Modification of Ica for Extracting Blood Vessel-related Component in Nuclear Medicine: Contrast Function and Nonnegative Constraints

The problem of extracting a blood vessel-related component from dynamic brain PET images is similar to the ICA analysis of fMRI data. Unique characteristics of this problem are: (1) the spatial distribution of vessels can be acquired by PET, and therefore the property of the probability distribution of the vessel component is known; and (2) independent maps and the mixing matrix are all nonnegative. We have proposed a method for extracting the pTAC based on ICA (EPICA). EPICA is a method designed for extracting the vessel component. We investigate (A) the variation of the estimated pTAC with changing parameters of a contrast function of EPICA, and (B) the effect of the nonnegative constraints in ICA using the ensemble learning algorithm. Our results show that (A) a penalty term influences the tail of the estimated pTAC, and (B) a nonnegative assumption in ICA is feasible for extracting a vessel component.