Abstract
The quantitative analysis of tracers in positron emission tomography (PET) studies requires the measurement uptake and retention of tracer in tissue over time. This analysis applied to the heart allows to diagnose its state. It could provide a means to identify areas of myocardial viability and to assess myocardial ischemia. However, the input function (IF), quite commonly used in quantitative analysis, can be corrupted by undesirable effects such as spillover. In this paper, we propose a new approach to correct the cross contamination effect on PET dynamic image sequences. It 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 FDG-PET of healthy and unhealthy rats. The results show the benefit of our correction on the generation of pixel-wise images of myocardial metabolic rates for glucose (MMRG).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
