
authors.affiliation: Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
authors.forename: S
authors.initials: S
authors.lastname: Van Huffel
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D005910:Glioma / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012815:Signal Processing, Computer-Assisted