Kinetic modeling of the dynamic PET brain data using blind source separation methods

Image-based definition of regions of interest is a typical prerequisite step for estimation of time-activity curves in dynamic positron emission tomography (PET). This procedure is done manually by a human operator and therefore suffers from subjective errors. Another such problem is to estimate the input function. It can be measured from arterial blood or it can be searched for a vascular structure on the images which is hard to be done, unreliable, and often impossible. In this study, we focus on blind source separation methods with no needs of manual interaction. Recently, we developed sparse blind source separation and deconvolution (S-BSS-vecDC) method for separation of original sources from dynamic medical data based on probability modeling and Variational Bayes approximation methodology. We apply the methods on dynamic brain PET data and application and comparison of our S-BSS-vecDC algorithm with those of similar assumptions are given. The S-BSS-vecDC algorithm is publicly available for download.