摘要：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.
