This work develops a theoretical framework and corresponding algorithms for the modeling of dynamic PET-SPECT studies both in time and space. The problem of estimating the spatial dimension is solved by applying the wavelet transform to each scan of the dynamic sequence and then performing the kinetic modeling and statistical analysis in the wavelet domain. On reconstruction through the inverse wavelet transform, one obtains parametric images that are consistent estimates of the spatial patterns of the kinetic parameter of interest. The theoretical setup allows the use of linear techniques currently used in PET-SPECT for kinetic analysis. The method is applied to artificial and real data sets. The application to dynamic PET-SPECT studies was performed both for validation purposes, when the spatial patterns are known, and for illustration of the advantages offered by the technique in case of tracers with an unknown pattern of distribution.