  We present a robust method to correct for motion in volumetric in-utero MRI
time series. Time-course analysis for in-utero volumetric MRI time series often
suffers from substantial and unpredictable fetal motion. Registration provides
voxel correspondences between images and is commonly employed for motion
correction. Current registration methods often fail when aligning images that
are substantially different from a template (reference image). To achieve
accurate and robust alignment, we make a Markov assumption on the nature of
motion and take advantage of the temporal smoothness in the image data. Forward
message passing in the corresponding hidden Markov model (HMM) yields an
estimation algorithm that only has to account for relatively small motion
between consecutive frames. We evaluate the utility of the temporal model in
the context of in-utero MRI time series alignment by examining the accuracy of
propagated segmentation label maps. Our results suggest that the proposed model
captures accurately the temporal dynamics of transformations in in-utero MRI
time series.
