Wavelet analysis has become routinely used in neuroimaging, especially in PET image analysis. Techniques have been developed which combine spatial wavelet transforms and linear temporal models 1, 2. However, in PET, often the temporal model can be well described using a compartmental model 3. Compartmental models are non-linear models and as such some of the assumptions made when wavelet analysis is used are no longer justified. The advantage of wavelet analysis is that spatially correlated data can be analyzed easily and the resulting estimates will have better mean squared error properties. Here, we investigate the feasibility of combining PET compartmental models with wavelet spatial models, in order to integrate the advantages of both underlying techniques. We have investigated using the Simplified Reference Tissue Model (SRTM) 4 both in the image domain and in the wavelet domain, where the data had been transformed using Battle-Lemarie wavelets. A simulation based on several blocks in a small artificial image was considered, where each block had a time activity curve (TAC) associated with it. These TACs were generated from the underlying true model using parameter values based on real data. In addition a simulated PET image was generated using a PET simulator and analyzed. In the wavelet domain, in both studies, the data was analyzed firstly without any shrinkage being performed, and subsequently using three different shrinkage operators; hard shrinkage, soft shrinkage and linear shrinkage. The variances used in the shrinkage were estimated in two different ways, block by block using the parameter values themselves, and point-by-point using the time series data 5. The results of our simulation studies suggest that it is indeed possible to combine the two techniques of PET compartmental modeling and spatial wavelet analysis. The small block simulation showed that in the presence of spatially correlated noise, the wavelet analysis results have better power signal to noise ratio (pSNR) properties than the corresponding image domain results. While there was a small improvement in the overall image, a greater improvement was seen in the regions containing true signal (as opposed to the background). It was also found that linear shrinkage with point-by-point variance estimation gave the greatest improvement in pSNR. As can be seen in the figure 1, the result of the wavelet shrinkage analysis of the PET simulator data is much smoother than the image domain result, and does not contain spurious pixels of large intensity. This work demonstrates, through simulation, that the integration of PET compartmental models and wavelet analysis techniques is feasible, certainly for the SRTM. It would be of interest to examine other compartmental models, and of course, apply this method to real data sets.