Cardiac PET imaging of 18F-FDG metabolism: study of healthy and infarcted hearts of rats.

This paper, considers the evolution of a method presented previously by authors to correct for cross contamination effect on the dynamic image sequences and shows how this development allows for a robust voxel by voxel implementation yielding parametric images for healthy and unhealthy subjects. Our approach is based on the decomposition of image pixel intensity into blood and tissue components using Bayesian statistics. The method uses an a priori knowledge of the probable distribution of blood and tissue in the images. Likelihood measures are computed by a General Gaussian Distribution (GGD) model. Bayes' rule is then applied to compute weights that account for the concentrations of the radiotracer in blood and tissue and their relative contributions in each image pixel. We tested the method on a set of dynamic cardiac (18)F-fluoro-deoxy-d-glucose PET of healthy rats and unhealthy rats. The results show the benefit of our correction on the generation of parametric images of myocardial metabolic rates for glucose (MMRG).

Extraction of time activity curves from gated FDG-PET images for small animals' heart studies.

We introduce a new approach to extract the input function and the tissue time activity curve from dynamic ECG-gated (18)F-FDG PET images. These curves are mandatory to model the myocardium metabolic rate of glucose for heart studies. The proposed method utilizes coupled active contours to track the myocardium and the blood pool deformations. Furthermore, a statistical approach is developed to model the blood and tissue activities and to correct for spillovers. The developed algorithm offers a reliable alternative to serial blood sampling for small animal cardiac PET studies. Indeed, the calculated MMRG value differs by 1.54% only from the reference value.