Parametric images generated from dynamic positron emission tomography (PET)studies are useful for presenting functional/biological information in the3-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.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging