Application of Non-negative Matrix Factorization to Dynamic Positron Emission Tomography

Recently suggested non-negative matrix factorization (NMF) seems to overcome fundamental limitations of factor analysis at least in theoretical aspect. NMF cost function uses Poisson statistics as a noise model, rather than the Gaussian statistics, and provides a simple learning rule, in contrast to the tricky optimization in factor analysis. To study the feasibility of NMF for the analysis of dynamic image sequences in nuclear medicine, NMF was applied to H2 O dynamic myocardial PET images acquired from dog studies, and the results were compared with those obtained by conventional factor analysis method. Using NMF we could obtain basis images corresponding to major cardiac components. Their time-activity curves showed reasonable shapes that we have been familiar with. With the assumption of proper number of factors, NMF presented good results at least similar with those by factor analysis. Our results showed that NMF would be feasible for image segmentation and factor extraction from dynamic image sequences in nuclear medicine.