title: Clustering-based linear least square fitting method for generation of parametric images in dynamic FDG PET studies.
abstract: Parametric images generated from dynamic positron emission tomography (PET) studies are useful for presenting functional/biological information in the 3-dimensional space, but usually suffer from their high sensitivity to image noise. To improve the quality of these images, we proposed in this study a modified linear least square (LLS) fitting method named cLLS that incorporates a clustering-based spatial constraint for generation of parametric images from dynamic PET data of high noise levels. In this method, the combination of K-means and hierarchical cluster analysis was used to classify dynamic PET data. Compared with conventional LLS, cLLS can achieve high statistical reliability in the generated parametric images without incurring a high computational burden. The effectiveness of the method was demonstrated both with computer simulation and with a human brain dynamic FDG PET study. The cLLS method is expected to be useful for generation of parametric images from dynamic FDG PET study.
authors.affiliation: The Beijing City Key Lab of Medical Physics and Engineering, Peking University, Beijing 100871, China.
authors.forename: Xinrui
authors.initials: X
authors.lastname: Huang
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhou
authors.forename: Shangliang
authors.initials: S
authors.lastname: Bao
authors.forename: Sung-Cheng
authors.initials: SC
authors.lastname: Huang