Volumetric medical imaging data presents a special challenge in terms of storage and communication. Even the smallest sets of volumetric data are many times larger than most single medical images, and the onset of new applications that link data-sharing with video conferencing and multi-media make efficient and flexible coding of volumetric data an important task. Here the authors explore motion-like models for the coding of volumetric data. They first visit the affine interframe model, which was recently used to code MRI sequences effectively. Motivated by the encouraging results from the affine coder, the authors performed a comparative study of motion vs. 3-D spatial optimal autoregressive predictors. The results of this study indicate that motion analysis indeed leads to improved predictor performance in volumetric medical images, compared to the optimal 3-D predictor.