title: Blind source separation for the computational analysis of dynamic oncological PET studies.
abstract: The analysis of dynamic positron emission tomography (PET) studies provides clinically useful parametric information, but often requires complex and time-consuming compartmental or non-compartmental techniques. Independent component analysis (ICA), a statistical method used for feature extraction and signal separation, is applied to dynamic PET studies to facilitate the initial interpretation and visual analysis of these large image sequences. ICA produces parametric images, where structures with different kinetic characteristics are assigned opposite values and readily discriminated, improving the identification of lesions and facilitating the posterior detailed kinetic analysis.
authors.affiliation: Biomedical Engineering Laboratory, National Technical University of Athens, Athens, Greece.
authors.forename: Trias
authors.initials: T
authors.lastname: Thireou
authors.forename: Sotiris
authors.initials: S
authors.lastname: Pavlopoulos
authors.forename: George
authors.initials: G
authors.lastname: Kontaxakis
authors.forename: Andres
authors.initials: A
authors.lastname: Santos
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D007700:Kinetics; D009369:Neoplasms / Q000000981:diagnostic imaging*; D049268:Positron-Emission Tomography / Q000706:statistics & numerical data*