Clustering huge data sets for parametric PET imaging.
A new preprocessing clustering technique for quantification of kinetic PET data is presented. A two-stage clustering process, which combines a precluster and a classic hierarchical cluster analysis, provides data which are clustered according to a distance measure between time activity curves (TACs). The resulting clustered mean TACs can be used directly for estimation of kinetic parameters at the cluster level, or to span a vector space that is used for subsequent estimation of voxel level kinetics. The introduction of preclustering significantly reduces the overall time for clustering of multiframe kinetic data. The efficiency and superiority of the preclustering scheme combined with thresholding is validated by comparison of the results for clustering both with and without preclustering for FDG-PET brain data of 13 healthy subjects.
Department of Mathematics and Statistics, Arizona State University, Tempe, AZ 85287-1804, USA. hb_guo@asu.edu
Hongbin
H
Guo
Rosemary
R
Renaut
Kewei
K
Chen
Eric
E
Reiman
D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D016208:Databases, Factual; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007700:Kinetics; D014055:Tomography, Emission-Computed / Q000706:statistics & numerical data*
D019788:Fluorodeoxyglucose F18