Abstract
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).
Published Version
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