Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging
| Autor: | Nabil Boughanmi, Khadidja Berradja |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Physics
Bayesian probability Input function 020206 networking & telecommunications Markov chain Monte Carlo 02 engineering and technology Pet imaging 030218 nuclear medicine & medical imaging 03 medical and health sciences symbols.namesake 0302 clinical medicine Small animal 0202 electrical engineering electronic engineering information engineering Econometrics Metabolic rate symbols Algorithm |
| Zdroj: | Polish Journal of Medical Physics and Engineering. 22:69-75 |
| ISSN: | 1898-0309 |
| DOI: | 10.1515/pjmpe-2016-0012 |
| Popis: | In dynamic cardiac PET FDG studies the assessment of myocardial metabolic rate of glucose (MMRG) requires the knowledge of the blood input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. The IF is contaminated by spillover of radioactivity from the adjacent myocardium and this could cause important error in the estimated MMRG. In this study, we show that the IF can be extracted from the images in a rat heart study with 18F-fluorodeoxyglucose (18F-FDG) by means of Independent Component Analysis (ICA) based on Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling method (BICA). Images of the heart from rats were acquired with the Sherbrooke small animal PET scanner. A region of interest (ROI) was drawn around the rat image and decomposed into blood and tissue using BICA. The Statistical study showed that there is a significant difference (p < 0.05) between MMRG obtained with IF extracted by BICA with respect to IF extracted from measured images corrupted with spillover. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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