Parametric imaging in dynamic glucose metabolism studies in brain

Parametric imaging is more and more popular in dynamic brain studies. It enables to quantitatively or semi-quantitatively estimate physiological state and processes in brain. This work analyse the dynamic 18FDG-PET studies for estimation of brain glucose metabolism. The influence of the signal noise is analysed to estimate its influence on the final glucose metabolism parameter values. The LCMRGlc parameter is under investigation. It is based on three compartmental model proposed by Phelps. Using different 18FDG-PET data series obtained from independent sources the Gaussian noise was introduced (with different variance). Then the quality of the model fitting results were estimated. The final results clearly indicates than the noise is highly compensated in microparameter used in calculation of LCMRGlc. Concluding, it is possible to estimate the LCMRGlc parameter value even in the presence of noise.