Quantitative cardiac dynamic imaging of small animal PET images using cluster analysis

Quantitative PET imaging requires a dynamic scan in order to measure the arterial input function and the tissue time-activity curves. By combining these two curves with adequate mathematical models it is possible to obtain useful physiological information such as the metabolic rate, perfusion, receptors density etc. Cluster Analysis (CA) allows to group pixels having the same kinetic. In this work the performance of two clustering algorithms were assessed. The user must supply a set of images acquired at different time points and the number of clusters. The choice of the correct number of clusters was performed by using a parsimony criteria. In order to test the CA method real dynamic small animal PET data were acquired. Image derived arterial input function and myocardial FDG uptake were measured. Results showed that CA allow us to obtain accurate tissue time activity curves without the need of manual region on interest delineation.