title: Clustering-initiated factor analysis application for tissue classification in dynamic brain positron emission tomography.
abstract: The goal is to quantify the fraction of tissues that exhibit specific tracer binding in dynamic brain positron emission tomography (PET). It is achieved using a new method of dynamic image processing: clustering-initiated factor analysis (CIFA). Standard processing of such data relies on region of interest analysis and approximate models of the tracer kinetics and of tissue properties, which can degrade accuracy and reproducibility of the analysis. Clustering-initiated factor analysis allows accurate determination of the time-activity curves and spatial distributions for tissues that exhibit significant radiotracer concentration at any stage of the emission scan, including the arterial input function. We used this approach in the analysis of PET images obtained using (11)C-Pittsburgh Compound B in which specific binding reflects the presence of β-amyloid. The fraction of the specific binding tissues determined using our approach correlated with that computed using the Logan graphical analysis. We believe that CIFA can be an accurate and convenient tool for measuring specific binding tissue concentration and for analyzing tracer kinetics from dynamic images for a variety of PET tracers. As an illustration, we show that four-factor CIFA allows extraction of two blood curves and the corresponding distributions of arterial and venous blood from PET images even with a coarse temporal resolution.
authors.affiliation: Lawrence Berkeley National Lab, Berkeley, California, USA.
authors.forename: Rostyslav
authors.initials: R
authors.lastname: Boutchko
authors.affiliation: Department of Computer Science, Florida Institute of Technology, Melbourne, Florida, USA.
authors.forename: Debasis
authors.initials: D
authors.lastname: Mitra
authors.affiliation: Lawrence Berkeley National Lab, Berkeley, California, USA.
authors.forename: Suzanne L
authors.initials: SL
authors.lastname: Baker
authors.affiliation: Lawrence Berkeley National Lab, Berkeley, California, USA.
authors.forename: William J
authors.initials: WJ
authors.lastname: Jagust
authors.affiliation: Lawrence Berkeley National Lab, Berkeley, California, USA.
authors.forename: Grant T
authors.initials: GT
authors.lastname: Gullberg
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D016229:Amyloid beta-Peptides / Q000032:analysis*; D000814:Aniline Compounds; D001921:Brain / Q000473:pathology*; D002250:Carbon Radioisotopes; D016000:Cluster Analysis; D005163:Factor Analysis, Statistical; D006801:Humans; D059906:Neuroimaging / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D013844:Thiazoles
chemical_list: C475519:2-(4'-(methylamino)phenyl)-6-hydroxybenzothiazole; D016229:Amyloid beta-Peptides; D000814:Aniline Compounds; D002250:Carbon Radioisotopes; D013844:Thiazoles