Assessment of brain glucose metabolism with input function determined from brain PET images by means of Bayesian ICA and MCMC methods
Positron emission tomography (PET) imaging has the capability to produce regional or parametric images of physiological aspects in a tissue of interest. Apart from the acquired PET data, the concentration of the radiotracer supplied to the tissue through the vascularization has to be known as the 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. In the present work, we determine IF from internal artery in fluorodeoxyglucose (18F-FDG) brain images by means of Independent Component Analysis (ICA) based on Bayesian theory and Monte Carlo Markov Chain (MCMC) sampling method (BICA, Bayesian ICA). Dynamic brain images were decomposed with BICA into image sequences of blood and tissue components. A region of interest was drawn around the internal artery in the blood image to determine BICA-IF. BICA-IF was therefore corrected for spillover of radioactivity emission from tissue, then it was normalized with three plasma samples to correct for partial volume and blood to plasma radioactivity ratio. BICA-IF was found comparable to IF determined by blood sampling, and rCMRG values in several brain structures obtained with BICA-IF and sampled IF showed a bias of 6.4% which was attributed to the difference in the time sampling of 4s for sampled IF versus 15s for BICA-MC-IF at early times. In conclusion, BICA is a strong approach in image decomposition to extract blood curves in a noninvasive way.
input function positron emission tomography independent component analysis (ica) mcmc sampling bayesian approach